From 2ecd5f4ab07045cd7a747b98e0a28501b6460451 Mon Sep 17 00:00:00 2001
From: richard lyman <richard.r.lyman@gmail.com>
Date: Fri, 17 Feb 2017 14:36:57 -0800
Subject: [PATCH 1/6] misc commits

---
 15-01-01 459_Mont_Lyman.jpg  | Bin 0 -> 1149201 bytes
 README.md                    |   2 +-
 o4_image_to_image.py         | 110 +++++++++++
 ocr_utils.py                 | 233 ++++++++++++++++++++--
 q2_tensorflow_mnist.py       | 306 +----------------------------
 q5_tensorflow_residual.py    | 364 +++-------------------------------
 q6_tensorflow_residual3x3.py | 371 -----------------------------------
 q8_tika.py                   |  46 ++++-
 q9_tensorflow_gpu_test.py    |   4 +-
 run_batch.sc                 |  20 +-
 10 files changed, 420 insertions(+), 1036 deletions(-)
 create mode 100644 15-01-01 459_Mont_Lyman.jpg
 create mode 100644 o4_image_to_image.py
 delete mode 100644 q6_tensorflow_residual3x3.py

diff --git a/15-01-01 459_Mont_Lyman.jpg b/15-01-01 459_Mont_Lyman.jpg
new file mode 100644
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diff --git a/README.md b/README.md
index 7c714a1..b92b17c 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@ be used with E13B.
 
 run_batch.sc is a bash script that runs all of the programs in the directory
 
-The script file will create many files in the folder, ./plots.
+The script file will create many files in the folder, /tmp/plots.
 
 
 Python 3.4
diff --git a/o4_image_to_image.py b/o4_image_to_image.py
new file mode 100644
index 0000000..e438f7d
--- /dev/null
+++ b/o4_image_to_image.py
@@ -0,0 +1,110 @@
+"""# ==========================================================================
+
+# Copyright 2015 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+This sample program is a modified version of the Google mnist convolutional 
+network tutorial example.  See the mnist tutorial in www.tensorflow.org 
+
+This graph has multiple sections  3 layers each, 400 100 400 followed
+by a fully connected layer.
+
+see tensor_flow_graph.png
+"""# ==============================================================================
+import ocr_utils
+import datetime
+from collections import namedtuple
+import numpy as np
+import pandas as pd
+import n1_image_to_image as nnetwork  
+#import n1_residual3x4 as nnetwork  
+import tensorflow as tf  
+dtype = np.float32
+#with tf.device('/GPU:0'):
+#with tf.device('/cpu:0'): 
+       
+    
+if True:
+    # single font train
+    
+    # examples
+    # select only images from 'OCRB'  scanned font
+    # input_filters_dict = {'font': ('OCRA',)}
+    
+    # select only images from 'HANDPRINT'  font
+    #input_filters_dict = {'font': ('HANDPRINT',)}
+    
+    # select only images from 'OCRA' and 'OCRB' fonts with the 'scanned" fontVariant
+    # input_filters_dict = {'font': ('OCRA','OCRB'), 'fontVariant':('scanned',)}
+    
+    # select everything; all fonts , font variants, etc.
+    # input_filters_dict = {}
+    
+    # select the digits 0 through 9 in the E13B font
+    # input_filters_dict = {'m_label': range(48,58), 'font': 'E13B'}
+    
+    # select the digits 0 and 2in the E13B font
+    # input_filters_dict = {'m_label': (48,50), 'font': 'E13B'}
+    
+    # output the character label, image, italic flag, aspect_ratio and upper_case flag
+    # output_feature_list = ['m_label_one_hot','image','italic','aspect_ratio','upper_case']    
+    
+    # output only the character label and the image
+    # output_feature_list = ['m_label_one_hot','image'] 
+    
+    #   identify the font given the input images
+    #output_feature_list = ['font_one_hot','image','italic','aspect_ratio','upper_case']   
+
+    # train the digits 0-9 for all fonts
+    input_filters_dict = {'m_label': range(48,58),'italic':0,'strength':.4}
+    #input_filters_dict = {'font':'BANKGOTHIC','m_label': list(range(48,58)),'italic':0,'strength':.7}    
+    #input_filters_dict = {}    
+    output_feature_list = ['low_pass_image','image']    
+ 
+    """# ==============================================================================
+    
+    Train and Evaluate the Model
+    
+    """# ==============================================================================
+    ds = ocr_utils.read_data(input_filters_dict = input_filters_dict, 
+                                output_feature_list=output_feature_list,
+                                test_size = .2,
+                                engine_type='tensorflow',dtype=dtype)    
+    nn = nnetwork.network(ds.train)
+    nn.fit( ds.train,  nEpochs=5000)  
+    nn.test2(ds.test)
+      
+#     train_a_font(input_filters_dict,  output_feature_list, nEpochs = 50000)    
+    
+else:
+    # loop through all the fonts and train individually
+
+    # pick up the entire list of fonts and font variants. Train each one.
+    df1 = ocr_utils.get_list(input_filters_dict={'font': ()})      
+    
+    import pprint as pprint
+    pp = pprint.PrettyPrinter(indent=4)
+    pp.pprint(df1)
+   
+    output_feature_list = ['m_label_one_hot','image','italic','aspect_ratio','upper_case','font_one_hot']
+    
+    # Change nEpochs to 5000 for better results
+    for l in df1:
+        input_filters_dict= {'font': (l[0],)}       
+        train_a_font(input_filters_dict,output_feature_list, nEpochs = 5000) 
+    
+    
+print ('\n########################### No Errors ####################################')
+
diff --git a/ocr_utils.py b/ocr_utils.py
index a3c0238..2791558 100644
--- a/ocr_utils.py
+++ b/ocr_utils.py
@@ -25,8 +25,11 @@
 
 ######################################################
 show_plot = False   #set True to show plot on screen, set False to save to file
+plot_dir = '/tmp/plots'
 #####################################################
 
+extension = '.jpg'
+
 ##############################################################################
 default_zip_file = "fonts.zip"   #small data set
 #default_zip_file = 'fonts_all.zip' #for the big data set
@@ -40,6 +43,9 @@
 import matplotlib.pyplot as plt
 import sys
 import os
+import subprocess
+from PIL import Image
+import io
 
 
 def report(blocknr, blocksize, size):
@@ -165,8 +171,12 @@ def __init__(self,  features, output_feature_list, one_hot_map, engine_type,h,w,
         self._epochs_completed = 0
         self._index_in_epoch = 0
         self._feature_names = output_feature_list   # list of names of features
+        
+        for i,nm in enumerate(self._feature_names):
+            if nm in self._feature_names[i+1:]:
+                self._feature_names[i+1] = nm + "_"+ str(i)
         self._num_features = len(features)
-        self._one_hot_map = one_hot_map             # list >0 for each feature that is one_hot
+        self._one_hot_map = one_hot_map             # list 0 for non one-hots, or # of one_hots 
         self._engine_type= engine_type        
         self._dtype = dtype
         
@@ -252,7 +262,7 @@ def next_batch(self, batch_size):
             A list of npArrays, one for each feature requested
             
         """        
-    
+        batch_size = min(batch_size, self._num_examples)
         start = self._index_in_epoch
         self._index_in_epoch += batch_size
         if self._index_in_epoch > self._num_examples:
@@ -267,7 +277,7 @@ def next_batch(self, batch_size):
             # Start next epoch
             start = 0
             self._index_in_epoch = batch_size
-            assert batch_size <= self._num_examples
+
         end = self._index_in_epoch
         outs = []
         for i in range(self._num_features):
@@ -313,6 +323,7 @@ def apply_column_filters(df, input_filters_dict ):
         filtered datafram
     '''
     for key,value in input_filters_dict.items():
+    
         if isinstance(value, str): 
             value = (value,) 
         if hasattr(value, '__iter__')==False:
@@ -320,6 +331,10 @@ def apply_column_filters(df, input_filters_dict ):
         if len(value) > 0:
             criterion = df[key].map(lambda x: x in value)
             df = df[criterion]  
+#     try:
+#         print (df['font'].iloc[0])
+#     except:
+#         print (df)
     return df
 
 def convert_to_unique(t1):
@@ -338,7 +353,8 @@ def convert_to_unique(t1):
     for i,u in enumerate(unique):
         t2[t1==u]=i
     return t2
-            
+
+
 def read_data(fileName=default_zip_file, 
               input_filters_dict={}, 
               output_feature_list=[], 
@@ -347,6 +363,7 @@ def read_data(fileName=default_zip_file,
               dtype=np.float32, 
               engine_type='', 
               random_state=None  ):
+
     """
     Reads data from a given .zip file holding .csv files, 
     filters the data to extract the requested features and 
@@ -448,10 +465,7 @@ def read_data(fileName=default_zip_file,
          respective column in the .csv file
     
     """
-    class DataSets(object):
-        pass
-    
-    data_sets = DataSets()
+
 
     '''
     1) read in the fonts applying the input filter to extract only the fonts, 
@@ -465,13 +479,35 @@ class DataSets(object):
     
     5) construct training and test set TruthedCharacters classes and return them    
     '''
-    engine_type = engine_type.lower()
 
+    df = read_file(fileName, input_filters_dict,random_state=random_state)
+ 
+    return read_df(df, 
+        input_filters_dict=input_filters_dict, 
+        output_feature_list=output_feature_list, 
+        test_size=test_size, 
+        evaluation_size=evaluation_size, 
+        dtype=dtype, 
+        engine_type=engine_type, 
+        random_state=random_state  )
+                    
+def read_df(df, 
+              input_filters_dict={}, 
+              output_feature_list=[], 
+              test_size=0.0, 
+              evaluation_size=0.0, 
+              dtype=np.float32, 
+              engine_type='', 
+              random_state=None  ):
+    
+    class DataSets(object):
+        pass
+    
     print('\nparameter: input_filters_dict\n\t{}'.format(sorted(input_filters_dict.items())))
     print('parameter: output_feature_list\n\t{}'.format(output_feature_list))    
-
-    df = read_file(fileName, input_filters_dict,random_state=random_state)
     
+    engine_type = engine_type.lower()
+    data_sets = DataSets()
     available_columns = []
     for key in df.columns:
         if key=='r0c0':  #omit the image
@@ -519,7 +555,38 @@ class DataSets(object):
         elif colName=='image':  
             t1 = np.array(df.loc[:,'r0c0':],dtype=dtype) #extract the images with is everything to the right of row 0 column 0
             t1 = np.multiply(t1, 1.0 / 256.0)     
-            feature_name.append(colName)                                  
+            feature_name.append(colName)  
+            
+        elif colName=='low_pass_image':  
+            ''' Create unique images for each of the labels, using a single font
+            These are the concepts.
+            Make an np array of the original images with the concept images replacing
+            the originals.
+            '''
+            
+            boolDF1 = df['fontVariant'] == 'BANKGOTHIC MD BT'
+            
+            criterion = df['fontVariant'].map(lambda x: x in 'BANKGOTHIC MD BT')
+            x = df[criterion] 
+            dx = pd.DataFrame(df)
+            labels = np.array(x['m_label'])
+            
+            for i,label in enumerate(labels):  
+                to_be_replaced =  df['m_label']!=label     
+                dx =dx.where(to_be_replaced, other= x.iloc[i], axis=1)        
+
+            
+            t1 = np.array(dx.loc[:,'r0c0':],dtype=dtype) #extract the images with is everything to the right of row 0 column 0
+            t1 = np.reshape(t1, (t1.shape[0],h,w))
+            t2 = np.zeros((t1.shape[0],h,int(w/2)),dtype=dtype)            
+            for col in range(w):
+                t2[:,:,int(col/2)] = np.sum(t1[:,:,col:col+2],axis=2)
+            t3 = np.zeros((t1.shape[0], int(h/2),int(w/2)),dtype=dtype)
+            for row in range(h):
+                t3[:,int(row/2),:] = np.sum(t2[:,row:row+2,:],axis=1)    
+            t1 = np.reshape(t3,(t3.shape[0],int(h*w/4)) )        
+            t1=(t1/4)/256.0                  
+            feature_name.append(colName)                                        
             
         elif colName=='m_label_one_hot': 
             t1  = np.array(df['m_label'])
@@ -533,6 +600,12 @@ class DataSets(object):
             one_hot_map[-1] = len(np.unique(t1))   
             feature_name.append(colName) 
             
+        elif colName=='orientation_one_hot': 
+            t1 =  np.array(df['orientation'])
+            t1 = convert_to_unique(t1)    
+            one_hot_map[-1] = len(np.unique(t1))   
+            feature_name.append(colName)             
+            
         elif colName=='fontVariant_one_hot':  
             t1 = np.array(df['fontVariant'] ) 
             t1 = convert_to_unique(t1)
@@ -721,10 +794,135 @@ def compute_column_sum(npx,h,w):
     see column sum notes under read_data
     '''
     npx = np.reshape(npx,(npx.shape[0],h,w))
-    return np.sum(npx,axis=1) # sum of rows in each column    
+    return np.sum(npx,axis=1) # sum of rows in each column  
+  
+import skimage.transform as af
+  
+def shear(X, skew):
+    ''' given a 2D image, shear and return a 2D image
+    
+    parameters:
+        X is the 2D image of shape (nRows, nColumns)
+        skew is the amount to shear in the range 0 to 1.0
+    '''
+    
+    rows = X.shape[0]
+    cols = X.shape[1]    
+    ratioY = skew*cols/rows
+    matrix =  np.array( [[1, ratioY, 0] ,[0, 1, 0] ,[0, 0, 1 ]])                                         
+    tp=af.ProjectiveTransform(matrix=matrix) 
+    #tp  = tf.AffineTransform(scale=(.3,.3), shear=skew)    
+    f = af.warp(X, tp)      
+    return f
+# 
+# class file_names(object):
+#     ''' store variants of file a file name with .jpg, .png, .box variations
+#     '''
+#     def __init__(selp, base_name, dir_name = ''):
+#         base = base_name
+#         jpeg = base_name + '.jpg'
+#         png = base_name + '.png'
+#         box = base_name + '.box'
+        
+def file_to_df(base_file, character_size, title = "", white_space = 0, input_filters_dict={}):
+    ''' 
+    Given a 2D image file with some characters, uses Tesseract to cut out
+    the characters, , plots the characters and the boxes that Tesseract
+    found, then places the characters into a pandas dataframe
+    and also returns the images in 2D format
+    parameters:
+        image_file, string  is the path name of the file
+        character size, integer,  is the number of pixels of the return images
+            in pixels, height and width are equal
+        title, string is the name to be in the character/box plotted file
+        white_space is the amount of padding to be placed around the characters
+    '''
+    input_image_file_jpg = base_file + extension
+    print('input_image_file_jpg = {}'.format(input_image_file_jpg ))
+    subprocess.run(['tesseract', input_image_file_jpg, base_file,  'batch.nochop', 'makebox'])
+    #subprocess.run(['tesseract', input_image_file_jpg])    
+    im = Image.open(input_image_file_jpg)
+    
+    images =[]
+    recognized_label = []
+    tops=[]
+    originalH=[]
+    lefts=[]
+    originalW=[]
+    orientation = 0
+
+    f = open(base_file+'.box','r')
+
+    for line in f: 
+        coords = line.split(' ')        
+        top = im.height-int(coords[4])
+        bottom = im.height-int(coords[2])
+        left = int(coords[1])
+        right = int(coords[3])
+        tops.append(top)
+        originalW.append(right-left)
+        lefts.append(left)
+        originalH.append(bottom-top)
+        
+        img2 = Image.new('L',(character_size,character_size),color=255)
+        img =  im.crop(box=(left, top, right, bottom))       
+        img = img.convert('LA')     
+        img2.paste(img, box=(white_space,white_space))    
+                    
+        imgByteArr = img2.tobytes()
+        lst = list(imgByteArr)
+        image = np.array(lst)/255.0 
+        image = 1.0 - image        
+        images.append(image)
+        try:
+            recognized_label.append(ord(coords[0]))
+        except:
+            print (coords[0])
+            recognized_label.append(ord('_'))
+ 
+    df = make_df(images, character_size, character_size, originalH, originalW, tops, lefts, orientation, recognized_label )
+    df= apply_column_filters(df, input_filters_dict )
+    t1 = np.array(df.loc[:,'r0c0':]) #extract the images with is everything to the right of row 0 column 0 
+    t1 = np.reshape(t1,(t1.shape[0],character_size,character_size )) 
+    montage(t1, title=title)
+    return df,t1
+        
+def make_df(images, character_w, character_h, originalH, originalW, tops, lefts, orientation, recognized_label ):
+    ''' Given an numpy array of images and attributes of each image, place these in a pandas dataframe
+    '''
+    x = np.zeros((len(images), character_w*character_h+8))
+    x[:,0] = recognized_label
+    x[:,1] = orientation   
+    x[:,2] = tops
+    x[:,3] = lefts  
+    x[:,4] = originalH
+    x[:,5] = originalW   
+    x[:,6] = character_h  
+    x[:,7] = character_w
+#     print (' appending images')
+    x[:,8:] = images                 
+#     print (' DONE appending images')            
+    
+#     images = np.array(images)   
+#     images = np.insert(images, 0, character_w, axis=1)
+#     images = np.insert(images, 0, character_h, axis=1)
+#     images = np.insert(images, 0, originalW, axis=1)    
+#     images = np.insert(images, 0, originalH, axis=1)
+#     images = np.insert(images, 0, lefts, axis=1)      
+#     images = np.insert(images, 0, tops, axis=1)
+#     images = np.insert(images, 0, orientation, axis=1) 
+#     images = np.insert(images, 0, recognized_label, axis=1)
+        
+    columns = ['m_label', 'orientation','m_top','m_left','originalH','originalW', 'h','w']
+    for i in range(character_h):
+        for j in range(character_w):
+            columns.append('r{}c{}'.format(i,j))
 
+    df = pd.DataFrame(x, columns=columns)          
 
-#################  Miscellaneous Plot Routines ##############################
+    return  df
+                
+###############################################  Miscellaneous Plot Routines ############################################################
 
 num_fig = 0 # used to give each saved plot a unique name
 def program_name():
@@ -739,7 +937,7 @@ def show_figures(plt, title="untitled"):
         
         If show_plot is true, the the plot is shown on the screen.
         If show_plot is false, the plot will be saved to a file in the
-        ./plots folder
+        /plots folder
         
         The files are given unique names based on the plot title
     args
@@ -759,7 +957,7 @@ def show_figures(plt, title="untitled"):
     if show_plot:
         plt.show()   
     else:    
-        plot_dir = './plots'
+
         try:
             os.mkdir(plot_dir)
         except:
@@ -773,6 +971,7 @@ def show_figures(plt, title="untitled"):
         plt.clf() # savefig does not clear the figure like show does
         plt.cla()
     num_fig += 1
+#     print ('END PLOT')
 
 def scatter_plot(X=None, y=None, legend_entries=[],axis_labels=("",""), title="",xlim=None, ylim=None):
     ''' 
@@ -892,7 +1091,7 @@ def plot_decision_regions(X=None, y=None, classifier=None, resolution = .005, te
     show_figures(plt, title)   
     
 
-def montage(X, maxChars = 256, title=''):  
+def montage(X, maxChars = 2500, title=''):  
     ''' 
     montage displays a square matrix of characters
     
diff --git a/q2_tensorflow_mnist.py b/q2_tensorflow_mnist.py
index 20a6b8d..fa9ff46 100644
--- a/q2_tensorflow_mnist.py
+++ b/q2_tensorflow_mnist.py
@@ -26,307 +26,15 @@
 
 see tensor_flow_graph.png
 """# ==============================================================================
+
 import ocr_utils
 import datetime
 from collections import namedtuple
 import numpy as np
 import pandas as pd
- 
+import n1_2cnv1fc as nnetwork      
 import tensorflow as tf  
-dtype=np.float32
-#with tf.device('/gpu:0'):
-#with tf.device('/cpu:0'):    
-def train_a_font(input_filters_dict,output_feature_list, nEpochs=5000):
- 
-    ds = ocr_utils.read_data(input_filters_dict = input_filters_dict, 
-                                output_feature_list=output_feature_list,
-                                test_size = .1,
-                                engine_type='tensorflow',
-                                dtype=dtype)
-
-        
-    """# ==============================================================================
-    
-    Start TensorFlow Interactive Session
-    
-    """# ==============================================================================
-    
-
-    sess = tf.InteractiveSession()
-    
-    """# ==============================================================================
-    
-    Placeholders
-    
-    Compute the size of various layers 
-    
-    Create a tensorflow Placeholder for each feature of data returned from the
-    dataset
-    
-    """# ==============================================================================
-
-    
-
-    lst = []
-    extra_features_width = 0 # width of extra features
-    
-    for i,nm in enumerate(output_feature_list):
-        
-        # features[0], is always the target. For instance it may be m_label_one_hot 
-        # the second features[1] is the 'image' that is passed to the convolution layers 
-        # Any additional features bypass the convolution layers and go directly 
-        # into the fully connected layer.  
-        
-        # The width of the extra features is calculated in order to allocate 
-        # the correct widths of weights,  # and inputs 
-        # names are assigned to make the look pretty on the tensorboard graph.
-        
-        if i == 0:
-            nm = 'y_'+nm
-        else:
-            nm = 'x_'+nm
-        if i>1:
-            extra_features_width += ds.train.feature_width[i]
-        lst.append(tf.placeholder(dtype, shape=[None, ds.train.feature_width[i]], name=nm))
-        
-    # ph is a named tuple with key names like 'image', 'm_label', and values that
-    # are tensors.  The display name on the Chrome graph are 'y_m_label', 'x_image, 
-    # x_upper_case etc.
-    Place_Holders = namedtuple('Place_Holders', output_feature_list)   
-    ph = Place_Holders(*lst) # unpack placeholders into named Tuple
-        
-    nRows = ds.train.num_rows #image height
-    nCols = ds.train.num_columns #image width    
-    nFc = 1024      # size of fully connected layer
-    nConv1 = 32     # size of first convolution layer
-    nConv2 = 64     # size of second convolution layer
-    nTarget = ds.train.feature_width[0]  # the number of one_hot features in the target, 'm_label'    
-    n_h_pool2_outputs = int(nRows/4) * int(nCols/4) * nConv2 # second pooling layer 
-    n_h_pool2_outputsx = n_h_pool2_outputs + extra_features_width # fully connected
-        
-    """# ==============================================================================
-    
-    Build a Multilayer Convolutional Network
-    
-    Weight Initialization
-    
-    """# ==============================================================================
-    
-    def weight_variable(shape, dtype):
-        initial = tf.truncated_normal(shape, stddev=0.1,dtype=dtype)
-        return tf.Variable(initial)
-    
-    def bias_variable(shape, dtype):
-        initial = tf.constant(0.1, shape=shape, dtype=dtype)
-        return tf.Variable(initial)   
-    
-    """# ==============================================================================
-    
-    Convolution and Pooling
-    
-    keep our code cleaner, let's also abstract those operations into functions.
-    
-    """# ==============================================================================
-    
-    def conv2d(x, W):
-        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
-    
-    def max_pool_2x2(x):
-        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
-                            strides=[1, 2, 2, 1], padding='SAME')
-    
-    """# ==============================================================================
-    
-    First Convolutional Layer
-    
-    """# ==============================================================================
-    with tf.name_scope("w_conv1") as scope:
-        W_conv1 = weight_variable([5, 5, 1, nConv1],dtype)
-        b_conv1 = bias_variable([nConv1],dtype)    
-    
-    with tf.name_scope("reshape_x_image") as scope:
-        x_image = tf.reshape(ph.image, [-1,nCols,nRows,1])
-    
-    image_summ = tf.image_summary("x_image", x_image)
-    
-    """# ==============================================================================
-    
-    We then convolve x_image with the weight tensor, add the bias, apply the ReLU function,
-     and finally max pool.
-    
-    """# ==============================================================================
-    
-    with tf.name_scope("convolve_1") as scope:
-        h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
-        
-    with tf.name_scope("pool_1") as scope:    
-        h_pool1 = max_pool_2x2(h_conv1)
-    
-    """# ==============================================================================
-    
-    Second Convolutional Layer
-    
-    In order to build a deep network, we stack several layers of this type. The second 
-    layer will have 64 features for each 5x5 patch.
-    
-    """# ==============================================================================
-    
-    with tf.name_scope("convolve_2") as scope:
-        W_conv2 = weight_variable([5, 5, nConv1, nConv2],dtype)
-        b_conv2 = bias_variable([64],dtype)
-        h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)  
-         
-    with tf.name_scope("pool_2") as scope:
-        h_pool2 = max_pool_2x2(h_conv2)
-    
-    """# ==============================================================================
-    
-    Densely Connected Layer
-    
-    Now that the image size has been reduced to 7x7, we add a fully-connected layer 
-    with neurons to allow processing on the entire image. We reshape the tensor 
-    from the pooling layer into a batch of vectors, multiply by a weight matrix, add 
-    a bias, and apply a ReLU.
-    
-    """# ==============================================================================
-    
-    with tf.name_scope("W_fc1_b") as scope:
-        W_fc1 = weight_variable([n_h_pool2_outputsx, nFc],dtype)
-        b_fc1 = bias_variable([nFc],dtype)
-            
-        h_pool2_flat = tf.reshape(h_pool2, [-1, n_h_pool2_outputs])
-        
-        # append the features, the 2nd on, that go directly to the fully connected layer
-        for i in range(2,ds.train.num_features ):
-            h_pool2_flat = tf.concat(1, [h_pool2_flat, ph[i]])  
-        h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
-    
-    """# ==============================================================================
-    
-    Dropout
-    
-    """# ==============================================================================
-    keep_prob = tf.placeholder(dtype,name='keep_prob')
-    
-    with tf.name_scope("drop") as scope:
-        h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
-    
-    """# ==============================================================================
-    
-    Readout Layer
-    
-    """# ==============================================================================
-    with tf.name_scope("softmax") as scope:
-        W_fc2 = weight_variable([nFc, nTarget],dtype)
-        b_fc2 = bias_variable([nTarget],dtype)    
-        y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
-    
-    """# ==============================================================================
-    
-    Train and Evaluate the Model
-    
-    """# ==============================================================================
-    
-    with tf.name_scope("xent") as scope:
-        # 1e-8 added to eliminate the crash of training when taking log of 0
-        cross_entropy = -tf.reduce_sum(ph[0]*tf.log(y_conv+1e-8))
-        ce_summ = tf.scalar_summary("cross entropy", cross_entropy)
-            
-    with tf.name_scope("train") as scope:
-        train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
-        
-    with tf.name_scope("test") as scope:        
-        correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(ph[0],1))
-    
-        accuracy = tf.reduce_mean(tf.cast(correct_prediction, dtype))
-        accuracy_summary = tf.scalar_summary("accuracy", accuracy)    
-    
-    merged = tf.merge_all_summaries()
-    tm = ""
-    tp = datetime.datetime.now().timetuple()
-    for i in range(4):
-        tm += str(tp[i])+'-'
-    tm += str(tp[4])    
-    writer = tf.train.SummaryWriter("/tmp/ds_logs/"+ tm, sess.graph)
-    
-    # To see the results in Chrome, 
-    # Run the following in terminal to activate server.
-    # tensorboard --logdir '/tmp/ds_logs/'
-    # See results on localhost:6006 
-    
-    sess.run(tf.initialize_all_variables())
-    
-    perfect_count=10
-    for i in range(nEpochs):
-    
-        batch = ds.train.next_batch(100)
-        # assign feature data to each placeholder
-        # the batch list is returned in the same order as the features requested
-        feed = {keep_prob: 0.5}
-        for j in range(ds.train.num_features):
-            feed[ph[j]] = batch[j]  
-            
-        if i%100 == 0:
-            # sh=h_pool2_flat.get_shape()
-            feed[keep_prob] = 1.0
-            result = sess.run([merged, accuracy ], feed_dict=feed)    
-            summary_str = result[0]
-            #acc = result[1]       
-            writer.add_summary(summary_str, i)
-            train_accuracy = accuracy.eval(feed)    
-            if train_accuracy != 1:
-                perfect_count=10;
-            else:
-                perfect_count -= 1
-                if perfect_count==0:
-                    break;  
-                
-            print ("step %d, training accuracy %g"%(i, train_accuracy),flush=True)
-        train_step.run(feed_dict=feed)
-    
-    def computeSize(s,tens):
-        sumC = 1
-        tShape = tens.get_shape()
-        nDims = len(tShape)
-        for i in range(nDims):
-            sumC *= tShape[i].value
-        print ('\t{}\t{}'.format(s,sumC),flush=True)
-        return sumC
-            
-    print ('network size:',flush=True)
-    total = computeSize("W_fc1",W_fc1)+ \
-    computeSize ("b_fc1",b_fc1) + \
-    computeSize ("W_conv1",W_conv1) + \
-    computeSize ("b_conv1",b_conv1) + \
-    computeSize ("W_conv2",W_conv2) + \
-    computeSize ("b_conv2",b_conv2) + \
-    computeSize ("W_fc2",W_fc2) + \
-    computeSize ("b_fc2",b_fc2)
-    print('\ttotal\t{}'.format(total),flush=True)
-    
-    feed={keep_prob: 1.0}
-    # assign feature data to each placeholder
-    error_images = np.empty((0,nRows,nCols))
-        
-    test_accuracy=0
-    m=0
-    for n in range(0,ds.test.features[0].shape[0],100 ):   
-        for i in range(ds.train.num_features ):  
-            feed[ph[i]] = ds.test.features[i] [n:n+100]
-        result = sess.run([accuracy, x_image, W_conv1, correct_prediction], feed_dict=feed)    
-        test_accuracy += result[0]
-        error_images = np.append(error_images, result[1][:,:,:,0][result[3]==False],axis=0)
-        m += 1
-    try:        
-        print ("test accuracy {} for font: {}".format(test_accuracy/m, input_filters_dict['font']),flush=True)       
-        ocr_utils.montage(error_images,title='TensorFlow {} Error Images'.format(input_filters_dict['font']))  
-    except:                            
-        print ("test accuracy {}".format(test_accuracy/m),flush=True)       
-        ocr_utils.montage(error_images,title='TensorFlow Error Images')    
-    
-    tf.reset_default_graph() # only necessary when iterating through fonts
-    sess.close()
-
+dtype = np.float32
     
 if True:
     # single font train
@@ -362,7 +70,13 @@ def computeSize(s,tens):
     # train the digits 0-9 for all fonts
     input_filters_dict = {'m_label': range(48,58)}
     output_feature_list = ['m_label_one_hot','image','italic','aspect_ratio','upper_case']    
-    train_a_font(input_filters_dict,  output_feature_list, nEpochs = 5000)    
+    ds = ocr_utils.read_data(input_filters_dict = input_filters_dict, 
+                                output_feature_list=output_feature_list,
+                                test_size = .1,
+                                engine_type='tensorflow',dtype=dtype)    
+    nn = nnetwork.network(ds.train)
+    nn.fit( ds.train,  nEpochs=5000)  
+    nn.test(ds.test)
     
 else:
     # loop through all the fonts and train individually
diff --git a/q5_tensorflow_residual.py b/q5_tensorflow_residual.py
index 3044b46..c9e0815 100644
--- a/q5_tensorflow_residual.py
+++ b/q5_tensorflow_residual.py
@@ -33,346 +33,19 @@
 from collections import namedtuple
 import numpy as np
 import pandas as pd
+import n1_2cnv1fc as nnetwork 
+
+# import tensorflow as tf  
  
-import tensorflow as tf  
 dtype = np.float32
 #with tf.device('/GPU:0'):
 #with tf.device('/cpu:0'):    
-def train_a_font(input_filters_dict,output_feature_list, nEpochs=5000):
- 
-    ds = ocr_utils.read_data(input_filters_dict = input_filters_dict, 
-                                output_feature_list=output_feature_list,
-                                test_size = .1,
-                                engine_type='tensorflow',
-                                dtype=dtype)
 
-        
-    """# ==============================================================================
-    
-    Start TensorFlow Interactive Session
-    
-    """# ==============================================================================
-
-    sess = tf.InteractiveSession()
-    
-    """# ==============================================================================
-    
-    Placeholders
-    
-    Compute the size of various layers 
-    
-    Create a tensorflow Placeholder for each feature of data returned from the
-    dataset
-    
-    """# ==============================================================================
-
-    
-
-    lst = []
-    extra_features_width = 0 # width of extra features
-    
-    for i,nm in enumerate(output_feature_list):
-        
-        # features[0], is always the target. For instance it may be m_label_one_hot 
-        # the second features[1] is the 'image' that is passed to the convolution layers 
-        # Any additional features bypass the convolution layers and go directly 
-        # into the fully connected layer.  
-        
-        # The width of the extra features is calculated in order to allocate 
-        # the correct widths of weights,  # and inputs 
-        # names are assigned to make the look pretty on the tensorboard graph.
-        
-        if i == 0:
-            nm = 'y_'+nm
-        else:
-            nm = 'x_'+nm
-        if i>1:
-            extra_features_width += ds.train.feature_width[i]
-        lst.append(tf.placeholder(dtype, shape=[None, ds.train.feature_width[i]], name=nm))
-        
-    # ph is a named tuple with key names like 'image', 'm_label', and values that
-    # are tensors.  The display name on the Chrome graph are 'y_m_label', 'x_image, 
-    # x_upper_case etc.
-    Place_Holders = namedtuple('Place_Holders', output_feature_list)   
-    ph = Place_Holders(*lst) # unpack placeholders into named Tuple
-        
-    nRows = ds.train.num_rows #image height
-    nCols = ds.train.num_columns #image width    
-    nFc0 = 2048      # size of fully connected layer
-    nFc1 = 2048      # size of fully connected layer        
-    nFc2 = 2048      # size of fully connected layer    
-    nConv1 = 32     # size of first convolution layer
-    nConv2 = 64     # size of second convolution layer
-    nTarget = ds.train.feature_width[0]  # the number of one_hot features in the target, 'm_label'    
-    n_h_pool2_outputs = int(nRows/4) * int(nCols/4) * nConv2 # second pooling layer 
-    n_h_pool2_outputsx = n_h_pool2_outputs + extra_features_width # fully connected
-        
-    """# ==============================================================================
-    
-    Build a Multilayer Convolutional Network
-    
-    Weight Initialization
-    
-    """# ==============================================================================
-    
-    def weight_variable(shape, dtype):
-        initial = tf.truncated_normal(shape, stddev=0.1,dtype=dtype)
-        return tf.Variable(initial)
-    
-    def bias_variable(shape, dtype):
-        initial = tf.constant(0.1, shape=shape, dtype=dtype)
-        return tf.Variable(initial)   
-    
-    """# ==============================================================================
-    
-    Convolution and Pooling
-    
-    keep our code cleaner, let's also abstract those operations into functions.
-    
-    """# ==============================================================================
-    
-    def conv2d(x, W):
-        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
-    
-    def max_pool_2x2(x):
-        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
-                            strides=[1, 2, 2, 1], padding='SAME')
-    
-    """# ==============================================================================
-    
-    First Convolutional Layer
-    
-    """# ==============================================================================
-    with tf.name_scope("w_conv1") as scope:
-        W_conv1 = weight_variable([5, 5, 1, nConv1],dtype)
-        b_conv1 = bias_variable([nConv1],dtype)    
-    
-    with tf.name_scope("reshape_x_image") as scope:
-        x_image = tf.reshape(ph.image, [-1,nCols,nRows,1])
-    
-    image_summ = tf.image_summary("x_image", x_image)
-    
-    """# ==============================================================================
-    
-    We then convolve x_image with the weight tensor, add the bias, apply the ReLU function,
-     and finally max pool.
-    
-    """# ==============================================================================
-    
-    with tf.name_scope("convolve_1") as scope:
-        h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
-        
-    with tf.name_scope("pool_1") as scope:    
-        h_pool1 = max_pool_2x2(h_conv1)
-    
-    """# ==============================================================================
-    
-    Second Convolutional Layer
-    
-    In order to build a deep network, we stack several layers of this type. The second 
-    layer will have 64 features for each 5x5 patch.
-    
-    """# ==============================================================================
-    
-    with tf.name_scope("convolve_2") as scope:
-        W_conv2 = weight_variable([5, 5, nConv1, nConv2],dtype)
-        b_conv2 = bias_variable([64],dtype)
-        h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)  
-         
-    with tf.name_scope("pool_2") as scope:
-        h_pool2 = max_pool_2x2(h_conv2)
     
-    """# ==============================================================================
-    
-    Densely Connected Layer 0
-    
-    Now that the image size has been reduced to 7x7, we add a fully-connected layer 
-    with neurons to allow processing on the entire image. We reshape the tensor 
-    from the pooling layer into a batch of vectors, multiply by a weight matrix, add 
-    a bias, and apply a ReLU.
-    
-    """# ==============================================================================
-    
-    with tf.name_scope("W_fc0_b") as scope:
-        W_fc0 = weight_variable([n_h_pool2_outputsx, nFc0],dtype)
-        b_fc0 = bias_variable([nFc0],dtype)
-            
-        h_pool2_flat = tf.reshape(h_pool2, [-1, n_h_pool2_outputs])
-        
-        # append the features, the 2nd on, that go directly to the fully connected layer
-        for i in range(2,ds.train.num_features ):
-            h_pool2_flat = tf.concat(1, [h_pool2_flat, ph[i]])  
-        h_fc0 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc0) + b_fc0)
-    """# ==============================================================================
-    
-    Densely Connected Layer 1
-    
-    We add a fully-connected layer 
-    with neurons to allow processing on the entire image. We reshape the tensor 
-    from the pooling layer into a batch of vectors, multiply by a weight matrix, add 
-    a bias, and apply a ReLU.
-    
-    """# ==============================================================================  
-          
-    with tf.name_scope("W_fc1_b") as scope:
-        W_fc1 = weight_variable([nFc0, nFc1],dtype)
-        b_fc1 = bias_variable([nFc1],dtype)
-        
-        h_fc1 = tf.nn.relu(tf.matmul(h_fc0, W_fc1) + b_fc1)    
-    
-    """# ==============================================================================
-    
-    Densely Connected Layer 2
-    
-    We add a fully-connected layer 
-    with neurons to allow processing on the entire image. We reshape the tensor 
-    from the pooling layer into a batch of vectors, multiply by a weight matrix, add 
-    a bias, and apply a ReLU.
-    
-    """# ==============================================================================
-    
-    with tf.name_scope("W_fc2_b") as scope:
-        W_fc2 = weight_variable([nFc1, nFc2],dtype)
-        b_fc2 = bias_variable([nFc2],dtype)
-        
-        h_fc2 = tf.nn.relu(tf.matmul(h_fc1, W_fc2) + b_fc2)
-        
-    """# ==============================================================================        
-    Dropout
-    
-    """# ==============================================================================
-    keep_prob = tf.placeholder(dtype,name='keep_prob')
-    
-    with tf.name_scope("drop") as scope:
-        h_fc2_drop = tf.nn.dropout(h_fc2, keep_prob)
-    
-    """# ==============================================================================
-    
-    Readout Layer
-    
-    """# ==============================================================================
-    with tf.name_scope("softmax") as scope:
-        W_fc3 = weight_variable([nFc2, nTarget],dtype)
-        b_fc3 = bias_variable([nTarget],dtype)    
-        y_conv=tf.nn.softmax(tf.matmul(h_fc2_drop, W_fc3) + b_fc3)
-    
-    """# ==============================================================================
-    
-    Train and Evaluate the Model
-    
-    """# ==============================================================================
-    
-    with tf.name_scope("xent") as scope:
-        # 1e-8 added to eliminate the crash of training when taking log of 0
-        cross_entropy = -tf.reduce_sum(ph[0]*tf.log(y_conv+1e-8))
-        ce_summ = tf.scalar_summary("cross entropy", cross_entropy)
-            
-    with tf.name_scope("train") as scope:
-        train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
-        
-    with tf.name_scope("test") as scope:        
-        correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(ph[0],1))
-    
-        accuracy = tf.reduce_mean(tf.cast(correct_prediction,dtype))
-        accuracy_summary = tf.scalar_summary("accuracy", accuracy)    
-    
-    merged = tf.merge_all_summaries()
-    tm = ""
-    tp = datetime.datetime.now().timetuple()
-    for i in range(4):
-        tm += str(tp[i])+'-'
-    tm += str(tp[4])    
-    writer = tf.train.SummaryWriter("/tmp/ds_logs/"+ tm, sess.graph)
-    
-    # To see the results in Chrome, 
-    # Run the following in terminal to activate server.
-    # tensorboard --logdir '/tmp/ds_logs/'
-    # See results on localhost:6006 
-    
-    sess.run(tf.initialize_all_variables())
-    
-    perfect_count=10
-    for i in range(nEpochs):
-    
-        batch = ds.train.next_batch(100)
-        # assign feature data to each placeholder
-        # the batch list is returned in the same order as the features requested
-        feed = {keep_prob: 0.5}
-        for j in range(ds.train.num_features):
-            feed[ph[j]] = batch[j]  
-            
-        if i%100 == 0:
-            # sh=h_pool2_flat.get_shape()
-            feed[keep_prob] = 1.0
-            result = sess.run([merged, accuracy ], feed_dict=feed)    
-            summary_str = result[0]
-            #acc = result[1]       
-            writer.add_summary(summary_str, i)
-            train_accuracy = accuracy.eval(feed)    
-            if train_accuracy != 1:
-                perfect_count=10;
-            else:
-                perfect_count -= 1
-                if perfect_count==0:
-                    break;  
-                
-            print ("step %d, training accuracy %g"%(i, train_accuracy),flush=True)
-        train_step.run(feed_dict=feed)
-    
-    def computeSize(s,tens):
-        sumC = 1
-        tShape = tens.get_shape()
-        nDims = len(tShape)
-        for i in range(nDims):
-            sumC *= tShape[i].value
-        print ('\t{}\t{}'.format(s,sumC),flush=True)
-        return sumC
-            
-    print ('network size:',flush=True)
-    total = computeSize("W_fc0",W_fc0)+ \
-    computeSize ("b_fc0",b_fc0) + \
-    computeSize ("W_conv1",W_conv1) + \
-    computeSize ("b_conv1",b_conv1) + \
-    computeSize ("W_conv2",W_conv2) + \
-    computeSize ("b_conv2",b_conv2) + \
-    computeSize ("W_fc0",W_fc0) + \
-    computeSize ("b_fc0",b_fc0) + \
-    computeSize ("W_fc1",W_fc1) + \
-    computeSize ("b_fc1",b_fc1) + \
-    computeSize ("W_fc2",W_fc2) + \
-    computeSize ("b_fc2",b_fc2)   
-
-    
-    print('\ttotal\t{}'.format(total),flush=True)
-    
-    feed={keep_prob: 1.0}
-    # assign feature data to each placeholder
-    error_images = np.empty((0,nRows,nCols))
-        
-    test_accuracy=0
-    m=0
-    for n in range(0,ds.test.features[0].shape[0],100 ):   
-        for i in range(ds.train.num_features ):  
-            feed[ph[i]] = ds.test.features[i] [n:n+100]
-        result = sess.run([accuracy, x_image, W_conv1, correct_prediction], feed_dict=feed)    
-        test_accuracy += result[0]
-        error_images = np.append(error_images, result[1][:,:,:,0][result[3]==False],axis=0)
-        m += 1
-    try:        
-        print ("test accuracy {} for font: {}".format(test_accuracy/m, input_filters_dict['font']),flush=True)       
-        ocr_utils.montage(error_images,title='TensorFlow {} Error Images'.format(input_filters_dict['font']))  
-    except:                            
-        print ("test accuracy {}".format(test_accuracy/m),flush=True)       
-        ocr_utils.montage(error_images,title='TensorFlow Error Images')    
-    
-    tf.reset_default_graph() # only necessary when iterating through fonts
-    sess.close()
-
-    
-if True:
+if False:
     # single font train
     
-    # esamples
+    # examples
     # select only images from 'OCRB'  scanned font
     # input_filters_dict = {'font': ('OCRA',)}
     
@@ -404,7 +77,13 @@ def computeSize(s,tens):
     input_filters_dict = {'m_label': list(range(48,58))+list(range(65,91))+list(range(97,123)),'fontVariant':'scanned'}
     #input_filters_dict = {}    
     output_feature_list = ['m_label_one_hot','image','italic','aspect_ratio','upper_case']    
-    train_a_font(input_filters_dict,  output_feature_list, nEpochs = 20000)    
+    ds = ocr_utils.read_data(input_filters_dict = input_filters_dict, 
+                                output_feature_list=output_feature_list,
+                                test_size = .1,
+                                engine_type='tensorflow',dtype=dtype)    
+    nn = nnetwork.network( ds.train)
+    nn.fit( ds.train,  nEpochs=5000)  
+    nn.test(ds.test)
     
 else:
     # loop through all the fonts and train individually
@@ -420,9 +99,22 @@ def computeSize(s,tens):
     
     # Change nEpochs to 5000 for better results
     for l in df1:
-        input_filters_dict= {'font': (l[0],)}       
-        train_a_font(input_filters_dict,output_feature_list, nEpochs = 500) 
-    
+        #input_filters_dict= {'font': (l[0],)}   
+        input_filters_dict = {'m_label': list(range(48,58))+list(range(65,91))+list(range(97,123)),'font': (l[0],)}            
+        #train_a_font(input_filters_dict,output_feature_list, nEpochs = 500) 
+        
+        ds = ocr_utils.read_data(input_filters_dict = input_filters_dict, 
+                                    output_feature_list=output_feature_list,
+                                    test_size = .1,
+                                    engine_type='tensorflow',dtype=dtype)    
+        
+        nn = nnetwork.network(ds.train)
+        nn.fit( ds.train,  nEpochs=5000)  
+        nn.test(ds.test, title = l[0] )
+        nn.reset_graph()
+
+
+        
     
 print ('\n########################### No Errors ####################################')
 
diff --git a/q6_tensorflow_residual3x3.py b/q6_tensorflow_residual3x3.py
deleted file mode 100644
index 4a9ea2d..0000000
--- a/q6_tensorflow_residual3x3.py
+++ /dev/null
@@ -1,371 +0,0 @@
-"""# ==========================================================================
-
-# Copyright 2015 Google Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-This sample program is a modified version of the Google mnist convolutional 
-network tutorial example.  See the mnist tutorial in www.tensorflow.org 
-
-This graph has multiple sections  3 layers each, 400 100 400 followed
-by a fully connected layer.
-
-see tensor_flow_graph.png
-"""# ==============================================================================
-import ocr_utils
-import datetime
-from collections import namedtuple
-import numpy as np
-import pandas as pd
- 
-import tensorflow as tf  
-dtype = np.float32
-#with tf.device('/GPU:0'):
-#with tf.device('/cpu:0'): 
-       
-def train_a_font(input_filters_dict,output_feature_list, nEpochs=5000):
- 
-    ds = ocr_utils.read_data(input_filters_dict = input_filters_dict, 
-                                output_feature_list=output_feature_list,
-                                test_size = .1,
-                                engine_type='tensorflow',dtype=dtype)
-
-        
-    """# ==============================================================================
-    
-    Start TensorFlow Interactive Session
-    
-    """# ==============================================================================
-
-    sess = tf.InteractiveSession()
-    
-    """# ==============================================================================
-    
-    Placeholders
-    
-    Compute the size of various layers 
-    
-    Create a tensorflow Placeholder for each feature of data returned from the
-    dataset
-    
-    """# ==============================================================================
-
-    
-
-    lst = []
-    extra_features_width = 0 # width of extra features
-    
-    for i,nm in enumerate(output_feature_list):
-        
-        # features[0], is always the target. For instance it may be m_label_one_hot 
-        # the second features[1] is the 'image' that is passed to the convolution layers 
-        # Any additional features bypass the convolution layers and go directly 
-        # into the fully connected layer.  
-        
-        # The width of the extra features is calculated in order to allocate 
-        # the correct widths of weights,  # and inputs 
-        # names are assigned to make the look pretty on the tensorboard graph.
-        
-        if i == 0:
-            nm = 'y_'+nm
-        else:
-            nm = 'x_'+nm
-        if i>1:
-            extra_features_width += ds.train.feature_width[i]
-        print (ds.train.features[i].dtype)
-        lst.append(tf.placeholder(dtype, shape=[None, ds.train.feature_width[i]], name=nm))
-        
-    # ph is a named tuple with key names like 'image', 'm_label', and values that
-    # are tensors.  The display name on the Chrome graph are 'y_m_label', 'x_image, 
-    # x_upper_case etc.
-    Place_Holders = namedtuple('Place_Holders', ds.train.feature_names)   
-    ph = Place_Holders(*lst) # unpack placeholders into named Tuple
-        
-    nRows = ds.train.num_rows #image height
-    nCols = ds.train.num_columns #image width    
-    
-    nSections = 10
-    w = list(range(nSections*3))
-    b = list(range(nSections*3))
-    h = list(range(nSections*3+1))
-
-    
-    in_out_width = nRows*nCols
-    internal_width = int(in_out_width/4)
-
-    
-#     nFc0 = 2048      # size of fully connected layer
-    nFc1 = 2048      # size of fully connected layer        
-#     nFc2 = 2048      # size of fully connected layer    
-#     nConv1 = 32     # size of first convolution layer
-#     nConv2 = 64     # size of second convolution layer
-    nTarget = ds.train.feature_width[0]  # the number of one_hot features in the target, 'm_label'    
-    
-#     n_h_pool2_outputs = int(nRows/4) * int(nCols/4) * nConv2 # second pooling layer 
-#     n_h_pool2_outputsx = n_h_pool2_outputs + extra_features_width # fully connected
-#         
-    """# ==============================================================================
-    
-    Build a Multilayer Convolutional Network
-    
-    Weight Initialization
-    
-    """# ==============================================================================
-       
-    def weight_variable(shape, dtype):
-        initial = tf.truncated_normal(shape, stddev=0.1,dtype=dtype)
-        return tf.Variable(initial)
-    
-    def bias_variable(shape, dtype):
-        initial = tf.constant(0.1, shape=shape, dtype=dtype)
-        return tf.Variable(initial)    
-
-        
-    """# ==============================================================================
-    Convolution and Pooling
-    
-    keep our code cleaner, let's also abstract those operations into functions.
-    
-    """# ==============================================================================
-    
-    def conv2d(x, W):
-        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
-
-    
-    """# ==============================================================================
-    
-    First Convolutional Layers
-    
-    """# ==============================================================================
-    
-    def shapeOuts(n):
-        print ('n={}, hin={},w={}, b={} ,hout={}\n'.format(n, h[n]._shape, w[n]._variable._shape, b[n]._variable._shape, h[n+1]._shape))
-        
-    def section(n):
-        with tf.name_scope('section_'+str(n)+'_0') as scope:     
-            w[n]=weight_variable([in_out_width, internal_width],dtype)
-            b[n]=bias_variable([internal_width],dtype)  
-            h[n+1] = tf.nn.relu(tf.matmul(h[n], w[n]) + b[n])
-            shapeOuts(n)
-            
-        with tf.name_scope('section_'+str(n)+'_1') as scope:  
-            w[n+1]=weight_variable([internal_width, internal_width],dtype)
-            b[n+1]=bias_variable([internal_width],dtype)     
-                          
-            h[n+2]=tf.nn.relu(tf.matmul(h[n+1], w[n+1]) + b[n+1])
-            shapeOuts(n+1)                  
-                            
-        with tf.name_scope('section_'+str(n)+'_2') as scope:  
-            w[n+2]=weight_variable([internal_width, in_out_width],dtype)
-            b[n+2]=bias_variable([in_out_width],dtype)   
-            z= tf.nn.relu(tf.matmul(h[n+2], w[n+2]) + b[n+2])
-            h[n+3]= tf.add(z   ,h[n]) #n+3   
-                     
-            print('z shape ={}'.format(z._shape)) 
-            shapeOuts(n+2)                  
-        return    
-              
-    def computeSize(s,tens):
-        sumC = 1
-        tShape = tens.get_shape()
-        nDims = len(tShape)
-        for i in range(nDims):
-            sumC *= tShape[i].value
-        print ('\t{}\t{}'.format(s,sumC),flush=True)
-        return sumC
-                    
-    """# ==============================================================================        
-    Build sectional network
-    
-    """# ==============================================================================      
-    h[0]= ph[1]
-    for i in range(nSections):
-        section(3*i)
-            
-    """# ==============================================================================        
-    Dropout
-    
-    """# ==============================================================================
-    keep_prob = tf.placeholder(dtype,name='keep_prob')
-    
-    with tf.name_scope("drop") as scope:
-        h_fc2_drop = tf.nn.dropout(h[nSections*3], keep_prob)
-    
-    """# ==============================================================================
-    
-    Readout Layer
-    
-    """# ==============================================================================
-    with tf.name_scope("softmax") as scope:
-        w_fc3 = weight_variable([in_out_width, nTarget],dtype)
-        b_fc3 = bias_variable([nTarget],dtype)    
-        y_conv=tf.nn.softmax(tf.matmul(h_fc2_drop, w_fc3) + b_fc3)
-    
-    print ('network size:',flush=True)
-    total = 0
-    for i in range(nSections*3):
-        total = total + computeSize("w{}".format(i),w[i])
-    total = total + computeSize ("b_fc3",b_fc3) + \
-        computeSize ("w_fc3",w_fc3) 
-
-    
-    print('\ttotal\t{}'.format(total),flush=True)
-        
-    """# ==============================================================================
-    
-    Train and Evaluate the Model
-    
-    """# ==============================================================================
-    with tf.name_scope("reshape_x_image") as scope:
-        x_image = tf.reshape(ph.image, [-1,nCols,nRows,1])
-        
-    with tf.name_scope("xent") as scope:
-        # 1e-8 added to eliminate the crash of training when taking log of 0
-        cross_entropy = -tf.reduce_sum(ph[0]*tf.log(y_conv+1e-8))
-        ce_summ = tf.scalar_summary("cross entropy", cross_entropy)
-            
-    with tf.name_scope("train") as scope:
-        train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
-        
-    with tf.name_scope("test") as scope:        
-        correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(ph[0],1))
-    
-        accuracy = tf.reduce_mean(tf.cast(correct_prediction, dtype))
-        accuracy_summary = tf.scalar_summary("accuracy", accuracy)    
-    
-    merged = tf.merge_all_summaries()
-    tm = ""
-    tp = datetime.datetime.now().timetuple()
-    for i in range(4):
-        tm += str(tp[i])+'-'
-    tm += str(tp[4])    
-    writer = tf.train.SummaryWriter("/tmp/ds_logs/"+ tm, sess.graph)
-    
-    # To see the results in Chrome, 
-    # Run the following in terminal to activate server.
-    # tensorboard --logdir '/tmp/ds_logs/'
-    # See results on localhost:6006 
-    
-    sess.run(tf.initialize_all_variables())
-    
-    perfect_count=10
-    for i in range(nEpochs):
-    
-        batch = ds.train.next_batch(100)
-        # assign feature data to each placeholder
-        # the batch list is returned in the same order as the features requested
-        feed = {keep_prob: 0.5}
-        for j in range(ds.train.num_features):
-            feed[ph[j]] = batch[j]  
-            
-        if i%100 == 0:
-            # sh=h_pool2_flat.get_shape()
-            feed[keep_prob] = 1.0
-            result = sess.run([merged, accuracy ], feed_dict=feed)    
-            summary_str = result[0]
-            #acc = result[1]       
-            writer.add_summary(summary_str, i)
-            train_accuracy = accuracy.eval(feed)    
-            if train_accuracy != 1:
-                perfect_count=10;
-            else:
-                perfect_count -= 1
-                if perfect_count==0:
-                    break;  
-                
-            print ("step %d, training accuracy %g"%(i, train_accuracy),flush=True)
-        train_step.run(feed_dict=feed)
-        
-    feed={keep_prob: 1.0}
-    # assign feature data to each placeholder
-    error_images = np.empty((0,nRows,nCols))
-        
-    test_accuracy=0
-    m=0
-    for n in range(0,ds.test.features[0].shape[0],100 ):   
-        for i in range(ds.train.num_features ):  
-            feed[ph[i]] = ds.test.features[i] [n:n+100]
-        result = sess.run([accuracy, x_image, correct_prediction], feed_dict=feed)    
-        test_accuracy += result[0]
-        error_images = np.append(error_images, result[1][:,:,:,0][result[2]==False],axis=0)
-        m += 1
-    try:        
-        print ("test accuracy {} for font: {}".format(test_accuracy/m, input_filters_dict['font']),flush=True)       
-        ocr_utils.montage(error_images,title='TensorFlow {} Error Images'.format(input_filters_dict['font']))  
-    except:                            
-        print ("test accuracy {}".format(test_accuracy/m),flush=True)       
-        ocr_utils.montage(error_images,title='TensorFlow Error Images')    
-    
-    tf.reset_default_graph() # only necessary when iterating through fonts
-    sess.close()
-
-    
-if True:
-    # single font train
-    
-    # esamples
-    # select only images from 'OCRB'  scanned font
-    # input_filters_dict = {'font': ('OCRA',)}
-    
-    # select only images from 'HANDPRINT'  font
-    #input_filters_dict = {'font': ('HANDPRINT',)}
-    
-    # select only images from 'OCRA' and 'OCRB' fonts with the 'scanned" fontVariant
-    # input_filters_dict = {'font': ('OCRA','OCRB'), 'fontVariant':('scanned',)}
-    
-    # select everything; all fonts , font variants, etc.
-    # input_filters_dict = {}
-    
-    # select the digits 0 through 9 in the E13B font
-    # input_filters_dict = {'m_label': range(48,58), 'font': 'E13B'}
-    
-    # select the digits 0 and 2in the E13B font
-    # input_filters_dict = {'m_label': (48,50), 'font': 'E13B'}
-    
-    # output the character label, image, italic flag, aspect_ratio and upper_case flag
-    # output_feature_list = ['m_label_one_hot','image','italic','aspect_ratio','upper_case']    
-    
-    # output only the character label and the image
-    # output_feature_list = ['m_label_one_hot','image'] 
-    
-    #   identify the font given the input images
-    #output_feature_list = ['font_one_hot','image','italic','aspect_ratio','upper_case']   
-
-    # train the digits 0-9 for all fonts
-    #input_filters_dict = {'m_label': range(48,58)}
-    input_filters_dict = {'font':'ARIAL','m_label': list(range(48,58))+list(range(65,91))+list(range(97,123))}    
-    #input_filters_dict = {}    
-    output_feature_list = ['m_label_one_hot','image']    
-    train_a_font(input_filters_dict,  output_feature_list, nEpochs = 50000)    
-    
-else:
-    # loop through all the fonts and train individually
-
-    # pick up the entire list of fonts and font variants. Train each one.
-    df1 = ocr_utils.get_list(input_filters_dict={'font': ()})      
-    
-    import pprint as pprint
-    pp = pprint.PrettyPrinter(indent=4)
-    pp.pprint(df1)
-   
-    output_feature_list = ['m_label_one_hot','image','italic','aspect_ratio','upper_case','font_one_hot']
-    
-    # Change nEpochs to 5000 for better results
-    for l in df1:
-        input_filters_dict= {'font': (l[0],)}       
-        train_a_font(input_filters_dict,output_feature_list, nEpochs = 500) 
-    
-    
-print ('\n########################### No Errors ####################################')
-
diff --git a/q8_tika.py b/q8_tika.py
index bae89cf..b1c7f93 100644
--- a/q8_tika.py
+++ b/q8_tika.py
@@ -4,11 +4,49 @@
 
 @author: richard lyman
 '''
+
+import pytesseract 
 import tika
+from tika import  translate, detector, language
+
+
+ 
+filename = '15-01-01 459_Mont_Lyman.jpg'
+filename2 = 'img20150901_15233271bw.jpg'
+
+from PIL import Image
+
+rawText = pytesseract.image_to_string(Image.open(filename2), lang="rus")
+print (rawText)
+lines = rawText.split('\n')
+
+import os
+#os.putenv( 'TIKA_VERSION','default')  # - set to the version string, e.g., 1.12 or default to current Tika version.
+#os.putenv( 'TIKA_SERVER_JAR','/home/richard/.m2/repository/org/apache/tika/tika-server/1.13/tika-server-1.13.jar') #- set to the full URL to the remote Tika server jar to download and cache.
+os.putenv( 'TIKA_SERVER_ENDPOINT',' http://localhost:9998') #- set to the host (local or remote) for the running Tika server jar.
+#os.putenv( 'TIKA_SERVER_ENDPOINT',' http://localhost:9998/language/string') #- set to the host (local or remote) for the running Tika server jar.
+#os.putenv( 'TIKA_CLIENT_ONLY','True') #- if set to True, then TIKA_SERVER_JAR is ignored, and relies on the value for TIKA_SERVER_ENDPOINT and treats Tika like a REST client.
+#os.putenv( 'TIKA_TRANSLATOR','org/apache/tika/language/translate/') #- set to the fully qualified class name (defaults to Lingo24) for the Tika translator implementation.
+#os.putenv( 'TIKA_SERVER_CLASSPATH','/home/richard/.m2/repository/org/apache/tika/tika-server/1.13/tika-server-1.13.jar') #- set to a string (delimited by ':' for each additional path) to prepend to the Tika server jar path.
 tika.initVM()
 from tika import parser
-parsed = parser.from_file('15-01-01 459_Mont_Lyman.jpg') 
-#parsed = parser.from_file('img20150901_15233271bw.jpg')
-print (parsed["metadata"])
-print (parsed["content"])
+parsed = parser.from_buffer("comme çi comme ça")
+print(parsed["metadata"])
+print(parsed["content"])
+global Verbose
+Verbose=True
+
+result=translate.auto_from_buffer("comme çi comme ça", 'en')
+print(result)
+result = detector.from_buffer("comme çi comme ça")
+print (result)
+result = translate.from_buffer("comme çi comme ça",'fr','en')
+print (result)
+result = language.from_buffer("comme çi comme ça")
+print (result)
+for line in lines:
+    if len(line)>0:
+        result=translate.from_buffer(line, 'ru','en')
+        print(result)
+
 print ('\n########################### No Errors ####################################')
\ No newline at end of file
diff --git a/q9_tensorflow_gpu_test.py b/q9_tensorflow_gpu_test.py
index dbb7ad1..c2fbd2f 100644
--- a/q9_tensorflow_gpu_test.py
+++ b/q9_tensorflow_gpu_test.py
@@ -6,4 +6,6 @@
 # Creates a session with log_device_placement set to True.
 sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
 # Runs the op.
-print (sess.run(c))
\ No newline at end of file
+print (sess.run(c))
+
+print ('\n########################### No Errors ####################################')
\ No newline at end of file
diff --git a/run_batch.sc b/run_batch.sc
index 6ff5449..045a5ae 100755
--- a/run_batch.sc
+++ b/run_batch.sc
@@ -1,16 +1,16 @@
 #!/bin/bash  
 
-if [ ! -d "./plots" ]; then
-	mkdir ./plots
+if [ ! -d "/tmp/plots" ]; then
+	mkdir /tmp/plots
 fi
-echo "" > ./plots/run_batch.txt        
-for i in $(ls -1v ./[p-q]*.py ); do
-	echo "" |& tee -a ./plots/run_batch.txt
-	echo "##############################################################" |& tee -a ./plots/run_batch.txt
-	echo "$i  ###############################" |& tee -a ./plots/run_batch.txt
-	echo "##############################################################" |& tee -a ./plots/run_batch.txt
-	echo "" |& tee -a ./plots/run_batch.txt
-   	python3 $i |& tee -a ./plots/run_batch.txt 
+echo "" > /tmp/plots/run_batch.txt        
+for i in $(ls -1v ./[o-q]*.py ); do
+	echo "" |& tee -a /tmp/plots/run_batch.txt
+	echo "##############################################################" |& tee -a /tmp/plots/run_batch.txt
+	echo "$i  ###############################" |& tee -a /tmp/plots/run_batch.txt
+	echo "##############################################################" |& tee -a /tmp/plots/run_batch.txt
+	echo "" |& tee -a /tmp/plots/run_batch.txt
+   	python3 $i |& tee -a /tmp/plots/run_batch.txt 
 
 done
 

From 22673f426cda49ab17e4b2e125a0fd6ac00146fe Mon Sep 17 00:00:00 2001
From: richard lyman <richard.r.lyman@gmail.com>
Date: Fri, 17 Feb 2017 15:08:38 -0800
Subject: [PATCH 2/6] misc commits

---
 n0_network.py                  |  96 ++++++++++
 n1_2cnv1fc.py                  | 240 ++++++++++++++++++++++++
 n1_2cnv2fc.py                  | 279 ++++++++++++++++++++++++++++
 n1_baseTensorNN.py             | 272 ++++++++++++++++++++++++++++
 n1_image_to_image.py           | 322 +++++++++++++++++++++++++++++++++
 n1_residual3x4.py              | 230 +++++++++++++++++++++++
 o1_top_secret_cnn.py           | 225 +++++++++++++++++++++++
 o2_top_secret_lda-tesseract.py | 266 +++++++++++++++++++++++++++
 o3_top_secret_python_box.py    | 266 +++++++++++++++++++++++++++
 9 files changed, 2196 insertions(+)
 create mode 100644 n0_network.py
 create mode 100644 n1_2cnv1fc.py
 create mode 100644 n1_2cnv2fc.py
 create mode 100644 n1_baseTensorNN.py
 create mode 100644 n1_image_to_image.py
 create mode 100644 n1_residual3x4.py
 create mode 100644 o1_top_secret_cnn.py
 create mode 100644 o2_top_secret_lda-tesseract.py
 create mode 100644 o3_top_secret_python_box.py

diff --git a/n0_network.py b/n0_network.py
new file mode 100644
index 0000000..29e140b
--- /dev/null
+++ b/n0_network.py
@@ -0,0 +1,96 @@
+import tensorflow as tf  
+import numpy as np
+from collections import namedtuple
+import datetime
+import ocr_utils
+
+class base_network(object):
+    ''' definition of the network
+    '''
+
+        
+        
+
+    def fit(self, truthed_data, nEpochs=5000):    
+
+        perfect_count=10
+        for i in range(nEpochs):
+        
+            batch = truthed_data.next_batch(100)
+            # assign feature data to each placeholder
+            # the batch list is returned in the same order as the features requested
+            feed = {self._keep_prob: 0.5}
+            for j in range(truthed_data.num_features):
+                feed[self._ph[j]] = batch[j]  
+                
+            if i%100 == 0:
+
+                feed[self._keep_prob] = 1.0
+                result = self._sess.run([self._merged, self._accuracy ], feed_dict=feed)    
+                summary_str = result[0]
+ 
+                self._writer.add_summary(summary_str, i)
+                train_accuracy = self._accuracy.eval(feed)    
+                if train_accuracy <= (1.0 - 1e-5  ):
+                    perfect_count=10;
+                else:
+                    perfect_count -= 1
+                    if perfect_count==0:
+                        break;  
+                    
+                print ("step %d, training accuracy %g"%(i, train_accuracy),flush=True)
+            self._train_step.run(feed_dict=feed)
+            
+
+    
+
+
+    def test(self, truthed_data,  title = ''): 
+
+        # assign feature data to each placeholder
+        error_images = np.empty((0,self._nRows,self._nCols))
+            
+        test_accuracy=0
+        m=0
+   
+        for i in range(int(len(truthed_data.features[0])/100)):
+        
+            batch = truthed_data.next_batch(100)
+            # assign feature data to each placeholder
+            # the batch list is returned in the same order as the features requested
+            feed = {self._keep_prob: 1.0}
+            for j in range(truthed_data.num_features):
+                feed[self._ph[j]] = batch[j]  
+                
+
+            result = self._sess.run([self._accuracy, self._x_image, self._correct_prediction], feed_dict=feed)    
+            
+            test_accuracy += result[0]
+            error_images = np.append(error_images, result[1][:,:,:,0][result[2]==False],axis=0)
+            m += 1
+        try:        
+            print ("test accuracy {} for : {}".format(test_accuracy/m, title),flush=True)       
+            ocr_utils.montage(error_images,title='TensorFlow {} Error Images'.format(title))  
+        except:  
+            if m==0:
+                print ("test accuracy 1",flush=True)
+            else:                                                                    
+                print ("test accuracy {}".format(test_accuracy/m),flush=True)  
+                ocr_utils.montage(error_images,title='TensorFlow Error Images') 
+            
+         
+    def predict(self, truthed_features): 
+        feed={self._keep_prob: 1.0}
+        # assign feature data to each placeholder
+        error_images = np.empty((truthed_features.num_rows,truthed_features.num_columns))
+            
+        test_accuracy=0
+        m=0
+          
+        for j in range(1,truthed_features.num_features):
+             feed[self._ph[j]] = truthed_features.features[j]
+        result = self._sess.run([self._prediction], feed_dict=feed)    
+    
+        return result[0]
+     
+                  
\ No newline at end of file
diff --git a/n1_2cnv1fc.py b/n1_2cnv1fc.py
new file mode 100644
index 0000000..117fa8d
--- /dev/null
+++ b/n1_2cnv1fc.py
@@ -0,0 +1,240 @@
+import tensorflow as tf  
+import numpy as np
+from collections import namedtuple
+import datetime
+import ocr_utils
+from  n0_network  import  base_network as b_network
+
+class network(b_network):
+    ''' definition of the network
+    '''
+    def __init__(self, truthed_features, dtype=np.float32):
+
+        self._sess = tf.InteractiveSession()  
+
+        lst = []
+        extra_features_width = 0 # width of extra features
+        
+        """# ==============================================================================
+        
+        Placeholders
+        
+        Compute the size of various layers 
+        
+        Create a tensorflow Placeholder for each feature of data returned from the
+        dataset
+        
+        """# ==============================================================================        
+        
+        for i,nm in enumerate(truthed_features.feature_names):
+            
+            # features[0], is always the target. For instance it may be m_label_one_hot 
+            # the second features[1] is the 'image' that is passed to the convolution layers 
+            # Any additional features bypass the convolution layers and go directly 
+            # into the fully connected layer.  
+            
+            # The width of the extra features is calculated in order to allocate 
+            # the correct widths of weights,  # and inputs 
+            # names are assigned to make the look pretty on the tensorboard graph.
+            
+            if i == 0:
+                nm = 'y_'+nm
+            else:
+                nm = 'x_'+nm
+            if i>1:
+                extra_features_width += truthed_features.feature_width[i]
+            lst.append(tf.placeholder(dtype, shape=[None, truthed_features.feature_width[i]], name=nm))
+            
+        # ph is a named tuple with key names like 'image', 'm_label', and values that
+        # are tensors.  The display name on the Chrome graph are 'y_m_label', 'x_image, 
+        # x_upper_case etc.
+    
+    
+        Place_Holders = namedtuple('Place_Holders', truthed_features.feature_names)   
+        self._ph = Place_Holders(*lst) # unpack placeholders into named Tuple
+        self._keep_prob = tf.placeholder(dtype,name='keep_prob')    
+        self._nRows = truthed_features.num_rows #image height
+        self._nCols = truthed_features.num_columns #image width    
+        nFc = 1024      # size of fully connected layer
+        nConv1 = 32     # size of first convolution layer
+        nConv2 = 64     # size of second convolution layer
+        nTarget = truthed_features.feature_width[0]  # the number of one_hot features in the target, 'm_label'    
+        n_h_pool2_outputs = int(self._nRows/4) * int(self._nCols/4) * nConv2 # second pooling layer 
+        n_h_pool2_outputsx = n_h_pool2_outputs + extra_features_width # fully connected
+            
+        """# ==============================================================================
+        
+        Build a Multilayer Convolutional Network
+        
+        Weight Initialization
+        
+        """# ==============================================================================
+        
+        def weight_variable(shape, dtype):
+            initial = tf.truncated_normal(shape, stddev=0.1,dtype=dtype)
+            return tf.Variable(initial)
+        
+        def bias_variable(shape, dtype):
+            initial = tf.constant(0.1, shape=shape, dtype=dtype)
+            return tf.Variable(initial)   
+        
+        """# ==============================================================================
+        
+        Convolution and Pooling
+        
+        keep our code cleaner, let's also abstract those operations into functions.
+        
+        """# ==============================================================================
+        
+        def conv2d(x, W):
+            return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
+        
+        def max_pool_2x2(x):
+            return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
+                                strides=[1, 2, 2, 1], padding='SAME')
+        
+        """# ==============================================================================
+        
+        First Convolutional Layer
+        
+        """# ==============================================================================
+        with tf.name_scope("w_conv1") as scope:
+            W_conv1 = weight_variable([5, 5, 1, nConv1],dtype)
+            b_conv1 = bias_variable([nConv1],dtype)    
+        
+        with tf.name_scope("reshape_x_image") as scope:
+            self._x_image = tf.reshape(self._ph.image, [-1,self._nCols,self._nRows,1])
+        
+        image_summ = tf.image_summary("x_image", self._x_image)
+        
+        """# ==============================================================================
+        
+        We then convolve x_image with the weight tensor, add the bias, apply the ReLU function,
+         and finally max pool.
+        
+        """# ==============================================================================
+        
+        with tf.name_scope("convolve_1") as scope:
+            h_conv1 = tf.nn.relu(conv2d(self._x_image, W_conv1) + b_conv1)
+            
+        with tf.name_scope("pool_1") as scope:    
+            h_pool1 = max_pool_2x2(h_conv1)
+        
+        """# ==============================================================================
+        
+        Second Convolutional Layer
+        
+        In order to build a deep network, we stack several layers of this type. The second 
+        layer will have 64 features for each 5x5 patch.
+        
+        """# ==============================================================================
+        
+        with tf.name_scope("convolve_2") as scope:
+            W_conv2 = weight_variable([5, 5, nConv1, nConv2],dtype)
+            b_conv2 = bias_variable([64],dtype)
+            h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)  
+             
+        with tf.name_scope("pool_2") as scope:
+            h_pool2 = max_pool_2x2(h_conv2)
+        
+        """# ==============================================================================
+        
+        Densely Connected Layer
+        
+        Now that the image size has been reduced to 7x7, we add a fully-connected layer 
+        with neurons to allow processing on the entire image. We reshape the tensor 
+        from the pooling layer into a batch of vectors, multiply by a weight matrix, add 
+        a bias, and apply a ReLU.
+        
+        """# ==============================================================================
+        
+        with tf.name_scope("W_fc1_b") as scope:
+            W_fc1 = weight_variable([n_h_pool2_outputsx, nFc],dtype)
+            b_fc1 = bias_variable([nFc],dtype)
+                
+            h_pool2_flat = tf.reshape(h_pool2, [-1, n_h_pool2_outputs])
+            
+            # append the features, the 2nd on, that go directly to the fully connected layer
+            for i in range(2,truthed_features.num_features ):
+                h_pool2_flat = tf.concat(1, [h_pool2_flat, self._ph[i]])  
+            h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
+        
+        """# ==============================================================================
+        
+        Dropout
+        
+        """# ==============================================================================
+        
+        
+        with tf.name_scope("drop") as scope:
+            h_fc1_drop = tf.nn.dropout(h_fc1, self._keep_prob)
+        
+        """# ==============================================================================
+        
+        Readout Layer
+        
+        """# ==============================================================================
+        with tf.name_scope("softmax") as scope:
+            W_fc2 = weight_variable([nFc, nTarget],dtype)
+            b_fc2 = bias_variable([nTarget],dtype)    
+            y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
+    
+        
+        with tf.name_scope("xent") as scope:
+        
+            # 1e-8 added to eliminate the crash of training when taking log of 0
+            cross_entropy = -tf.reduce_sum(self._ph[0]*tf.log(y_conv+ 1e-8  ))
+            ce_summ = tf.scalar_summary("cross entropy", cross_entropy)
+            
+        with tf.name_scope("train") as scope:
+            self._train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
+    
+        with tf.name_scope("test") as scope:        
+            self._correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(self._ph[0],1))
+            self._prediction = tf.argmax(y_conv,1)
+        
+            self._accuracy = tf.reduce_mean(tf.cast(self._correct_prediction, dtype))
+            accuracy_summary = tf.scalar_summary("accuracy", self._accuracy)    
+        """# ==============================================================================
+        
+        Start TensorFlow Interactive Session
+        
+        """# ==============================================================================   
+       
+        self._sess.run(tf.initialize_all_variables())  
+        self._merged = tf.merge_all_summaries()
+        tm = ""
+        tp = datetime.datetime.now().timetuple()
+        for i in range(4):
+            tm += str(tp[i])+'-'
+        tm += str(tp[4])    
+        self._writer = tf.train.SummaryWriter("/tmp/ds_logs/"+ tm, self._sess.graph)
+        
+        def computeSize(s,tens):
+            sumC = 1
+            tShape = tens.get_shape()
+            nDims = len(tShape)
+            for i in range(nDims):
+                sumC *= tShape[i].value
+            print ('\t{}\t{}'.format(s,sumC),flush=True)
+            return sumC
+                
+        print ('network size:',flush=True)
+        total = computeSize("W_fc1",W_fc1)+ \
+        computeSize ("b_fc1",b_fc1) + \
+        computeSize ("W_conv1",W_conv1) + \
+        computeSize ("b_conv1",b_conv1) + \
+        computeSize ("W_conv2",W_conv2) + \
+        computeSize ("b_conv2",b_conv2) + \
+        computeSize ("W_fc2",W_fc2) + \
+        computeSize ("b_fc2",b_fc2)
+        print('\ttotal\t{}'.format(total),flush=True )
+        
+
+    def reset_graph(self):
+        tf.reset_default_graph() # only necessary when iterating through fonts
+        self._sess.close()  
+        
+        
+
+                  
\ No newline at end of file
diff --git a/n1_2cnv2fc.py b/n1_2cnv2fc.py
new file mode 100644
index 0000000..831b904
--- /dev/null
+++ b/n1_2cnv2fc.py
@@ -0,0 +1,279 @@
+import tensorflow as tf  
+import numpy as np
+from collections import namedtuple
+import datetime
+from  n0_network  import  base_network as b_network
+import ocr_utils
+
+class network( b_network):
+    ''' definition of the network
+    '''
+       
+    def __init__(self, truthed_features, dtype=np.float32):
+        self._sess = tf.InteractiveSession()  
+      
+        lst = []
+        extra_features_width = 0 # width of extra features
+        
+        """# ==============================================================================
+        
+        Placeholders
+        
+        Compute the size of various layers 
+        
+        Create a tensorflow Placeholder for each feature of data returned from the
+        dataset
+        
+        """# ==============================================================================        
+        
+        for i,nm in enumerate(truthed_features.feature_names):
+            
+            # features[0], is always the target. For instance it may be m_label_one_hot 
+            # the second features[1] is the 'image' that is passed to the convolution layers 
+            # Any additional features bypass the convolution layers and go directly 
+            # into the fully connected layer.  
+            
+            # The width of the extra features is calculated in order to allocate 
+            # the correct widths of weights,  # and inputs 
+            # names are assigned to make the look pretty on the tensorboard graph.
+            
+            if i == 0:
+                nm = 'y_'+nm
+            else:
+                nm = 'x_'+nm
+            if i>1:
+                extra_features_width += truthed_features.feature_width[i]
+            lst.append(tf.placeholder(dtype, shape=[None, truthed_features.feature_width[i]], name=nm))
+            
+        # ph is a named tuple with key names like 'image', 'm_label', and values that
+        # are tensors.  The display name on the Chrome graph are 'y_m_label', 'x_image, 
+        # x_upper_case etc.
+    
+    
+        Place_Holders = namedtuple('Place_Holders', truthed_features.feature_names)   
+        self._ph = Place_Holders(*lst) # unpack placeholders into named Tuple
+        self._keep_prob = tf.placeholder(dtype,name='keep_prob')    
+        self._nRows = truthed_features.num_rows #image height
+        self._nCols = truthed_features.num_columns #image width    
+        nFc0 = 2048      # size of fully connected layer
+        nFc1 = 2048      # size of fully connected layer        
+        nFc2 = 2048      # size of fully connected layer              
+        nConv1 = 32     # size of first convolution layer
+        nConv2 = 64     # size of second convolution layer
+        nTarget = truthed_features.feature_width[0]  # the number of one_hot features in the target, 'm_label'    
+        n_h_pool2_outputs = int(self._nRows/4) * int(self._nCols/4) * nConv2 # second pooling layer 
+        n_h_pool2_outputsx = n_h_pool2_outputs + extra_features_width # fully connected
+            
+        """# ==============================================================================
+        
+        Build a Multilayer Convolutional Network
+        
+        Weight Initialization
+        
+        """# ==============================================================================
+        
+        def weight_variable(shape, dtype):
+            initial = tf.truncated_normal(shape, stddev=0.1,dtype=dtype)
+            return tf.Variable(initial)
+        
+        def bias_variable(shape, dtype):
+            initial = tf.constant(0, shape=shape, dtype=dtype)
+            return tf.Variable(initial)   
+        
+        """# ==============================================================================
+        
+        Convolution and Pooling
+        
+        keep our code cleaner, let's also abstract those operations into functions.
+        
+        """# ==============================================================================
+        
+        def conv2d(x, W):
+            return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
+        
+        def max_pool_2x2(x):
+            return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
+                                strides=[1, 2, 2, 1], padding='SAME')
+        
+        """# ==============================================================================
+        
+        First Convolutional Layer
+        
+        """# ==============================================================================
+        with tf.name_scope("w_conv1") as scope:
+            W_conv1 = weight_variable([5, 5, 1, nConv1],dtype)
+            b_conv1 = bias_variable([nConv1],dtype)    
+        
+        with tf.name_scope("reshape_x_image") as scope:
+            self._x_image = tf.reshape(self._ph.image, [-1,self._nCols,self._nRows,1])
+        
+        image_summ = tf.image_summary("x_image", self._x_image)
+        
+        """# ==============================================================================
+        
+        We then convolve x_image with the weight tensor, add the bias, apply the ReLU function,
+         and finally max pool.
+        
+        """# ==============================================================================
+        
+        with tf.name_scope("convolve_1") as scope:
+            h_conv1 = tf.nn.relu(conv2d(self._x_image, W_conv1) + b_conv1)
+            
+        with tf.name_scope("pool_1") as scope:    
+            h_pool1 = max_pool_2x2(h_conv1)
+        
+        """# ==============================================================================
+        
+        Second Convolutional Layer
+        
+        In order to build a deep network, we stack several layers of this type. The second 
+        layer will have 64 features for each 5x5 patch.
+        
+        """# ==============================================================================
+        
+        with tf.name_scope("convolve_2") as scope:
+            W_conv2 = weight_variable([5, 5, nConv1, nConv2],dtype)
+            b_conv2 = bias_variable([64],dtype)
+            h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)  
+             
+        with tf.name_scope("pool_2") as scope:
+            h_pool2 = max_pool_2x2(h_conv2)
+        
+        """# ==============================================================================
+        
+        Densely Connected Layer
+        
+        Now that the image size has been reduced to 7x7, we add a fully-connected layer 
+        with neurons to allow processing on the entire image. We reshape the tensor 
+        from the pooling layer into a batch of vectors, multiply by a weight matrix, add 
+        a bias, and apply a ReLU.
+        
+        """# ==============================================================================
+        
+        with tf.name_scope("W_fc1_b") as scope:
+            W_fc0 = weight_variable([n_h_pool2_outputsx, nFc0],dtype)
+            b_fc0 = bias_variable([nFc0],dtype)
+                
+            h_pool2_flat = tf.reshape(h_pool2, [-1, n_h_pool2_outputs])
+            
+            # append the features, the 2nd on, that go directly to the fully connected layer
+            for i in range(2,truthed_features.num_features ):
+                h_pool2_flat = tf.concat(1, [h_pool2_flat, self._ph[i]])  
+            h_fc0 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc0) + b_fc0)
+            
+        """# ==============================================================================
+            
+            Densely Connected Layer 1
+            
+            We add a fully-connected layer 
+            with neurons to allow processing on the entire image. We reshape the tensor 
+            from the pooling layer into a batch of vectors, multiply by a weight matrix, add 
+            a bias, and apply a ReLU.
+        
+        """# ==============================================================================  
+              
+        with tf.name_scope("W_fc1_b") as scope:
+            W_fc1 = weight_variable([nFc0, nFc1],dtype)
+            b_fc1 = bias_variable([nFc1],dtype)
+            
+            h_fc1 = tf.nn.relu(tf.matmul(h_fc0, W_fc1) + b_fc1)    
+        
+        """# ==============================================================================
+        
+        Densely Connected Layer 2
+        
+        We add a fully-connected layer 
+        with neurons to allow processing on the entire image. We reshape the tensor 
+        from the pooling layer into a batch of vectors, multiply by a weight matrix, add 
+        a bias, and apply a ReLU.
+        
+        """# ==============================================================================
+        
+        with tf.name_scope("W_fc2_b") as scope:
+            W_fc2 = weight_variable([nFc1, nFc2],dtype)
+            b_fc2 = bias_variable([nFc2],dtype)
+            
+            h_fc2 = tf.nn.relu(tf.matmul(h_fc1, W_fc2) + b_fc2)
+                    
+        """# ==============================================================================
+        
+        Dropout
+        
+        """# ==============================================================================
+        
+        
+        with tf.name_scope("drop") as scope:
+            h_fc2_drop = tf.nn.dropout(h_fc2, self._keep_prob)
+        
+        """# ==============================================================================
+        
+        Readout Layer
+        
+        """# ==============================================================================
+        with tf.name_scope("softmax") as scope:
+            W_fc3 = weight_variable([nFc2, nTarget],dtype)
+            b_fc3 = bias_variable([nTarget],dtype)    
+            y_conv=tf.nn.softmax(tf.matmul(h_fc2_drop, W_fc3) + b_fc3)    
+        
+        with tf.name_scope("xent") as scope:
+        
+            # 1e-8 added to eliminate the crash of training when taking log of 0
+            cross_entropy = -tf.reduce_sum(self._ph[0]*tf.log(y_conv+ 1e-8  ))
+            ce_summ = tf.scalar_summary("cross entropy", cross_entropy)
+            
+        with tf.name_scope("train") as scope:
+            self._train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
+    
+        with tf.name_scope("test") as scope:        
+            self._correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(self._ph[0],1))
+            self._prediction = tf.argmax(y_conv,1)
+        
+            self._accuracy = tf.reduce_mean(tf.cast(self._correct_prediction, dtype))
+            accuracy_summary = tf.scalar_summary("accuracy", self._accuracy)    
+        """# ==============================================================================
+        
+        Start TensorFlow Interactive Session
+        
+        """# ==============================================================================      
+    
+        self._sess.run(tf.initialize_all_variables())  
+        self._merged = tf.merge_all_summaries()
+        tm = ""
+        tp = datetime.datetime.now().timetuple()
+        for i in range(4):
+            tm += str(tp[i])+'-'
+        tm += str(tp[4])    
+        self._writer = tf.train.SummaryWriter("/tmp/ds_logs/"+ tm, self._sess.graph)
+        
+        def computeSize(s,tens):
+            sumC = 1
+            tShape = tens.get_shape()
+            nDims = len(tShape)
+            for i in range(nDims):
+                sumC *= tShape[i].value
+            print ('\t{}\t{}'.format(s,sumC),flush=True)
+            return sumC
+                
+        print ('network size:',flush=True)        
+        total = computeSize("W_fc0",W_fc0)+ \
+        computeSize ("b_fc0",b_fc0) + \
+        computeSize ("W_conv1",W_conv1) + \
+        computeSize ("b_conv1",b_conv1) + \
+        computeSize ("W_conv2",W_conv2) + \
+        computeSize ("b_conv2",b_conv2) + \
+        computeSize ("W_fc0",W_fc0) + \
+        computeSize ("b_fc0",b_fc0) + \
+        computeSize ("W_fc1",W_fc1) + \
+        computeSize ("b_fc1",b_fc1) + \
+        computeSize ("W_fc2",W_fc2) + \
+        computeSize ("b_fc2",b_fc2)  
+            
+        print('\ttotal\t{}'.format(total),flush=True)
+        
+    def reset_graph(self):
+        tf.reset_default_graph() # only necessary when iterating through fonts
+        self._sess.close()  
+        
+
+     
+                  
\ No newline at end of file
diff --git a/n1_baseTensorNN.py b/n1_baseTensorNN.py
new file mode 100644
index 0000000..62353db
--- /dev/null
+++ b/n1_baseTensorNN.py
@@ -0,0 +1,272 @@
+import tensorflow as tf  
+import numpy as np
+from collections import namedtuple
+import datetime
+import ocr_utils
+'''
+        # To see the results in Chrome, 
+        # Run the following in terminal to activate server.
+        # tensorboard --logdir '/tmp/ds_logs/'
+        # See results on localhost:6006 
+'''
+        
+class network(object):
+    ''' definition of the network
+    '''
+    def __init__(self, truthed_features, dtype=np.float32):
+        self._ph=None
+        self._keep_prob=None
+        self._train_step =None
+        self._accuracy=None   
+        self._prediction=None         
+        self._merged=None
+        self._writer=None
+        self._correct_prediction=None 
+
+
+        lst = []
+        extra_features_width = 0 # width of extra features
+        
+        """# ==============================================================================
+        
+        Placeholders
+        
+        Compute the size of various layers 
+        
+        Create a tensorflow Placeholder for each feature of data returned from the
+        dataset
+        
+        """# ==============================================================================        
+        
+        for i,nm in enumerate(truthed_features.feature_names):
+            
+            # features[0], is always the target. For instance it may be m_label_one_hot 
+            # the second features[1] is the 'image' that is passed to the convolution layers 
+            # Any additional features bypass the convolution layers and go directly 
+            # into the fully connected layer.  
+            
+            # The width of the extra features is calculated in order to allocate 
+            # the correct widths of weights,  # and inputs 
+            # names are assigned to make the look pretty on the tensorboard graph.
+            
+            if i == 0:
+                nm = 'y_'+nm
+            else:
+                nm = 'x_'+nm
+            if i>1:
+                extra_features_width += truthed_features.feature_width[i]
+            lst.append(tf.placeholder(dtype, shape=[None, truthed_features.feature_width[i]], name=nm))
+            
+        # ph is a named tuple with key names like 'image', 'm_label', and values that
+        # are tensors.  The display name on the Chrome graph are 'y_m_label', 'x_image, 
+        # x_upper_case etc.
+    
+    
+        Place_Holders = namedtuple('Place_Holders', truthed_features.feature_names)   
+        self._ph = Place_Holders(*lst) # unpack placeholders into named Tuple
+        self._keep_prob = tf.placeholder(dtype,name='keep_prob')    
+        self._nRows = truthed_features.num_rows #image height
+        self._nCols = truthed_features.num_columns #image width    
+        nFc = 1024      # size of fully connected layer
+        nConv1 = 32     # size of first convolution layer
+        nConv2 = 64     # size of second convolution layer
+        nTarget = truthed_features.feature_width[0]  # the number of one_hot features in the target, 'm_label'    
+        n_h_pool2_outputs = int(self._nRows/4) * int(self._nCols/4) * nConv2 # second pooling layer 
+        n_h_pool2_outputsx = n_h_pool2_outputs + extra_features_width # fully connected
+            
+        """# ==============================================================================
+        
+        Build a Multilayer Convolutional Network
+        
+        Weight Initialization
+        
+        """# ==============================================================================
+        
+        def weight_variable(shape, dtype):
+            initial = tf.truncated_normal(shape, stddev=0.1,dtype=dtype)
+            return tf.Variable(initial)
+        
+        def bias_variable(shape, dtype):
+            initial = tf.constant(0, shape=shape, dtype=dtype)
+            return tf.Variable(initial)   
+        
+        """# ==============================================================================
+        
+        Convolution and Pooling
+        
+        keep our code cleaner, let's also abstract those operations into functions.
+        
+        """# ==============================================================================
+        
+        def conv2d(x, W):
+            return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
+        
+        def max_pool_2x2(x):
+            return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
+                                strides=[1, 2, 2, 1], padding='SAME')
+        
+        """# ==============================================================================
+        
+        Image debugging output
+        
+        """# ==============================================================================  
+        
+        with tf.name_scope("reshape_x_image") as scope:
+            self._x_image = tf.reshape(self._ph.image, [-1,self._nCols,self._nRows,1])
+        
+        image_summ = tf.image_summary("x_image", self._x_image)
+
+
+        
+        """# ==============================================================================
+        
+        Dropout
+        
+        """# ==============================================================================
+        
+        with tf.name_scope("drop") as scope:
+            h_fc1_drop = tf.nn.dropout(h_fc1, self._keep_prob)
+        
+        """# ==============================================================================
+        
+        Readout Layer
+        
+        """# ==============================================================================
+        with tf.name_scope("softmax") as scope:
+            W_fc2 = weight_variable([nFc, nTarget],dtype)
+            b_fc2 = bias_variable([nTarget],dtype)    
+            y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
+    
+        
+        with tf.name_scope("xent") as scope:
+        
+            # 1e-8 added to eliminate the crash of training when taking log of 0
+            cross_entropy = -tf.reduce_sum(self._ph[0]*tf.log(y_conv+ 1e-8  ))
+            ce_summ = tf.scalar_summary("cross entropy", cross_entropy)
+            
+        with tf.name_scope("train") as scope:
+            self._train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
+    
+        with tf.name_scope("test") as scope:        
+            self._correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(self._ph[0],1))
+            self._prediction = tf.argmax(y_conv,1)
+        
+            self._accuracy = tf.reduce_mean(tf.cast(self._correct_prediction, dtype))
+            accuracy_summary = tf.scalar_summary("accuracy", self._accuracy)    
+        """# ==============================================================================
+        
+        Start TensorFlow Interactive Session
+        
+        """# ==============================================================================          
+        self._sess = tf.InteractiveSession()          
+        self._sess.run(tf.initialize_all_variables())  
+        self._merged = tf.merge_all_summaries()
+        tm = ""
+        tp = datetime.datetime.now().timetuple()
+        for i in range(4):
+            tm += str(tp[i])+'-'
+        tm += str(tp[4])    
+        self._writer = tf.train.SummaryWriter("/tmp/ds_logs/"+ tm, self._sess.graph)
+        
+        def computeSize(s,tens):
+            sumC = 1
+            tShape = tens.get_shape()
+            nDims = len(tShape)
+            for i in range(nDims):
+                sumC *= tShape[i].value
+            print ('\t{}\t{}'.format(s,sumC),flush=True)
+            return sumC
+                
+        print ('network size:',flush=True)
+        total = computeSize("W_fc1",W_fc1)+ \
+        computeSize ("b_fc1",b_fc1) + \
+        computeSize ("W_conv1",W_conv1) + \
+        computeSize ("b_conv1",b_conv1) + \
+        computeSize ("W_conv2",W_conv2) + \
+        computeSize ("b_conv2",b_conv2) + \
+        computeSize ("W_fc2",W_fc2) + \
+        computeSize ("b_fc2",b_fc2)
+        print('\ttotal\t{}'.format(total),flush=True )
+        
+
+        
+    def __exit__(self, exc_type, exc_value, traceback):
+        tf.reset_default_graph() # only necessary when iterating through fonts
+        self._sess.close()  
+        
+        
+
+    def fit(self, truthed_data, nEpochs=5000):    
+
+        perfect_count=10
+        for i in range(nEpochs):
+        
+            batch = truthed_data.next_batch(100)
+            # assign feature data to each placeholder
+            # the batch list is returned in the same order as the features requested
+            feed = {self._keep_prob: 0.5}
+            for j in range(truthed_data.num_features):
+                feed[self._ph[j]] = batch[j]  
+                
+            if i%100 == 0:
+                # sh=h_pool2_flat.get_shape()
+                feed[self._keep_prob] = 1.0
+                result = self._sess.run([self._merged, self._accuracy ], feed_dict=feed)    
+                summary_str = result[0]
+                #acc = result[1]       
+                self._writer.add_summary(summary_str, i)
+                train_accuracy = self._accuracy.eval(feed)    
+                if train_accuracy <= (1.0 - 1e-5  ):
+                    perfect_count=10;
+                else:
+                    perfect_count -= 1
+                    if perfect_count==0:
+                        break;  
+                    
+                print ("step %d, training accuracy %g"%(i, train_accuracy),flush=True)
+            self._train_step.run(feed_dict=feed)
+            
+
+    
+
+
+    def test(self, truthed_features): 
+        feed={self._keep_prob: 1.0}
+        # assign feature data to each placeholder
+        error_images = np.empty((0,self._nRows,self._nCols))
+            
+        test_accuracy=0
+        m=0
+          
+        for j in range(truthed_features.num_features):
+             feed[self._ph[j]] =truthed_features.features[j]
+        result = self._sess.run([self._accuracy, self._x_image, self._correct_prediction], feed_dict=feed)    
+        test_accuracy += result[0]
+        error_images = np.append(error_images, result[1][:,:,:,0][result[2]==False],axis=0)
+        m += 1
+        try:        
+            print ("test accuracy {} for font: {}".format(test_accuracy/m, input_filters_dict['font']),flush=True)       
+            ocr_utils.montage(error_images,title='TensorFlow {} Error Images'.format(input_filters_dict['font']))  
+        except:  
+            if m==0:
+                print ("test accuracy 1",flush=True)
+            else:                                                                    
+                print ("test accuracy {}".format(test_accuracy/m),flush=True)  
+                ocr_utils.montage(error_images,title='TensorFlow Error Images') 
+        
+         
+    def predict(self, truthed_features): 
+        feed={self._keep_prob: 1.0}
+        # assign feature data to each placeholder
+        error_images = np.empty((truthed_features.num_rows,truthed_features.num_columns))
+            
+        test_accuracy=0
+        m=0
+          
+        for j in range(1,truthed_features.num_features):
+             feed[self._ph[j]] = truthed_features.features[j]
+        result = self._sess.run([self._prediction], feed_dict=feed)    
+    
+        return result[0]
+     
+                  
\ No newline at end of file
diff --git a/n1_image_to_image.py b/n1_image_to_image.py
new file mode 100644
index 0000000..6ba2b08
--- /dev/null
+++ b/n1_image_to_image.py
@@ -0,0 +1,322 @@
+import tensorflow as tf  
+import numpy as np
+from collections import namedtuple
+import datetime
+from  n0_network  import  base_network as b_network
+import ocr_utils
+
+class network( b_network):
+    ''' definition of the network
+    '''
+       
+    def __init__(self, truthed_features, dtype=np.float32):
+        self._sess = tf.InteractiveSession()  
+      
+        lst = []
+        extra_features_width = 0 # width of extra features
+        
+        """# ==============================================================================
+        
+        Placeholders
+        
+        Compute the size of various layers 
+        
+        Create a tensorflow Placeholder for each feature of data returned from the
+        dataset
+        
+        """# ==============================================================================        
+        
+        for i,nm in enumerate(truthed_features.feature_names):
+            
+            # features[0], is always the target. For instance it may be m_label_one_hot 
+            # the second features[1] is the 'image' that is passed to the convolution layers 
+            # Any additional features bypass the convolution layers and go directly 
+            # into the fully connected layer.  
+            
+            # The width of the extra features is calculated in order to allocate 
+            # the correct widths of weights,  # and inputs 
+            # names are assigned to make the look pretty on the tensorboard graph.
+            
+            if i == 0:
+                nm = 'y_'+nm
+            else:
+                nm = 'x_'+nm
+            if i>1:
+                extra_features_width += truthed_features.feature_width[i]
+            lst.append(tf.placeholder(dtype, shape=[None, truthed_features.feature_width[i]], name=nm))
+            
+        # ph is a named tuple with key names like 'image', 'm_label', and values that
+        # are tensors.  The display name on the Chrome graph are 'y_m_label', 'x_image, 
+        # x_upper_case etc.
+    
+    
+        Place_Holders = namedtuple('Place_Holders', truthed_features.feature_names)   
+        self._ph = Place_Holders(*lst) # unpack placeholders into named Tuple
+        self._keep_prob = tf.placeholder(dtype,name='keep_prob')    
+        self._nRows = truthed_features.num_rows #image height
+        self._nCols = truthed_features.num_columns #image width    
+        nFc0 = 2048      # size of fully connected layer
+        nFc1 = 100      # size of fully connected layer        
+        nFc2 = self._nRows*self._nCols      # size of fully connected layer              
+        nConv1 = 32     # size of first convolution layer
+        nConv2 = 64     # size of second convolution layer
+        nTarget = truthed_features.feature_width[0]  # the number of one_hot features in the target, 'm_label'    
+        n_h_pool2_outputs = int(self._nRows/4) * int(self._nCols/4) * nConv2 # second pooling layer 
+        n_h_pool2_outputsx = n_h_pool2_outputs + extra_features_width # fully connected
+            
+        """# ==============================================================================
+        
+        Build a Multilayer Convolutional Network
+        
+        Weight Initialization
+        
+        """# ==============================================================================
+        
+        def weight_variable(shape, dtype):
+            initial = tf.truncated_normal(shape, stddev=0.1,dtype=dtype)
+            return tf.Variable(initial)
+        
+        def bias_variable(shape, dtype):
+            initial = tf.constant(0, shape=shape, dtype=dtype)
+            return tf.Variable(initial)   
+        
+        """# ==============================================================================
+        
+        Convolution and Pooling
+        
+        keep our code cleaner, let's also abstract those operations into functions.
+        
+        """# ==============================================================================
+        
+        def conv2d(x, W):
+            return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
+        
+        def max_pool_2x2(x):
+            return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
+                                strides=[1, 2, 2, 1], padding='SAME')
+        
+        """# ==============================================================================
+        
+        First Convolutional Layer
+        
+        """# ==============================================================================
+        with tf.name_scope("w_conv1") as scope:
+            W_conv1 = weight_variable([5, 5, 1, nConv1],dtype)
+            b_conv1 = bias_variable([nConv1],dtype)    
+        
+        with tf.name_scope("reshape_x_image") as scope:
+            self._x_image = tf.reshape(self._ph.image, [-1,self._nCols,self._nRows,1])
+        
+        image_summ = tf.image_summary("x_image", self._x_image)
+        
+        """# ==============================================================================
+        
+        We then convolve x_image with the weight tensor, add the bias, apply the ReLU function,
+         and finally max pool.
+        
+        """# ==============================================================================
+        
+        with tf.name_scope("convolve_1") as scope:
+            h_conv1 = tf.nn.relu(conv2d(self._x_image, W_conv1) + b_conv1)
+            
+        with tf.name_scope("pool_1") as scope:    
+            h_pool1 = max_pool_2x2(h_conv1)
+        
+        """# ==============================================================================
+        
+        Second Convolutional Layer
+        
+        In order to build a deep network, we stack several layers of this type. The second 
+        layer will have 64 features for each 5x5 patch.
+        
+        """# ==============================================================================
+        
+        with tf.name_scope("convolve_2") as scope:
+            W_conv2 = weight_variable([5, 5, nConv1, nConv2],dtype)
+            b_conv2 = bias_variable([64],dtype)
+            h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)  
+             
+        with tf.name_scope("pool_2") as scope:
+            h_pool2 = max_pool_2x2(h_conv2)
+        
+        """# ==============================================================================
+        
+        Densely Connected Layer
+        
+        Now that the image size has been reduced to 7x7, we add a fully-connected layer 
+        with neurons to allow processing on the entire image. We reshape the tensor 
+        from the pooling layer into a batch of vectors, multiply by a weight matrix, add 
+        a bias, and apply a ReLU.
+        
+        """# ==============================================================================
+        
+        with tf.name_scope("W_fc1_b") as scope:
+            W_fc0 = weight_variable([n_h_pool2_outputsx, nFc0],dtype)
+            b_fc0 = bias_variable([nFc0],dtype)
+                
+            h_pool2_flat = tf.reshape(h_pool2, [-1, n_h_pool2_outputs])
+            
+            # append the features, the 2nd on, that go directly to the fully connected layer
+            for i in range(2,truthed_features.num_features ):
+                h_pool2_flat = tf.concat(1, [h_pool2_flat, self._ph[i]])  
+            h_fc0 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc0) + b_fc0)
+            
+        """# ==============================================================================
+            
+            Densely Connected Layer 1
+            
+            We add a fully-connected layer 
+            with neurons to allow processing on the entire image. We reshape the tensor 
+            from the pooling layer into a batch of vectors, multiply by a weight matrix, add 
+            a bias, and apply a ReLU.
+        
+        """# ==============================================================================  
+              
+        with tf.name_scope("W_fc1_b") as scope:
+            W_fc1 = weight_variable([nFc0, nFc1],dtype)
+            b_fc1 = bias_variable([nFc1],dtype)
+            
+            h_fc1 = tf.nn.relu(tf.matmul(h_fc0, W_fc1) + b_fc1)    
+        
+        """# ==============================================================================
+        
+        Densely Connected Layer 2
+        
+        We add a fully-connected layer 
+        with neurons to allow processing on the entire image. We reshape the tensor 
+        from the pooling layer into a batch of vectors, multiply by a weight matrix, add 
+        a bias, and apply a ReLU.
+        
+        """# ==============================================================================
+        
+        with tf.name_scope("W_fc2_b") as scope:
+            W_fc2 = weight_variable([nFc1, nFc2],dtype)
+            b_fc2 = bias_variable([nFc2],dtype)
+            
+            h_fc2 = tf.nn.relu(tf.matmul(h_fc1, W_fc2) + b_fc2)
+                    
+        """# ==============================================================================
+        
+        Dropout
+        
+        """# ==============================================================================
+        
+        
+        with tf.name_scope("drop") as scope:
+            h_fc2_drop = tf.nn.dropout(h_fc2, self._keep_prob)
+        
+        """# ==============================================================================
+        
+        Readout Layer
+        
+        """# ==============================================================================
+        with tf.name_scope("softmax") as scope:
+            W_fc3 = weight_variable([nFc2, nTarget],dtype)
+            b_fc3 = bias_variable([nTarget],dtype)    
+            y_conv=tf.nn.softmax(tf.matmul(h_fc2_drop, W_fc3) + b_fc3)    
+        
+        with tf.name_scope("xent") as scope:
+        
+            # 1e-8 added to eliminate the crash of training when taking log of 0
+            cross_entropy = -tf.reduce_sum(self._ph[0]*tf.log(y_conv+ 1e-8  ))
+            #cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
+             #   logits, labels, name='xentropy')            
+            ce_summ = tf.scalar_summary("cross entropy", cross_entropy)
+            
+        with tf.name_scope("reshape_x_image2") as scope:
+            self._x_image2 = tf.reshape(self._ph[0], [-1,int(self._nCols/2),int(self._nRows/2),1])
+        
+        image_summ2 = tf.image_summary("x_image2", self._x_image2)            
+            
+        with tf.name_scope("train") as scope:
+            self._train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
+            #self._train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)            
+    
+        with tf.name_scope("test") as scope:        
+            self._correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(self._ph[0],1))
+            self._prediction = tf.argmax(y_conv,1)
+        
+            self._accuracy = tf.reduce_mean(tf.cast(self._correct_prediction, dtype))
+            accuracy_summary = tf.scalar_summary("accuracy", self._accuracy)    
+        """# ==============================================================================
+        
+        Start TensorFlow Interactive Session
+        
+        """# ==============================================================================      
+    
+        self._sess.run(tf.initialize_all_variables())  
+        self._merged = tf.merge_all_summaries()
+        tm = ""
+        tp = datetime.datetime.now().timetuple()
+        for i in range(4):
+            tm += str(tp[i])+'-'
+        tm += str(tp[4])    
+        self._writer = tf.train.SummaryWriter("/tmp/ds_logs/"+ tm, self._sess.graph)
+        
+        def computeSize(s,tens):
+            sumC = 1
+            tShape = tens.get_shape()
+            nDims = len(tShape)
+            for i in range(nDims):
+                sumC *= tShape[i].value
+            print ('\t{}\t{}'.format(s,sumC),flush=True)
+            return sumC
+                
+        print ('network size:',flush=True)        
+        total = computeSize("W_fc0",W_fc0)+ \
+        computeSize ("b_fc0",b_fc0) + \
+        computeSize ("W_conv1",W_conv1) + \
+        computeSize ("b_conv1",b_conv1) + \
+        computeSize ("W_conv2",W_conv2) + \
+        computeSize ("b_conv2",b_conv2) + \
+        computeSize ("W_fc0",W_fc0) + \
+        computeSize ("b_fc0",b_fc0) + \
+        computeSize ("W_fc1",W_fc1) + \
+        computeSize ("b_fc1",b_fc1) + \
+        computeSize ("W_fc2",W_fc2) + \
+        computeSize ("b_fc2",b_fc2)  
+            
+        print('\ttotal\t{}'.format(total),flush=True)
+        
+    def reset_graph(self):
+        tf.reset_default_graph() # only necessary when iterating through fonts
+        self._sess.close()  
+        
+    def test2(self, truthed_data,  title = ''): 
+
+        # assign feature data to each placeholder
+
+        output_images = np.empty((0,int(self._nRows/2),int(self._nCols/2)))
+        input_images = np.empty((0,int(self._nRows),int(self._nCols)))            
+        test_accuracy=0
+        m=0
+   
+        for i in range(int(len(truthed_data.features[0])/100)):
+        
+            batch = truthed_data.next_batch(100)
+            # assign feature data to each placeholder
+            # the batch list is returned in the same order as the features requested
+            feed = {self._keep_prob: 1.0}
+            for j in range(truthed_data.num_features):
+                feed[self._ph[j]] = batch[j]  
+                
+
+            result = self._sess.run([self._accuracy, self._x_image, self._correct_prediction, self._x_image2], feed_dict=feed)    
+            
+            test_accuracy += result[0]
+            input_images  = np.append(input_images, result[1][:,:,:,0],axis=0) 
+            output_images  = np.append(output_images, result[3][:,:,:,0],axis=0)         
+            m += 1
+        try:        
+            print ("test accuracy {} for : {}".format(test_accuracy/m, title),flush=True)       
+            ocr_utils.montage(input_images,title='TensorFlow {} Input Images'.format(title))    
+            ocr_utils.montage(output_images,title='TensorFlow {} Output Images'.format(title))             
+        except:  
+            if m==0:
+                print ("test accuracy 1",flush=True)
+            else:                                                                    
+                print ("test accuracy {}".format(test_accuracy/m),flush=True)  
+                ocr_utils.montage(output_images,title='TensorFlow Output Images')         
+                ocr_utils.montage(input_images,title='TensorFlow Input Images')        
+     
+                  
\ No newline at end of file
diff --git a/n1_residual3x4.py b/n1_residual3x4.py
new file mode 100644
index 0000000..b274714
--- /dev/null
+++ b/n1_residual3x4.py
@@ -0,0 +1,230 @@
+'''
+
+takes an image input and trains it to make an image output
+
+funnels down to a 'key' and then goes back up to image
+
+
+
+'''
+import tensorflow as tf  
+import numpy as np
+from collections import namedtuple
+import datetime
+import ocr_utils
+from  n0_network  import  base_network as b_network
+
+class network(b_network):
+    ''' definition of the network
+    '''
+    def __init__(self, truthed_features, dtype=np.float32):
+        self._sess = tf.InteractiveSession()  
+
+        lst = []
+        extra_features_width = 0 # width of extra features
+        
+        """# ==============================================================================
+        
+        Placeholders
+        
+        Compute the size of various layers 
+        
+        Create a tensorflow Placeholder for each feature of data returned from the
+        dataset
+        
+        """# ==============================================================================        
+        
+        for i,nm in enumerate(truthed_features.feature_names):
+            
+            # features[0], is always the target. For instance it may be m_label_one_hot 
+            # the second features[1] is the 'image' that is passed to the convolution layers 
+            # Any additional features bypass the convolution layers and go directly 
+            # into the fully connected layer.  
+            
+            # The width of the extra features is calculated in order to allocate 
+            # the correct widths of weights,  # and inputs 
+            # names are assigned to make the look pretty on the tensorboard graph.
+            
+            if i == 0:
+                nm = 'y_'+nm
+            else:
+                nm = 'x_'+nm
+            if i>1:
+                extra_features_width += truthed_features.feature_width[i]
+            lst.append(tf.placeholder(dtype, shape=[None, truthed_features.feature_width[i]], name=nm))
+            
+        # ph is a named tuple with key names like 'image', 'm_label', and values that
+        # are tensors.  The display name on the Chrome graph are 'y_m_label', 'self._x_image, 
+        # x_upper_case etc.
+    
+    
+        Place_Holders = namedtuple('Place_Holders', truthed_features.feature_names)   
+        self._ph = Place_Holders(*lst) # unpack placeholders into named Tuple
+        self._keep_prob = tf.placeholder(dtype,name='keep_prob')    
+        self._nRows = truthed_features.num_rows #image height
+        self._nCols = truthed_features.num_columns #image width    
+        nSections = 10
+    
+        in_out_width = self._nRows*self._nCols
+        internal_width = int(in_out_width/4)
+        w = list(range(nSections*3))
+        b = list(range(nSections*3))
+        h = list(range(nSections*3+1))        
+        nFc1 = 2048      # size of fully connected layer
+
+        nTarget = truthed_features.feature_width[0]  # the number of one_hot features in the target, 'm_label'
+            
+        """# ==============================================================================
+        
+        Build a Multilayer Convolutional Network
+        
+        Weight Initialization
+        
+        """# ==============================================================================
+        
+        def weight_variable(shape, dtype):
+            initial = tf.truncated_normal(shape, stddev=0.1,dtype=dtype)
+            return tf.Variable(initial)
+        
+        def bias_variable(shape, dtype):
+            initial = tf.constant(0, shape=shape, dtype=dtype)
+            return tf.Variable(initial)   
+                
+        def shapeOuts(n):
+            print ('n={}, hin={},w={}, b={} ,hout={}\n'.format(n, h[n]._shape, w[n]._variable._shape, b[n]._variable._shape, h[n+1]._shape))
+             
+        def section(n):
+            with tf.name_scope('section_'+str(n)+'_0') as scope:     
+                w[n]=weight_variable([in_out_width, internal_width],dtype)
+                b[n]=bias_variable([internal_width],dtype)  
+                h[n+1] = tf.nn.relu(tf.matmul(h[n], w[n]) + b[n])
+                shapeOuts(n)
+                 
+            with tf.name_scope('section_'+str(n)+'_1') as scope:  
+                w[n+1]=weight_variable([internal_width, internal_width],dtype)
+                b[n+1]=bias_variable([internal_width],dtype)     
+                               
+                h[n+2]=tf.nn.relu(tf.matmul(h[n+1], w[n+1]) + b[n+1])
+                shapeOuts(n+1)                  
+                                 
+            with tf.name_scope('section_'+str(n)+'_2') as scope:  
+                w[n+2]=weight_variable([internal_width, in_out_width],dtype)
+                b[n+2]=bias_variable([in_out_width],dtype)   
+                z= tf.nn.relu(tf.matmul(h[n+2], w[n+2]) + b[n+2])
+                h[n+3]= tf.add(z   ,h[n]) #n+3   
+                          
+                print('z shape ={}'.format(z._shape)) 
+                shapeOuts(n+2)                  
+            return    
+                   
+        def computeSize(s,tens):
+            sumC = 1
+            tShape = tens.get_shape()
+            nDims = len(tShape)
+            for i in range(nDims):
+                sumC *= tShape[i].value
+            print ('\t{}\t{}'.format(s,sumC),flush=True)
+            return sumC
+                         
+        """# ==============================================================================        
+        Build sectional network
+         
+        """# ==============================================================================      
+        h[0]= self._ph[1]
+        for i in range(nSections):
+            section(3*i)
+                 
+        """# ==============================================================================        
+        Dropout
+         
+        """# ==============================================================================
+        self._keep_prob = tf.placeholder(dtype,name='keep_prob')
+         
+        with tf.name_scope("drop") as scope:
+            h_fc2_drop = tf.nn.dropout(h[nSections*3], self._keep_prob)
+         
+        """# ==============================================================================
+         
+        Readout Layer
+         
+        """# ==============================================================================
+        with tf.name_scope("softmax") as scope:
+            w_fc3 = weight_variable([in_out_width, nTarget],dtype)
+            b_fc3 = bias_variable([nTarget],dtype)    
+            y_conv=tf.nn.softmax(tf.matmul(h_fc2_drop, w_fc3) + b_fc3)
+         
+        print ('network size:',flush=True)
+        total = 0
+        for i in range(nSections*3):
+            total = total + computeSize("w{}".format(i),w[i])
+        total = total + computeSize ("b_fc3",b_fc3) + \
+            computeSize ("w_fc3",w_fc3)      
+         
+        print('\ttotal\t{}'.format(total),flush=True)
+         
+            
+        with tf.name_scope("reshape_self._x_image") as scope:        
+            self._x_image = tf.reshape(self._ph.image, [-1,self._nCols,self._nRows,1])
+         
+        with tf.name_scope("xent") as scope:
+            # 1e-8 added to eliminate the crash of training when taking log of 0
+            cross_entropy = -tf.reduce_sum(self._ph[0]*tf.log(y_conv+1e-8))
+            ce_summ = tf.scalar_summary("cross entropy", cross_entropy)
+                 
+        with tf.name_scope("train") as scope:
+            self._train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
+             
+        with tf.name_scope("test") as scope:        
+            self._correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(self._ph[0],1))
+            self._prediction = tf.argmax(y_conv,1)            
+         
+            self._accuracy = tf.reduce_mean(tf.cast(self._correct_prediction, dtype))
+            accuracy_summary = tf.scalar_summary("accuracy", self._accuracy)    
+
+        """# ==============================================================================
+        
+        Start TensorFlow Interactive Session
+        
+        """# ==============================================================================          
+        
+        self._sess.run(tf.initialize_all_variables())  
+        self._merged = tf.merge_all_summaries()
+        tm = ""
+        tp = datetime.datetime.now().timetuple()
+        for i in range(4):
+            tm += str(tp[i])+'-'
+        tm += str(tp[4])    
+        
+        # To see the results in Chrome, 
+        # Run the following in terminal to activate server.
+        # tensorboard --logdir '/tmp/ds_logs/'
+        # See results on localhost:6006 
+        
+        self._writer = tf.train.SummaryWriter("/tmp/ds_logs/"+ tm, self._sess.graph)
+        
+    def computeSize(s,tens):
+        sumC = 1
+        tShape = tens.get_shape()
+        nDims = len(tShape)
+        for i in range(nDims):
+            sumC *= tShape[i].value
+        print ('\t{}\t{}'.format(s,sumC),flush=True)
+        return sumC
+        
+
+        
+    def __exit__(self, exc_type, exc_value, traceback):
+        tf.reset_default_graph() # only necessary when iterating through fonts
+        self._sess.close()  
+            
+
+    def reset_graph(self):
+        tf.reset_default_graph() # only necessary when iterating through fonts
+        self._sess.close()  
+        
+#      
+#     def encode(self):  
+#         
+#         return key
+#         
+#     def decode(self, key):            
\ No newline at end of file
diff --git a/o1_top_secret_cnn.py b/o1_top_secret_cnn.py
new file mode 100644
index 0000000..86e931a
--- /dev/null
+++ b/o1_top_secret_cnn.py
@@ -0,0 +1,225 @@
+#!/usr/bin/python
+
+
+"""# ==========================================================================
+
+# Copyright 2015 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+
+encode a secret message in the angle of rotation of characters
+
+Train a neural network on rotated versions of characters with the output of 
+the network being the angle of rotation.  
+
+Thus, given a rotated character, the neural network will yield a value 
+that is the amount of rotation of the character.
+
+Encode a test set by applying a secret message with one bit for each character.
+Decode the secret message by running the rotated characters through the 
+neural network, yielding the pattern of bits.
+
+
+@author: richard lyman
+
+"""# ==============================================================================
+import ocr_utils
+
+
+import numpy as np
+from PIL import Image, ImageDraw
+import io
+#import n1_2cnv1fc as nnetwork     
+#import n1_residual3x4 as nnetwork 
+import n1_2cnv2fc as nnetwork     
+input_filters_dict = {'m_label': list(range(48,58))+list(range(65,91))}  
+output_feature_list  = ['orientation_one_hot','image']   
+dtype = np.float32
+
+skewRange = np.linspace(-0.2,0.2,2)
+       
+'''
+ pick up the base character
+ 
+ make a training set by rotating them through n angles
+ 
+ train
+ 
+ pick up the base characters
+ encode the secret message n bits at a time into the characters
+ this is the testing set
+ 
+ test secret message yielding a vector of rotations
+ 
+ convert the rotation back into bits
+ 
+ assemble the bits into the secret message.
+ '''
+
+
+# pick up the base characters from training_image_file
+# produce some skeared versions 
+# make into a training set
+# place in a ocr_utils TruthedCharacters class so we can use the
+# one hot and batch functions 
+
+character_size = 100
+white_space=8
+
+image_file= '15-01-01 459_Mont_Lyman'
+image_file_jpg = image_file+'.jpg'
+
+df,t1  = ocr_utils.file_to_df(image_file,character_size,title='Characters to Train',white_space=white_space)
+
+shp = t1.shape
+totalN = len(skewRange)*shp[0]
+
+images=[]
+originalH=[]
+originalW=[]
+tops=[]
+lefts=[]
+orientation=[]
+recognized_label =[]
+
+import skimage.transform as af  
+
+for j in range(shp[0]):
+    for i,skew in enumerate(skewRange):
+        k = i+j*len(skewRange)
+        
+        images.append(ocr_utils.shear(t1[j],skew))
+        originalH.append(df['originalH'][j])
+        tops.append(df['m_top'][j])        
+        originalW.append(df['originalW'][j])
+        lefts.append(df['m_left'][j])
+        
+        orientation.append(skew)
+        recognized_label.append( df['m_label'][j])
+images=np.array(images)
+ocr_utils.montage(images, title='Base Characters Skewed')  
+
+images = np.reshape(images,(images.shape[0],images.shape[1]*images.shape[2]))
+df = ocr_utils.make_df(images, character_size, character_size, originalH, originalW, tops, lefts, orientation, recognized_label )
+#df = ocr_utils.make_df(images, character_size, character_size, bottoms, rights, tops, lefts, orientation, recognized_label )
+
+
+# input_filters_dict = {'m_label': list(range(48,58))+list(range(65,91))}  
+input_filters_dict = {'m_label': list(range(48,58))+list(range(65,91))}  
+output_feature_list  = ['orientation_one_hot','image']   
+ds = ocr_utils.read_df(df,input_filters_dict = input_filters_dict, 
+                            output_feature_list=output_feature_list,
+                            test_size = 0,
+                            engine_type='tensorflow',
+                            dtype=dtype) 
+
+nn = nnetwork.network(ds.train) 
+"""# ==============================================================================
+
+Train and Evaluate the Model
+
+"""# ==============================================================================
+    
+nn.fit( ds.train ,  nEpochs=5000)
+
+#######################################################################################
+
+# now that the font is trained, pick up some text and encode a message
+image_file= '15-01-01 459_Mont_Lyman'
+image_file_jpg = image_file+'.jpg'
+df,t1 = ocr_utils.file_to_df(image_file,character_size, title = 'unencrypted file',white_space=white_space)
+ 
+from bitarray import bitarray  
+secret_message = "top secret"
+a = bitarray()   
+a.fromstring(secret_message)
+
+index = 0
+encoded_skews=[]
+def convert_to_shear(a):
+    index = 0
+    while True:
+        if index < len(a):        
+            bits = a[index:index+1].to01()      
+            index += 1
+            #c = int(bits,2)
+            c = int(bits)            
+            yield c
+        else:
+            yield -1   
+            
+gen= convert_to_shear(a)
+
+im = Image.open(image_file_jpg)     
+img2 = Image.new('L',(im.height,im.width),color=255)
+img3 = Image.new('L',(im.height,im.width),color=255)
+draw = ImageDraw.Draw(img3) 
+for i in range(t1.shape[0]):
+    left = int(df['m_left'][i])
+    right = left + int(df['originalW'][i])
+    top = int(df['m_top'][i])  
+    bottom = top + int(df['originalH'][i])  
+    skew_index = next(gen)
+    #print ('i={}, skew_index={}, left={}, top={}, right={}, bottom={}'.format(i,skew_index, left,top,right,bottom))    
+    encoded_skews.append(skew_index)
+    if skew_index >= 0:
+        t1[i] = ocr_utils.shear(t1[i], skewRange[skew_index]) 
+    im_clip = Image.fromarray(256.0-t1[i]*256.0)  
+    img2.paste(im_clip, box= (left , top))
+    img3.paste(im_clip, box= (left , top))  
+         
+
+    draw.rectangle((left,top,right+2*white_space,bottom+2*white_space), outline=0)
+    
+gen.close()
+
+###########################################################################vvvvvvv
+image_file= '/tmp/plots/01_encrypted_file'
+image_file_jpg = image_file+'.jpg'
+img2.save(image_file_jpg)   
+
+
+image_file3= '/tmp/plots/01_03_encrypted_file_with_box'
+image_file3_jpg = image_file3+'.jpg'
+img3.save(image_file3_jpg)    
+
+''' test the new encrptyed file
+'''
+df,t1  = ocr_utils.file_to_df(image_file,character_size, title = 'Encrypted File',white_space=white_space)
+
+ds = ocr_utils.read_df(df,input_filters_dict = input_filters_dict, 
+                            output_feature_list=output_feature_list,
+                            test_size = 1,
+                            engine_type='tensorflow',
+                            dtype=dtype)
+    
+results = nn.predict(ds.test)  
+correct_characters=[]
+incorrect_characters=[]
+for i,x in enumerate(df['m_label']):
+    try:
+        print('index={}, original character={}, result= {}, skew={}'.format(i, chr(int(x)),results[i], encoded_skews[i])   )  
+        if encoded_skews[i] >=0:
+            if results[i] == encoded_skews[i]:
+                correct_characters.append(chr(int(x)))
+            else:
+                incorrect_characters.append(chr(int(x)))
+    except:
+        print ('index out of bounds={}'.format(i))
+print ('correct characters={}'.format(correct_characters))       
+print ('incorrect characters={}'.format(incorrect_characters))
+     
+print ('\n########################### No Errors ####################################')
+
diff --git a/o2_top_secret_lda-tesseract.py b/o2_top_secret_lda-tesseract.py
new file mode 100644
index 0000000..899c6ad
--- /dev/null
+++ b/o2_top_secret_lda-tesseract.py
@@ -0,0 +1,266 @@
+'''
+
+Created on Oct, 2016
+T
+
+@author: richard
+'''
+import ocr_utils
+import numpy as np
+from PIL import Image, ImageDraw
+import io
+from sklearn.linear_model import LogisticRegression
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA   
+from sklearn.metrics import accuracy_score
+
+inputs = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghiklnopqrstuvwxyz'
+inputs_list = list(ord(x) for x in inputs)
+input_filters_dict = {'m_label': inputs_list}
+# input_filters_dict={}
+             
+output_feature_list  = ['orientation','image']   
+dtype = np.float32
+
+#if -0.3 whitespace 8 is not enough
+#if 0-.2 then whitespace 6 is just enough
+character_size = 100
+white_space=6
+skewRange = np.linspace(-0.1,0.1,4)
+       
+'''
+ pick up the base character via tesseract
+ 
+ make a training set by shearing them 
+ 
+ save images
+ 
+ retrieve and unbox with tesseract
+ 
+ train
+ 
+ pick up the base characters
+ encode the secret message n bits at a time into the characters
+ this is the testing set
+ 
+ test secret message yielding a vector of rotations
+ 
+ convert the rotation back into bits
+ 
+ assemble the bits into the secret message.
+ '''
+
+
+# pick up the base characters from training_image_file
+# produce some skeared versions 
+# make into a training set
+# place in a ocr_utils TruthedCharacters class so we can use the
+# one hot and batch functions 
+
+
+def encode_and_save_file(input_base, output_base, character_size, white_space, secret_message=''):       
+    input_image_file_jpg = input_base+ocr_utils.extension
+    ouput_encoded_file = output_base +ocr_utils.extension
+    output_box_file =  output_base +'_box' +ocr_utils.extension
+    print ('input_base = {}'.format(input_base))
+    print ('input_image_file_jpg = {}'.format(input_image_file_jpg))
+    print ('ouput_encoded_file = {}'.format(ouput_encoded_file))    
+    print ('output_box_file = {}'.format(output_box_file))
+    
+    df,t1 = ocr_utils.file_to_df(input_base, character_size, title = 'unencrypted file', white_space=white_space, input_filters_dict=input_filters_dict)
+    
+    from bitarray import bitarray  
+    a = bitarray()   
+    a.fromstring(secret_message)
+    
+    index = 0
+
+    def convert_to_shear(a):
+        index = 0
+        while True:
+            if index < len(a)-1:        
+                bits = a[index:index+2].to01()      
+                index += 2
+                c = int(bits,2)
+                #c = int(bits)            
+                yield c
+            else:
+                yield -1   
+                
+    def draw_encoded_images(skews_indices, offset=0):
+            
+        for i in range(len(t1)):
+            left = right = top = bottom = 0
+            try:
+                left = int((df['m_left']).iloc[i])
+                right = left + int((df['originalW']).iloc[i])
+                top = int((df['m_top']).iloc[i]) +offset 
+                bottom = top + int((df['originalH']).iloc[i])  
+                skew_index = skews_indices[i]
+                #print ('i={}, skew_index={}, left={}, top={}, right={}, bottom={}'.format(i,skew_index, left,top,right,bottom))    
+    
+                if skew_index >= 0:
+                    z = ocr_utils.shear(t1[i], skewRange[skew_index]) 
+                else:
+                    z=t1[i]
+                im_clip = Image.fromarray(256.0-z*256.0)  
+                img2.paste(im_clip, box= (left , top))
+                img3.paste(im_clip, box= (left , top))           
+                draw.rectangle((left,top,right+2*white_space,bottom+2*white_space), outline=0)
+            except:
+                print (left,right,top,bottom,df.columns)
+        return bottom
+            
+    im = Image.open(input_image_file_jpg)     
+         
+  
+    bottom = 0             
+    if len(secret_message)==0:
+        img2 = Image.new('L',(im.width,im.height*3),color=255)  
+        img3 = Image.new('L',(im.width,im.height*3),color=255) 
+        draw = ImageDraw.Draw(img3)
+        for skew_index in range(len(skewRange)):
+            skew_indices = []                    
+            for i in range(len(t1)):
+                skew_indices.append(skew_index)
+            bottom = draw_encoded_images(skew_indices, offset=bottom+16)             
+    else:
+        img2 = Image.new('L',(im.width,im.height),color=255)        
+        img3 = Image.new('L',(im.width,im.height),color=255)        
+        draw = ImageDraw.Draw(img3)        
+        gen= convert_to_shear(a)      
+        skew_indices = []       
+        for i in range(len(t1)):
+            skew_indices.append(next(gen))  
+        draw_encoded_images(skew_indices, offset=0)
+        gen.close()
+        
+    img2.save(ouput_encoded_file)   
+    img3.save(output_box_file)   
+    
+    return output_base,skew_indices
+
+
+######################################################################################
+# us the original document as the source of characters to shear and train
+######################################################################################
+
+base_file= '15-01-01 459_Mont_Lyman'
+next_base = '/tmp/plots/'+base_file+'_training'
+
+# shear the characters
+base_file,skew_indices = encode_and_save_file(base_file, next_base , character_size, white_space)
+
+# use tesseract to make the boxes around each skewed character.
+df,t1  = ocr_utils.file_to_df(base_file,  character_size,title='Characters to Train',white_space=white_space,input_filters_dict=input_filters_dict)
+
+ds = ocr_utils.read_df(df,input_filters_dict = input_filters_dict, 
+                            output_feature_list=output_feature_list,
+                            test_size = 0,
+                            engine_type='tensorflow',
+                            dtype=dtype) 
+
+
+
+X_train = ds.train.features[1]
+# the characters were written once for each entry in skewrange
+# fill in the y_train with the skew_index
+y_train = np.zeros(len(X_train), dtype=np.int32)
+for i in range(len(X_train)):
+    y_train[i] = i / (len(X_train)/len(skewRange))
+                   
+print (y_train)
+print (y_train.shape)
+print (X_train.shape)
+
+######################################################################################
+# train the characters. The resultant logistic regression is the key to decoded
+######################################################################################
+
+n_components = 2
+lda = LDA(n_components=n_components)
+
+X_train_lda = lda.fit_transform(X_train, y_train)
+
+print('\nLDA components = {}'.format(lda.n_components))
+lr = LogisticRegression()
+logistic_fitted = lr.fit(X_train_lda, y_train)
+
+y_train_pred = logistic_fitted.predict(X_train_lda)
+
+print('\nLDA Train Accuracy: {:4.6f}, n_components={} coefficients={}'.format(accuracy_score(y_train, y_train_pred),lda.n_components,lr.coef_.shape))
+# print('LDA Test Accuracy: {:4.6f}, n_components={} coefficients={}'.format(accuracy_score(y_test, y_test_pred),lda.n_components,lr.coef_.shape))
+
+X_errors_image = X_train[y_train!=y_train_pred]
+
+X_errors2D=np.reshape(X_errors_image, (X_errors_image.shape[0], character_size, character_size))
+ocr_utils.montage(X_errors2D,title='LDA Error Images, components={}'.format (n_components))
+
+#  X_combined = np.vstack((X_train_lda, X_test_lda))
+    #  y_combined = np.hstack((y_train, y_test))
+if X_train_lda.shape[1] > 1:
+    ocr_utils.plot_decision_regions(
+        X=X_train_lda,                                        
+        y=y_train,                                        
+        classifier=lr,  
+        labels = ['LDA1','LDA2']  ,     
+        title='logistic_regression after 2 component LDA')
+    
+######################################################################################
+# now that the font is trained, pick up some text and encode a message
+######################################################################################
+
+base_file = '15-01-01 459_Mont_Lyman'
+output_base = '/tmp/plots/15-01-01 459_Mont_Lyman_encrypted'
+base_file,skew_indices = encode_and_save_file(base_file, output_base, character_size, white_space, secret_message='your first born is mine')  
+print ('base file to decode = {}'.format(base_file))   
+
+
+df,t1  = ocr_utils.file_to_df(base_file, character_size, title = 'Encrypted File',white_space=white_space,input_filters_dict=input_filters_dict)
+
+ds = ocr_utils.read_df(df,input_filters_dict = input_filters_dict, 
+                            output_feature_list=output_feature_list,
+                            test_size = 0,
+                            engine_type='tensorflow',
+                            dtype=dtype)
+
+print ('document length in chars={}'.format(len(t1)))
+X_train = ds.train.features[1]
+X_train_lda = lda.transform(X_train)    
+results = logistic_fitted.predict(X_train_lda)  
+correct_characters=[]
+incorrect_characters=[]
+error_characters=[]
+decoded_message = ''
+dc = 0
+
+for i,x in enumerate(df['m_label']):
+    try:
+ 
+        if skew_indices[i] >=0:
+            dc = dc * 4 + skew_indices[i]
+            if (i+1) % 4 == 0:
+                decoded_message = decoded_message + chr(dc)
+                dc = 0
+                
+            
+            print('index={}, original character={}, result= {}, skew={}'.format(i, chr(int(x)),results[i], skew_indices[i])   )             
+            if results[i] == skew_indices[i]:
+                correct_characters.append(chr(int(x)))
+            else:
+                incorrect_characters.append(chr(int(x)))
+                error_characters.append(X_train[i])
+    except:
+        print ('.',end='')
+error_characters = np.array(error_characters)
+
+error_characters=np.reshape(error_characters, (error_characters.shape[0], character_size, character_size))
+ocr_utils.montage(error_characters,title='LDA Encrption Errors, components={}'.format (n_components))
+print ('\ncorrect characters={}'.format(correct_characters))       
+print ('incorrect characters={}'.format(incorrect_characters))
+print ("decoded message={}".format(decoded_message))
+     
+######################################################################################
+# decode the message
+######################################################################################
+
+print ('\n########################### No Errors ####################################')
diff --git a/o3_top_secret_python_box.py b/o3_top_secret_python_box.py
new file mode 100644
index 0000000..fec2a36
--- /dev/null
+++ b/o3_top_secret_python_box.py
@@ -0,0 +1,266 @@
+'''
+Created on Oct 23, 2016
+
+Created on Jul 12, 2016
+This program shows how Principal Component Analysis removes affine 
+transformation distortions.
+
+Parallel lines in an image remain parallel after an affine transformation. 
+For instance, if an image is rotated or sheared, lines remain parallel.
+
+PCA and LDA can remove affine transformations.  This is shown by making 3 shapes
+and then making a number of shear versions of the shapes.  Running 
+Principal Component Analysis reduces the number of features necessary to
+recognize the features during Logistic Regression with 100% accuracy, 
+down to 2 from 400 (20 columns by 20 rows).
+
+We make three images and then make about 80 copies of each image created by
+shearing the original image.
+
+Since there is very little noise introduced by the shearing, almost all of
+the explained variance is due to the shearing. PCA finds eigenvectors
+that line up with shearing.
+
+1) For a couple of shapes, make sheared version.
+2) train and print accuracies without PCA 
+3) repeat, but use PCA first before training.
+4) observe the improvement
+
+Do the same thing for Linear Discriminant Analysis
+encode a secret message in the angle of rotation of characters
+
+Train a neural network on rotated versions of characters with the output of 
+the network being the angle of rotation.  
+
+Thus, given a rotated character, the neural network will yield a value 
+that is the amount of rotation of the character.
+
+Encode a test set by applying a secret message with one bit for each character.
+Decode the secret message by running the rotated characters through the 
+neural network, yielding the pattern of bits.
+
+ pick up the base character
+ 
+ make a training set by rotating them through n angles
+ 
+ train
+ 
+ pick up the base characters
+ encode the secret message n bits at a time into the characters
+ this is the testing set
+ 
+ test secret message yielding a vector of rotations
+ 
+ convert the rotation back into bits
+ 
+ assemble the bits into the secret message.
+
+
+@author: richard lyman
+
+
+'''# ==============================================================================
+
+import ocr_utils
+
+
+import numpy as np
+from PIL import Image, ImageDraw
+import io
+from sklearn.metrics import accuracy_score
+from sklearn.decomposition import PCA
+from sklearn.metrics import accuracy_score
+from sklearn.lda import LDA
+from sklearn.linear_model import LogisticRegression
+from sklearn.cross_validation import train_test_split
+
+# input_filters_dict = {'m_label': list(range(48,58))+list(range(65,91))}  
+# output_feature_list  = ['orientation_one_hot','image']   
+dtype = np.float32
+character_size = 100
+white_space = 10       
+skewRange = np.linspace(-0.2,0.2,4)
+
+class c_box(object):
+    def __init__(self, top, left, right, bottom):
+        self._top = top
+        self._left = left
+        self._right = right
+        self._bottom = bottom
+
+
+def find_min_max(sums):
+    case = 0 
+    mins = []
+    maxes = []
+    for i,sum in enumerate(sums):   
+        '''
+            case 0, going through area between characters
+                if sum ==0 stay in case 0
+                if sum != 0 set the top to i and switch to case 1
+            case 1, going through a character
+                if sum ==0 set the bottom to i and drop to case 0
+                    also append the box to the list using 
+                    left = 0, and right = the width of the image                
+                if sum !=0 then continue in case 1
+        '''
+      
+        if case==0 :
+            if sum != 0 :
+                case = 1
+                min= i
+        else:
+            if sum == 0    :             
+                case = 0
+                max= i
+                mins.append(min)
+                maxes.append(max)
+    return mins, maxes
+          
+
+
+# pick up the base characters from training_image_file
+# produce some skeared versions 
+# make into a training set
+# place in a ocr_utils TruthedCharacters class so we can use the
+# one hot and batch functions 
+
+im = Image.open('15-01-01 459_Mont_Lyman.png')
+#im = Image.open('CourierFont.png')
+im = im.convert(mode='L')  
+data = 255-np.asarray( im, dtype="int32" )
+sums = np.sum(data,axis=1)
+mins, maxes = find_min_max(sums)
+boxes = []
+for top,bottom in zip(mins,maxes):
+    line = data[top:bottom]
+    line_sums = np.sum(line,axis=0)
+    lefts,rights = find_min_max(line_sums)
+    for left,right in zip(lefts,rights):
+        boxes.append(c_box(top,left,right,bottom))
+    
+images=[]
+orientation=[]
+recognized_label =[]
+for box in boxes:
+      
+    img2 = Image.new('L',(character_size,character_size),color=255)
+    
+    img =  im.crop(box=(box._left, box._top, box._right, box._bottom))     
+    img2.paste(img,box=(white_space,white_space))    
+    
+    imgByteArr = img2.tobytes()
+    lst = list(imgByteArr)
+    image = np.array(lst)/255.0 
+    image = 1.0 - image        
+    images.append(image)
+
+height = im.height
+width = im.width
+
+t1 = np.array(images)
+t1=np.reshape(t1,(t1.shape[0],character_size,character_size))
+ocr_utils.montage(t1, title='characters from file')
+
+shp = t1.shape
+totalN = len(skewRange)*shp[0]
+images = []
+import skimage.transform as af  
+
+for j in range(shp[0]):
+    for i,skew in enumerate(skewRange):       
+        images.append(ocr_utils.shear(t1[j],skew))       
+        orientation.append(skew)
+
+images=np.array(images)
+ocr_utils.montage(images, title='characters being trained')
+images=np.reshape(images,(len(images),character_size*character_size))
+ys = ocr_utils.convert_to_unique(orientation)
+
+
+X_train , X_test, y_train, y_test = train_test_split(images, ys, test_size=0.3, random_state=0)
+print (y_test.shape)
+
+lr = LogisticRegression()
+lr.fit(X_train, y_train)
+y_train_pred = lr.predict(X_train)
+y_test_pred = lr.predict(X_test)
+
+print('\nTrain Accuracy: {:4.6f} coefficients={}'.format(accuracy_score(y_train, y_train_pred), lr.coef_.shape))
+print('Test Accuracy: {:4.6f} coefficients={}'.format(accuracy_score(y_test, y_test_pred), lr.coef_.shape))
+
+#########################################################################
+# run Principal Component analysis first, then Logistic Regression
+
+n_components = 2
+pca = PCA(n_components=n_components)
+
+X_train_pca = pca.fit_transform(X_train)
+X_test_pca = pca.transform(X_test)
+
+print('\nPCA components = {}'.format(pca.components_.shape))
+
+lr = LogisticRegression()
+logistic_fitted = lr.fit(X_train_pca, y_train)
+
+y_train_pred = logistic_fitted.predict(X_train_pca)
+y_test_pred = logistic_fitted.predict(X_test_pca)
+
+print('\nPCA Train Accuracy: {:4.6f}, n_components={} coefficients={}'.format(accuracy_score(y_train, y_train_pred),pca.n_components,lr.coef_.shape))
+print('PCA Test Accuracy: {:4.6f}, n_components={} coefficients={}'.format(accuracy_score(y_test, y_test_pred),pca.n_components,lr.coef_.shape))
+
+X_errors_image = X_test[y_test!=y_test_pred]
+y_errors = y_test[y_test!=y_test_pred]
+X_errors_pca = X_test_pca[y_test!=y_test_pred]
+
+# change to a 2D shape 
+X_errors2D=np.reshape(X_errors_image, (X_errors_image.shape[0], character_size, character_size))
+ocr_utils.montage(X_errors2D,title='PCA Error Images, components={}'.format (n_components))
+
+X_combined = np.vstack((X_train_pca, X_test_pca))
+y_combined = np.hstack((y_train, y_test))
+
+ocr_utils.plot_decision_regions(
+                                         X=X_combined,                                        
+                                         y=y_combined,                                        
+                                         classifier=lr,  
+                                         labels = ['PC1','PC2']  ,     
+                                         title='logistic_regression after 2 component PCA')
+
+
+#########################################################################
+# run Linear Discriminant Analysis first then Logistic Regression
+
+
+n_components = 2
+lda = LDA(n_components=n_components)
+
+X_train_lda = lda.fit_transform(X_train, y_train)
+X_test_lda = lda.transform(X_test)
+
+print('\nLDA components = {}'.format(pca.components_.shape))
+lr = LogisticRegression()
+logistic_fitted = lr.fit(X_train_lda, y_train)
+
+y_train_pred = logistic_fitted.predict(X_train_lda)
+y_test_pred = logistic_fitted.predict(X_test_lda)
+
+print('\nLDA Train Accuracy: {:4.6f}, n_components={} coefficients={}'.format(accuracy_score(y_train, y_train_pred),lda.n_components,lr.coef_.shape))
+print('LDA Test Accuracy: {:4.6f}, n_components={} coefficients={}'.format(accuracy_score(y_test, y_test_pred),lda.n_components,lr.coef_.shape))
+
+X_errors_image = X_test[y_test!=y_test_pred]
+
+# change to a 2D shape 
+X_errors2D=np.reshape(X_errors_image, (X_errors_image.shape[0], character_size, character_size))
+ocr_utils.montage(X_errors2D,title='LDA Error Images, components={}'.format (n_components))
+
+X_combined = np.vstack((X_train_lda, X_test_lda))
+y_combined = np.hstack((y_train, y_test))
+if X_combined.shape[1] > 1:
+    ocr_utils.plot_decision_regions(
+                                             X=X_combined,                                        
+                                             y=y_combined,                                        
+                                             classifier=lr,  
+                                             labels = ['LDA1','LDA2']  ,     
+                                             title='logistic_regression after 2 component LDA')
+print ('\n########################### No Errors ####################################')

From 2fc9455c3c0405fabc47a691dec0d33da709cf5b Mon Sep 17 00:00:00 2001
From: richard lyman <richard.r.lyman@gmail.com>
Date: Wed, 25 Oct 2017 15:55:43 -0700
Subject: [PATCH 3/6] Adjust for library updates

---
 n0_network.py                        |   4 +-
 n1_2cnv1fc.py                        |  15 ++--
 n1_2cnv2fc.py                        |  10 +--
 n1_baseTensorNN.py                   |  14 ++--
 n1_image_to_image.py                 |  50 +++++++++---
 n1_residual3x4.py                    |   8 +-
 o3_top_secret_python_box.py          |   5 +-
 o4_image_to_image.py                 |   4 +-
 p115_l1_l2_regularization.py         |   2 +-
 p119_squential_backward_selection.py |   1 +
 p141_linear_descriminant_analsys.py  |   6 +-
 p177_k_fold_cross_validation.py      |   2 +-
 p356_neural_net.py                   |   4 +-
 p62_logistic_regression.py           |   4 +-
 q3_removing_affine_distortion.py     |   2 +-
 q6_tensorflow_residual3x4.py         | 109 +++++++++++++++++++++++++++
 q9_tensorflow_gpu_test.py            |   6 ++
 17 files changed, 199 insertions(+), 47 deletions(-)
 create mode 100644 q6_tensorflow_residual3x4.py

diff --git a/n0_network.py b/n0_network.py
index 29e140b..f3aa381 100644
--- a/n0_network.py
+++ b/n0_network.py
@@ -30,7 +30,7 @@ def fit(self, truthed_data, nEpochs=5000):
                 summary_str = result[0]
  
                 self._writer.add_summary(summary_str, i)
-                train_accuracy = self._accuracy.eval(feed)    
+                train_accuracy = result[1]   
                 if train_accuracy <= (1.0 - 1e-5  ):
                     perfect_count=10;
                 else:
@@ -39,7 +39,7 @@ def fit(self, truthed_data, nEpochs=5000):
                         break;  
                     
                 print ("step %d, training accuracy %g"%(i, train_accuracy),flush=True)
-            self._train_step.run(feed_dict=feed)
+            self._sess.run(self._train_step,feed_dict=feed)
             
 
     
diff --git a/n1_2cnv1fc.py b/n1_2cnv1fc.py
index 117fa8d..2b59ac2 100644
--- a/n1_2cnv1fc.py
+++ b/n1_2cnv1fc.py
@@ -105,7 +105,7 @@ def max_pool_2x2(x):
         with tf.name_scope("reshape_x_image") as scope:
             self._x_image = tf.reshape(self._ph.image, [-1,self._nCols,self._nRows,1])
         
-        image_summ = tf.image_summary("x_image", self._x_image)
+        image_summ = tf.summary.image("x_image", self._x_image)
         
         """# ==============================================================================
         
@@ -156,7 +156,9 @@ def max_pool_2x2(x):
             
             # append the features, the 2nd on, that go directly to the fully connected layer
             for i in range(2,truthed_features.num_features ):
-                h_pool2_flat = tf.concat(1, [h_pool2_flat, self._ph[i]])  
+                print(i)
+                print(self._ph[i])
+                h_pool2_flat = tf.concat([h_pool2_flat, self._ph[i]],1)  
             h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
         
         """# ==============================================================================
@@ -184,7 +186,7 @@ def max_pool_2x2(x):
         
             # 1e-8 added to eliminate the crash of training when taking log of 0
             cross_entropy = -tf.reduce_sum(self._ph[0]*tf.log(y_conv+ 1e-8  ))
-            ce_summ = tf.scalar_summary("cross entropy", cross_entropy)
+            ce_summ = tf.summary.scalar("cross entropy", cross_entropy)
             
         with tf.name_scope("train") as scope:
             self._train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
@@ -194,7 +196,8 @@ def max_pool_2x2(x):
             self._prediction = tf.argmax(y_conv,1)
         
             self._accuracy = tf.reduce_mean(tf.cast(self._correct_prediction, dtype))
-            accuracy_summary = tf.scalar_summary("accuracy", self._accuracy)    
+            accuracy_summary = tf.summary.scalar("accuracy", self._accuracy)    
+            weight_summary = tf.summary.histogram("weights", W_fc2)
         """# ==============================================================================
         
         Start TensorFlow Interactive Session
@@ -202,13 +205,13 @@ def max_pool_2x2(x):
         """# ==============================================================================   
        
         self._sess.run(tf.initialize_all_variables())  
-        self._merged = tf.merge_all_summaries()
+        self._merged = tf.summary.merge_all()
         tm = ""
         tp = datetime.datetime.now().timetuple()
         for i in range(4):
             tm += str(tp[i])+'-'
         tm += str(tp[4])    
-        self._writer = tf.train.SummaryWriter("/tmp/ds_logs/"+ tm, self._sess.graph)
+        self._writer = tf.summary.FileWriter("/tmp/ds_logs/"+ tm, self._sess.graph)
         
         def computeSize(s,tens):
             sumC = 1
diff --git a/n1_2cnv2fc.py b/n1_2cnv2fc.py
index 831b904..8865bb4 100644
--- a/n1_2cnv2fc.py
+++ b/n1_2cnv2fc.py
@@ -107,7 +107,7 @@ def max_pool_2x2(x):
         with tf.name_scope("reshape_x_image") as scope:
             self._x_image = tf.reshape(self._ph.image, [-1,self._nCols,self._nRows,1])
         
-        image_summ = tf.image_summary("x_image", self._x_image)
+        image_summ = tf.summary.image("x_image", self._x_image)
         
         """# ==============================================================================
         
@@ -219,7 +219,7 @@ def max_pool_2x2(x):
         
             # 1e-8 added to eliminate the crash of training when taking log of 0
             cross_entropy = -tf.reduce_sum(self._ph[0]*tf.log(y_conv+ 1e-8  ))
-            ce_summ = tf.scalar_summary("cross entropy", cross_entropy)
+            ce_summ = tf.summary.scalar("cross entropy", cross_entropy)
             
         with tf.name_scope("train") as scope:
             self._train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
@@ -229,7 +229,7 @@ def max_pool_2x2(x):
             self._prediction = tf.argmax(y_conv,1)
         
             self._accuracy = tf.reduce_mean(tf.cast(self._correct_prediction, dtype))
-            accuracy_summary = tf.scalar_summary("accuracy", self._accuracy)    
+            accuracy_summary = tf.summary.scalar("accuracy", self._accuracy)    
         """# ==============================================================================
         
         Start TensorFlow Interactive Session
@@ -237,13 +237,13 @@ def max_pool_2x2(x):
         """# ==============================================================================      
     
         self._sess.run(tf.initialize_all_variables())  
-        self._merged = tf.merge_all_summaries()
+        self._merged = tf.summary.merge_all()
         tm = ""
         tp = datetime.datetime.now().timetuple()
         for i in range(4):
             tm += str(tp[i])+'-'
         tm += str(tp[4])    
-        self._writer = tf.train.SummaryWriter("/tmp/ds_logs/"+ tm, self._sess.graph)
+        self._writer = tf.summary.FileWriter("/tmp/ds_logs/"+ tm, self._sess.graph)
         
         def computeSize(s,tens):
             sumC = 1
diff --git a/n1_baseTensorNN.py b/n1_baseTensorNN.py
index 62353db..7870c5e 100644
--- a/n1_baseTensorNN.py
+++ b/n1_baseTensorNN.py
@@ -141,11 +141,11 @@ def max_pool_2x2(x):
         with tf.name_scope("xent") as scope:
         
             # 1e-8 added to eliminate the crash of training when taking log of 0
-            cross_entropy = -tf.reduce_sum(self._ph[0]*tf.log(y_conv+ 1e-8  ))
-            ce_summ = tf.scalar_summary("cross entropy", cross_entropy)
+            self._cross_entropy = -tf.reduce_sum(self._ph[0]*tf.log(y_conv+ 1e-8  ))
+            ce_summ = tf.scalar_summary("cross entropy", self._cross_entropy)
             
         with tf.name_scope("train") as scope:
-            self._train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
+            self._train_step = tf.train.AdamOptimizer(1e-4).minimize(self._cross_entropy)
     
         with tf.name_scope("test") as scope:        
             self._correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(self._ph[0],1))
@@ -155,10 +155,10 @@ def max_pool_2x2(x):
             accuracy_summary = tf.scalar_summary("accuracy", self._accuracy)    
         """# ==============================================================================
         
-        Start TensorFlow Interactive Session
+        Start TensorFlow Session
         
         """# ==============================================================================          
-        self._sess = tf.InteractiveSession()          
+        self._sess = tf.Session()          
         self._sess.run(tf.initialize_all_variables())  
         self._merged = tf.merge_all_summaries()
         tm = ""
@@ -215,7 +215,7 @@ def fit(self, truthed_data, nEpochs=5000):
                 summary_str = result[0]
                 #acc = result[1]       
                 self._writer.add_summary(summary_str, i)
-                train_accuracy = self._accuracy.eval(feed)    
+                train_accuracy = result[1]  
                 if train_accuracy <= (1.0 - 1e-5  ):
                     perfect_count=10;
                 else:
@@ -224,7 +224,7 @@ def fit(self, truthed_data, nEpochs=5000):
                         break;  
                     
                 print ("step %d, training accuracy %g"%(i, train_accuracy),flush=True)
-            self._train_step.run(feed_dict=feed)
+            self._sess.run(self._train_step.run,feed_dict=feed)
             
 
     
diff --git a/n1_image_to_image.py b/n1_image_to_image.py
index 6ba2b08..e6b86c4 100644
--- a/n1_image_to_image.py
+++ b/n1_image_to_image.py
@@ -107,7 +107,7 @@ def max_pool_2x2(x):
         with tf.name_scope("reshape_x_image") as scope:
             self._x_image = tf.reshape(self._ph.image, [-1,self._nCols,self._nRows,1])
         
-        image_summ = tf.image_summary("x_image", self._x_image)
+        image_summ = tf.summary.image("x_image", self._x_image)
         
         """# ==============================================================================
         
@@ -218,18 +218,18 @@ def max_pool_2x2(x):
         with tf.name_scope("xent") as scope:
         
             # 1e-8 added to eliminate the crash of training when taking log of 0
-            cross_entropy = -tf.reduce_sum(self._ph[0]*tf.log(y_conv+ 1e-8  ))
+            self._cross_entropy = -tf.reduce_sum(self._ph[0]*tf.log(y_conv+ 1e-8  ))
             #cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
              #   logits, labels, name='xentropy')            
-            ce_summ = tf.scalar_summary("cross entropy", cross_entropy)
+            ce_summ = tf.summary.scalar("cross entropy", self._cross_entropy)
             
         with tf.name_scope("reshape_x_image2") as scope:
             self._x_image2 = tf.reshape(self._ph[0], [-1,int(self._nCols/2),int(self._nRows/2),1])
         
-        image_summ2 = tf.image_summary("x_image2", self._x_image2)            
+        image_summ2 = tf.summary.image("x_image2", self._x_image2)            
             
         with tf.name_scope("train") as scope:
-            self._train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
+            self._train_step = tf.train.AdamOptimizer(1e-4).minimize(self._cross_entropy)
             #self._train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)            
     
         with tf.name_scope("test") as scope:        
@@ -237,21 +237,21 @@ def max_pool_2x2(x):
             self._prediction = tf.argmax(y_conv,1)
         
             self._accuracy = tf.reduce_mean(tf.cast(self._correct_prediction, dtype))
-            accuracy_summary = tf.scalar_summary("accuracy", self._accuracy)    
+            accuracy_summary = tf.summary.scalar("accuracy", self._accuracy)    
         """# ==============================================================================
         
-        Start TensorFlow Interactive Session
+        Start TensorFlow Session
         
         """# ==============================================================================      
     
         self._sess.run(tf.initialize_all_variables())  
-        self._merged = tf.merge_all_summaries()
+        self._merged = tf.summary.merge_all()
         tm = ""
         tp = datetime.datetime.now().timetuple()
         for i in range(4):
             tm += str(tp[i])+'-'
         tm += str(tp[4])    
-        self._writer = tf.train.SummaryWriter("/tmp/ds_logs/"+ tm, self._sess.graph)
+        self._writer = tf.summary.FileWriter("/tmp/ds_logs/"+ tm, self._sess.graph)
         
         def computeSize(s,tens):
             sumC = 1
@@ -319,4 +319,36 @@ def test2(self, truthed_data,  title = ''):
                 ocr_utils.montage(output_images,title='TensorFlow Output Images')         
                 ocr_utils.montage(input_images,title='TensorFlow Input Images')        
      
+    def fit_entropy(self, truthed_data, nEpochs=5000):    
+
+        perfect_count=10
+        for i in range(nEpochs):
+        
+            batch = truthed_data.next_batch(100)
+            # assign feature data to each placeholder
+            # the batch list is returned in the same order as the features requested
+            feed = {self._keep_prob: 0.5}
+            for j in range(truthed_data.num_features):
+                feed[self._ph[j]] = batch[j]  
+                
+            if i%100 == 0:
+
+                feed[self._keep_prob] = 1.0
+                result = self._sess.run([self._merged, self._cross_entropy ], feed_dict=feed)    
+                summary_str = result[0]
+ 
+                self._writer.add_summary(summary_str, i)
+                train_entropy = result[1]  
+                if train_entropy >= (2000 ):
+                    perfect_count=10;
+                else:
+                    perfect_count -= 1
+                    if perfect_count==0:
+                        break;   
+                    
+                print ("step %d, training entropy %g"%(i, train_entropy),flush=True)
+            self._sess.run(self._train_step,feed_dict=feed)
+            
+
+    
                   
\ No newline at end of file
diff --git a/n1_residual3x4.py b/n1_residual3x4.py
index b274714..12569f3 100644
--- a/n1_residual3x4.py
+++ b/n1_residual3x4.py
@@ -169,7 +169,7 @@ def computeSize(s,tens):
         with tf.name_scope("xent") as scope:
             # 1e-8 added to eliminate the crash of training when taking log of 0
             cross_entropy = -tf.reduce_sum(self._ph[0]*tf.log(y_conv+1e-8))
-            ce_summ = tf.scalar_summary("cross entropy", cross_entropy)
+            ce_summ = tf.summary.scalar("cross entropy", cross_entropy)
                  
         with tf.name_scope("train") as scope:
             self._train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
@@ -179,7 +179,7 @@ def computeSize(s,tens):
             self._prediction = tf.argmax(y_conv,1)            
          
             self._accuracy = tf.reduce_mean(tf.cast(self._correct_prediction, dtype))
-            accuracy_summary = tf.scalar_summary("accuracy", self._accuracy)    
+            accuracy_summary = tf.summary.scalar("accuracy", self._accuracy)    
 
         """# ==============================================================================
         
@@ -188,7 +188,7 @@ def computeSize(s,tens):
         """# ==============================================================================          
         
         self._sess.run(tf.initialize_all_variables())  
-        self._merged = tf.merge_all_summaries()
+        self._merged = tf.summary.merge_all()
         tm = ""
         tp = datetime.datetime.now().timetuple()
         for i in range(4):
@@ -200,7 +200,7 @@ def computeSize(s,tens):
         # tensorboard --logdir '/tmp/ds_logs/'
         # See results on localhost:6006 
         
-        self._writer = tf.train.SummaryWriter("/tmp/ds_logs/"+ tm, self._sess.graph)
+        self._writer = tf.summary.FileWriter("/tmp/ds_logs/"+ tm, self._sess.graph)
         
     def computeSize(s,tens):
         sumC = 1
diff --git a/o3_top_secret_python_box.py b/o3_top_secret_python_box.py
index fec2a36..bffc071 100644
--- a/o3_top_secret_python_box.py
+++ b/o3_top_secret_python_box.py
@@ -70,7 +70,8 @@
 from sklearn.metrics import accuracy_score
 from sklearn.decomposition import PCA
 from sklearn.metrics import accuracy_score
-from sklearn.lda import LDA
+#from sklearn.model_selection
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
 from sklearn.linear_model import LogisticRegression
 from sklearn.cross_validation import train_test_split
 
@@ -233,7 +234,7 @@ def find_min_max(sums):
 
 
 n_components = 2
-lda = LDA(n_components=n_components)
+lda = LinearDiscriminantAnalysis(n_components=n_components)
 
 X_train_lda = lda.fit_transform(X_train, y_train)
 X_test_lda = lda.transform(X_test)
diff --git a/o4_image_to_image.py b/o4_image_to_image.py
index e438f7d..d54a19e 100644
--- a/o4_image_to_image.py
+++ b/o4_image_to_image.py
@@ -68,7 +68,7 @@
     #output_feature_list = ['font_one_hot','image','italic','aspect_ratio','upper_case']   
 
     # train the digits 0-9 for all fonts
-    input_filters_dict = {'m_label': range(48,58),'italic':0,'strength':.4}
+    input_filters_dict = {'m_label': [43]+list(range(48,58)),'italic':0,'strength':.4}
     #input_filters_dict = {'font':'BANKGOTHIC','m_label': list(range(48,58)),'italic':0,'strength':.7}    
     #input_filters_dict = {}    
     output_feature_list = ['low_pass_image','image']    
@@ -83,7 +83,7 @@
                                 test_size = .2,
                                 engine_type='tensorflow',dtype=dtype)    
     nn = nnetwork.network(ds.train)
-    nn.fit( ds.train,  nEpochs=5000)  
+    nn.fit_entropy( ds.train,  nEpochs=5000)  
     nn.test2(ds.test)
       
 #     train_a_font(input_filters_dict,  output_feature_list, nEpochs = 50000)    
diff --git a/p115_l1_l2_regularization.py b/p115_l1_l2_regularization.py
index c685ecd..ff90e48 100644
--- a/p115_l1_l2_regularization.py
+++ b/p115_l1_l2_regularization.py
@@ -100,7 +100,7 @@
 
 def weight_graph(regularization = 'l1'):
     weights, params = [], []
-    for c in np.arange(-4, 6):
+    for c in np.arange(0, 6):
         lr = LogisticRegression(penalty=regularization, C=10**c, random_state=0)
         lr.fit(X_train_std, y_train)
         weights.append(lr.coef_[1])
diff --git a/p119_squential_backward_selection.py b/p119_squential_backward_selection.py
index 3043008..2143d7c 100644
--- a/p119_squential_backward_selection.py
+++ b/p119_squential_backward_selection.py
@@ -80,6 +80,7 @@ def __init__(self, estimator, k_features,
         self.k_features = k_features
         self.test_size = test_size
         self.random_state = random_state
+        
     def fit(self, X, y):
         X_train, X_test, y_train, y_test = \
         train_test_split(X, y, test_size=self.test_size,
diff --git a/p141_linear_descriminant_analsys.py b/p141_linear_descriminant_analsys.py
index a43faf6..3e87962 100644
--- a/p141_linear_descriminant_analsys.py
+++ b/p141_linear_descriminant_analsys.py
@@ -57,8 +57,8 @@
 import numpy as np
 import ocr_utils
 import matplotlib.pyplot as plt
-from sklearn.lda import LDA
-        
+#from sklearn.lda import LDA
+from sklearn.discriminant_analysis import  LinearDiscriminantAnalysis as LDA   
 print_limit = 20
 chars_to_train = range(48,58)
 columnsXY=range(0,20)
@@ -115,7 +115,7 @@
 print('Within-class scatter matrix: {}x{}'.format(S_W.shape[0], S_W.shape[1]))
 
 print('Class label distribution: %s'
-    % np.bincount(np.array(y_train,dtype='int32'))[min(y_train):])
+    % np.bincount(np.array(y_train,dtype='int32'))[int(min(y_train)):])
 
 d = S_W.shape[1] # number of features
 S_W = np.zeros((d, d))
diff --git a/p177_k_fold_cross_validation.py b/p177_k_fold_cross_validation.py
index ca4eba0..c6bf4b5 100644
--- a/p177_k_fold_cross_validation.py
+++ b/p177_k_fold_cross_validation.py
@@ -49,7 +49,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from sklearn.cross_validation import StratifiedKFold
-from sklearn.lda import LDA
+from sklearn.discriminant_analysis import  LinearDiscriminantAnalysis as LDA  
 
 if __name__ == '__main__':
     #charsToTrain=range(48,58)
diff --git a/p356_neural_net.py b/p356_neural_net.py
index b034f82..b503870 100644
--- a/p356_neural_net.py
+++ b/p356_neural_net.py
@@ -178,7 +178,7 @@ def _encode_labels(self, y, k):
         """
         onehot = np.zeros((k, y.shape[0]))
         for idx, val in enumerate(y):
-            onehot[val, idx] = 1.0
+            onehot[int(val), idx] = 1.0
         return onehot
 
     def _initialize_weights(self):
@@ -587,7 +587,7 @@ def _encode_labels(self, y, k):
         """
         onehot = np.zeros((k, y.shape[0]))
         for idx, val in enumerate(y):
-            onehot[val, idx] = 1.0
+            onehot[int(val), idx] = 1.0
         return onehot
 
     def _initialize_weights(self):
diff --git a/p62_logistic_regression.py b/p62_logistic_regression.py
index b105ba6..e92e491 100644
--- a/p62_logistic_regression.py
+++ b/p62_logistic_regression.py
@@ -122,7 +122,7 @@ def cost_0(z):
 
 
 weights, params = [], []
-for c in np.arange(-5, 5):
+for c in np.arange(0, 5):
     lr = LogisticRegression(C=10**c, random_state=0)
     lr.fit(X_train_std, y_train)
     weights.append(lr.coef_[0])
@@ -131,7 +131,7 @@ def cost_0(z):
 
 title = 'regression_path'
 weights, params = [], []
-for c in np.arange(-5, 5):
+for c in np.arange(0, 5):
     lr = LogisticRegression(C=10**c, random_state=0)
     lr.fit(X_train_std, y_train)
     weights.append(lr.coef_[1])
diff --git a/q3_removing_affine_distortion.py b/q3_removing_affine_distortion.py
index e46baab..85d5f37 100644
--- a/q3_removing_affine_distortion.py
+++ b/q3_removing_affine_distortion.py
@@ -146,7 +146,7 @@ def shear(X, skew):
 #########################################################################
 # run Linear Discriminant Analysis first then Logistic Regression
 
-from sklearn.lda import LDA
+from sklearn.discriminant_analysis import  LinearDiscriminantAnalysis as LDA
 n_components = 2
 lda = LDA(n_components=n_components)
 
diff --git a/q6_tensorflow_residual3x4.py b/q6_tensorflow_residual3x4.py
new file mode 100644
index 0000000..951ff47
--- /dev/null
+++ b/q6_tensorflow_residual3x4.py
@@ -0,0 +1,109 @@
+"""# ==========================================================================
+
+# Copyright 2015 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+This sample program is a modified version of the Google mnist convolutional 
+network tutorial example.  See the mnist tutorial in www.tensorflow.org 
+
+This graph has multiple sections  3 layers each, 400 100 400 followed
+by a fully connected layer.
+
+see tensor_flow_graph.png
+"""# ==============================================================================
+import ocr_utils
+import datetime
+from collections import namedtuple
+import numpy as np
+import pandas as pd
+import n1_residual3x4 as nnetwork      
+import tensorflow as tf  
+dtype = np.float32
+#with tf.device('/GPU:0'):
+#with tf.device('/cpu:0'): 
+       
+    
+if True:
+    # single font train
+    
+    # examples
+    # select only images from 'OCRB'  scanned font
+    # input_filters_dict = {'font': ('OCRA',)}
+    
+    # select only images from 'HANDPRINT'  font
+    #input_filters_dict = {'font': ('HANDPRINT',)}
+    
+    # select only images from 'OCRA' and 'OCRB' fonts with the 'scanned" fontVariant
+    # input_filters_dict = {'font': ('OCRA','OCRB'), 'fontVariant':('scanned',)}
+    
+    # select everything; all fonts , font variants, etc.
+    # input_filters_dict = {}
+    
+    # select the digits 0 through 9 in the E13B font
+    # input_filters_dict = {'m_label': range(48,58), 'font': 'E13B'}
+    
+    # select the digits 0 and 2in the E13B font
+    # input_filters_dict = {'m_label': (48,50), 'font': 'E13B'}
+    
+    # output the character label, image, italic flag, aspect_ratio and upper_case flag
+    # output_feature_list = ['m_label_one_hot','image','italic','aspect_ratio','upper_case']    
+    
+    # output only the character label and the image
+    # output_feature_list = ['m_label_one_hot','image'] 
+    
+    #   identify the font given the input images
+    #output_feature_list = ['font_one_hot','image','italic','aspect_ratio','upper_case']   
+
+    # train the digits 0-9 for all fonts
+    #input_filters_dict = {'m_label': range(48,58)}
+    input_filters_dict = {'font':'ARIAL','m_label': list(range(48,58))+list(range(65,91))+list(range(97,123))}    
+    #input_filters_dict = {}    
+    output_feature_list = ['m_label_one_hot','image']    
+ 
+    """# ==============================================================================
+    
+    Train and Evaluate the Model
+    
+    """# ==============================================================================
+    ds = ocr_utils.read_data(input_filters_dict = input_filters_dict, 
+                                output_feature_list=output_feature_list,
+                                test_size = .1,
+                                engine_type='tensorflow',dtype=dtype)    
+    nn = nnetwork.network(ds.train)
+    nn.fit( ds.train,  nEpochs=5000)  
+    nn.test(ds.test)
+      
+#     train_a_font(input_filters_dict,  output_feature_list, nEpochs = 50000)    
+    
+else:
+    # loop through all the fonts and train individually
+
+    # pick up the entire list of fonts and font variants. Train each one.
+    df1 = ocr_utils.get_list(input_filters_dict={'font': ()})      
+    
+    import pprint as pprint
+    pp = pprint.PrettyPrinter(indent=4)
+    pp.pprint(df1)
+   
+    output_feature_list = ['m_label_one_hot','image','italic','aspect_ratio','upper_case','font_one_hot']
+    
+    # Change nEpochs to 5000 for better results
+    for l in df1:
+        input_filters_dict= {'font': (l[0],)}       
+        train_a_font(input_filters_dict,output_feature_list, nEpochs = 500) 
+    
+    
+print ('\n########################### No Errors ####################################')
+
diff --git a/q9_tensorflow_gpu_test.py b/q9_tensorflow_gpu_test.py
index c2fbd2f..84a255b 100644
--- a/q9_tensorflow_gpu_test.py
+++ b/q9_tensorflow_gpu_test.py
@@ -1,11 +1,17 @@
 # Creates a graph.
 import tensorflow as tf
+
+
 a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
 b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
 c = tf.matmul(a, b)
 # Creates a session with log_device_placement set to True.
 sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
 # Runs the op.
+
+tens1 = tf.constant([ [[1,2],[2,3]], [[3,4],[5,6]] ]) 
+print (sess.run(tens1)[1,1,0])
+
 print (sess.run(c))
 
 print ('\n########################### No Errors ####################################')
\ No newline at end of file

From 1ebc7f59ae01897d3cb516e87d308a4ef0022299 Mon Sep 17 00:00:00 2001
From: richard lyman <richard.r.lyman@gmail.com>
Date: Thu, 17 Jan 2019 09:57:15 -0800
Subject: [PATCH 4/6] initial commit

---
 license.txt | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)
 create mode 100644 license.txt

diff --git a/license.txt b/license.txt
new file mode 100644
index 0000000..4219ba5
--- /dev/null
+++ b/license.txt
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019 Richard Ricker Lyman
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
\ No newline at end of file

From 9fc5ae344d6efc9a8582ad130dedb5a4e1dda13e Mon Sep 17 00:00:00 2001
From: richard lyman <richard.r.lyman@gmail.com>
Date: Sun, 7 Feb 2021 14:45:13 -0800
Subject: [PATCH 5/6] Change to TenorFlow 2 from version 1 Adjust to changes in
 sklearn, theano, keras

---
 n0_network.py                             |  4 +-
 n1_2cnv1fc.py                             |  7 ++-
 n1_2cnv2fc.py                             |  6 ++-
 n1_baseTensorNN.py                        |  4 +-
 n1_image_to_image.py                      |  5 +-
 n1_residual3x4.py                         |  6 +--
 o1_top_secret_cnn.py                      | 12 +++--
 o2_top_secret_lda-tesseract.py            |  4 +-
 o3_top_secret_python_box.py               |  2 +-
 o4_image_to_image.py                      |  2 +-
 ocr_utils.py                              | 11 +++--
 p115_l1_l2_regularization.py              |  6 +--
 p119_squential_backward_selection.py      |  2 +-
 p131_principal_component_analysis.py      |  6 +--
 p141_linear_descriminant_analsys.py       | 13 ++---
 p154_pca_nonlinear_mapings.py             |  5 +-
 p177_k_fold_cross_validation.py           | 47 +++++++++---------
 p181_learning_curves.py                   |  6 +--
 p186_grid_search.py                       |  2 +-
 p189_nested_cross_validation.py           |  4 +-
 p193_model_precision_recall.py            |  4 +-
 p194_receiver_operating_characteristic.py | 58 +++++++++++++++--------
 p206_majority_vote_classifier.py          | 23 +++++----
 p221_bagging_bootstrap_samples.py         |  2 +-
 p229_adaboost.py                          |  2 +-
 p411_keras.py                             | 11 ++---
 p51_standard_scalar.py                    |  5 +-
 p62_logistic_regression.py                |  8 ++--
 p73_support_vector_machine.py             |  1 +
 q2_tensorflow_mnist.py                    |  3 +-
 q3_removing_affine_distortion.py          |  4 +-
 q4_Theano_mlp.py                          |  1 +
 q6_tensorflow_residual3x4.py              |  2 +-
 q8_tika.py                                |  1 +
 q9_tensorflow_gpu_test.py                 | 19 +++++---
 35 files changed, 171 insertions(+), 127 deletions(-)

diff --git a/n0_network.py b/n0_network.py
index f3aa381..c37a1bd 100644
--- a/n0_network.py
+++ b/n0_network.py
@@ -1,4 +1,6 @@
-import tensorflow as tf  
+#import tensorflow as tf  
+from tensorflow.compat import v1 as tf
+tf.compat.v1.disable_eager_execution()
 import numpy as np
 from collections import namedtuple
 import datetime
diff --git a/n1_2cnv1fc.py b/n1_2cnv1fc.py
index 2b59ac2..845b6eb 100644
--- a/n1_2cnv1fc.py
+++ b/n1_2cnv1fc.py
@@ -1,4 +1,6 @@
-import tensorflow as tf  
+#import tensorflow as tf  
+from tensorflow.compat import v1 as tf
+tf.compat.v1.disable_eager_execution()
 import numpy as np
 from collections import namedtuple
 import datetime
@@ -43,6 +45,7 @@ def __init__(self, truthed_features, dtype=np.float32):
                 nm = 'x_'+nm
             if i>1:
                 extra_features_width += truthed_features.feature_width[i]
+   
             lst.append(tf.placeholder(dtype, shape=[None, truthed_features.feature_width[i]], name=nm))
             
         # ph is a named tuple with key names like 'image', 'm_label', and values that
@@ -218,7 +221,7 @@ def computeSize(s,tens):
             tShape = tens.get_shape()
             nDims = len(tShape)
             for i in range(nDims):
-                sumC *= tShape[i].value
+                sumC *= tShape[i]
             print ('\t{}\t{}'.format(s,sumC),flush=True)
             return sumC
                 
diff --git a/n1_2cnv2fc.py b/n1_2cnv2fc.py
index 8865bb4..4e32471 100644
--- a/n1_2cnv2fc.py
+++ b/n1_2cnv2fc.py
@@ -1,4 +1,6 @@
-import tensorflow as tf  
+#import tensorflow as tf  
+from tensorflow.compat import v1 as tf
+tf.compat.v1.disable_eager_execution()
 import numpy as np
 from collections import namedtuple
 import datetime
@@ -250,7 +252,7 @@ def computeSize(s,tens):
             tShape = tens.get_shape()
             nDims = len(tShape)
             for i in range(nDims):
-                sumC *= tShape[i].value
+                sumC *= tShape[i]
             print ('\t{}\t{}'.format(s,sumC),flush=True)
             return sumC
                 
diff --git a/n1_baseTensorNN.py b/n1_baseTensorNN.py
index 7870c5e..22a90e6 100644
--- a/n1_baseTensorNN.py
+++ b/n1_baseTensorNN.py
@@ -1,4 +1,6 @@
-import tensorflow as tf  
+from tensorflow.compat import v1 as tf
+tf.compat.v1.disable_eager_execution()
+#import tf
 import numpy as np
 from collections import namedtuple
 import datetime
diff --git a/n1_image_to_image.py b/n1_image_to_image.py
index e6b86c4..f391548 100644
--- a/n1_image_to_image.py
+++ b/n1_image_to_image.py
@@ -1,4 +1,5 @@
-import tensorflow as tf  
+from tensorflow.compat import v1 as tf
+#import tf
 import numpy as np
 from collections import namedtuple
 import datetime
@@ -258,7 +259,7 @@ def computeSize(s,tens):
             tShape = tens.get_shape()
             nDims = len(tShape)
             for i in range(nDims):
-                sumC *= tShape[i].value
+                sumC *= tShape[i]
             print ('\t{}\t{}'.format(s,sumC),flush=True)
             return sumC
                 
diff --git a/n1_residual3x4.py b/n1_residual3x4.py
index 12569f3..3bf1cc6 100644
--- a/n1_residual3x4.py
+++ b/n1_residual3x4.py
@@ -7,7 +7,7 @@
 
 
 '''
-import tensorflow as tf  
+from tensorflow.compat import v1 as tf
 import numpy as np
 from collections import namedtuple
 import datetime
@@ -91,7 +91,7 @@ def bias_variable(shape, dtype):
             return tf.Variable(initial)   
                 
         def shapeOuts(n):
-            print ('n={}, hin={},w={}, b={} ,hout={}\n'.format(n, h[n]._shape, w[n]._variable._shape, b[n]._variable._shape, h[n+1]._shape))
+            print ('n={}, hin={},w={}, b={} ,hout={}\n'.format(n, h[n].shape, w[n].shape, b[n].shape, h[n+1]._shape))
              
         def section(n):
             with tf.name_scope('section_'+str(n)+'_0') as scope:     
@@ -122,7 +122,7 @@ def computeSize(s,tens):
             tShape = tens.get_shape()
             nDims = len(tShape)
             for i in range(nDims):
-                sumC *= tShape[i].value
+                sumC *= tShape[i]
             print ('\t{}\t{}'.format(s,sumC),flush=True)
             return sumC
                          
diff --git a/o1_top_secret_cnn.py b/o1_top_secret_cnn.py
index 86e931a..afe5e5a 100644
--- a/o1_top_secret_cnn.py
+++ b/o1_top_secret_cnn.py
@@ -44,6 +44,10 @@
 #import n1_2cnv1fc as nnetwork     
 #import n1_residual3x4 as nnetwork 
 import n1_2cnv2fc as nnetwork     
+import skimage.transform as af  
+from bitarray import bitarray 
+
+
 input_filters_dict = {'m_label': list(range(48,58))+list(range(65,91))}  
 output_feature_list  = ['orientation_one_hot','image']   
 dtype = np.float32
@@ -70,7 +74,7 @@
 
 
 # pick up the base characters from training_image_file
-# produce some skeared versions 
+# produce some sheared versions 
 # make into a training set
 # place in a ocr_utils TruthedCharacters class so we can use the
 # one hot and batch functions 
@@ -94,7 +98,7 @@
 orientation=[]
 recognized_label =[]
 
-import skimage.transform as af  
+
 
 for j in range(shp[0]):
     for i,skew in enumerate(skewRange):
@@ -141,10 +145,10 @@
 image_file_jpg = image_file+'.jpg'
 df,t1 = ocr_utils.file_to_df(image_file,character_size, title = 'unencrypted file',white_space=white_space)
  
-from bitarray import bitarray  
+ 
 secret_message = "top secret"
 a = bitarray()   
-a.fromstring(secret_message)
+a.frombytes(secret_message.encode('utf_8'))
 
 index = 0
 encoded_skews=[]
diff --git a/o2_top_secret_lda-tesseract.py b/o2_top_secret_lda-tesseract.py
index 899c6ad..b7dcc36 100644
--- a/o2_top_secret_lda-tesseract.py
+++ b/o2_top_secret_lda-tesseract.py
@@ -12,6 +12,7 @@
 from sklearn.linear_model import LogisticRegression
 from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA   
 from sklearn.metrics import accuracy_score
+from ruamel_yaml.compat import utf8
 
 inputs = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghiklnopqrstuvwxyz'
 inputs_list = list(ord(x) for x in inputs)
@@ -70,8 +71,7 @@ def encode_and_save_file(input_base, output_base, character_size, white_space, s
     
     from bitarray import bitarray  
     a = bitarray()   
-    a.fromstring(secret_message)
-    
+    a.frombytes(secret_message.encode('utf-8') )   
     index = 0
 
     def convert_to_shear(a):
diff --git a/o3_top_secret_python_box.py b/o3_top_secret_python_box.py
index bffc071..c330dd1 100644
--- a/o3_top_secret_python_box.py
+++ b/o3_top_secret_python_box.py
@@ -73,7 +73,7 @@
 #from sklearn.model_selection
 from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
 from sklearn.linear_model import LogisticRegression
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 
 # input_filters_dict = {'m_label': list(range(48,58))+list(range(65,91))}  
 # output_feature_list  = ['orientation_one_hot','image']   
diff --git a/o4_image_to_image.py b/o4_image_to_image.py
index d54a19e..5cce174 100644
--- a/o4_image_to_image.py
+++ b/o4_image_to_image.py
@@ -30,7 +30,7 @@
 import pandas as pd
 import n1_image_to_image as nnetwork  
 #import n1_residual3x4 as nnetwork  
-import tensorflow as tf  
+from tensorflow.compat import v1 as tf 
 dtype = np.float32
 #with tf.device('/GPU:0'):
 #with tf.device('/cpu:0'): 
diff --git a/ocr_utils.py b/ocr_utils.py
index 2791558..a130fe9 100644
--- a/ocr_utils.py
+++ b/ocr_utils.py
@@ -38,7 +38,8 @@
 import numpy as np
 import pandas as pd
 import math
-from pandas.io.common import ZipFile
+#from pandas.io.common import ZipFile
+from zipfile import ZipFile
 from matplotlib.colors import ListedColormap
 import matplotlib.pyplot as plt
 import sys
@@ -1064,9 +1065,13 @@ def plot_decision_regions(X=None, y=None, classifier=None, resolution = .005, te
     plt.ylim(xx2.min()-d, xx2.max()+d)
 
     # plot class samples
+
     for idx, cl in enumerate(np.unique(y)):
-        plt.scatter(X[y == cl, 0], X[y == cl, 1],
-                    alpha=0.8, c=cmap(idx),
+        xs = X[y == cl, 0]
+        ys = X[y == cl, 1]
+        c =cmap(idx)
+        plt.scatter(xs, ys,
+                    alpha=0.8, color=c,
                     marker=markers[idx%len(markers)], label=cl) 
         
     # highlight test samples
diff --git a/p115_l1_l2_regularization.py b/p115_l1_l2_regularization.py
index ff90e48..690ae2f 100644
--- a/p115_l1_l2_regularization.py
+++ b/p115_l1_l2_regularization.py
@@ -69,7 +69,7 @@
 
 from sklearn.linear_model import LogisticRegression
 
-lr = LogisticRegression(penalty='l1', C=0.1, random_state=0)
+lr = LogisticRegression(penalty='l1', C=0.1, random_state=0, solver='liblinear',multi_class='auto')
 lr.fit(X_train_std, y_train)
 print('Training accuracy-l1 regularization:', lr.score(X_train_std, y_train))
 print('Test accuracy-l1 regularization:', lr.score(X_test_std, y_test))
@@ -79,7 +79,7 @@
 print('\t{}'.format(lr.coef_))
 
 
-lr = LogisticRegression(penalty='l2', C=0.1, random_state=0)
+lr = LogisticRegression(penalty='l2', C=0.1, random_state=0, solver='liblinear',multi_class='auto')
 lr.fit(X_train_std, y_train)
 print('Training accuracy-l2 regularization:', lr.score(X_train_std, y_train))
 print('Test accuracy-l2 regularization:', lr.score(X_test_std, y_test))
@@ -101,7 +101,7 @@
 def weight_graph(regularization = 'l1'):
     weights, params = [], []
     for c in np.arange(0, 6):
-        lr = LogisticRegression(penalty=regularization, C=10**c, random_state=0)
+        lr = LogisticRegression(penalty=regularization, C=10**c, random_state=0, solver='liblinear',multi_class='auto')
         lr.fit(X_train_std, y_train)
         weights.append(lr.coef_[1])
         params.append(10**c)
diff --git a/p119_squential_backward_selection.py b/p119_squential_backward_selection.py
index 2143d7c..d226961 100644
--- a/p119_squential_backward_selection.py
+++ b/p119_squential_backward_selection.py
@@ -49,7 +49,7 @@
 y, X, y_test,  X_test, labels  = ocr_utils.load_E13B(chars_to_train = (48,49,50) , columns=range(0,20), nChars=1000, random_state=0) 
 
 
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 
 X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.3, random_state=0)
diff --git a/p131_principal_component_analysis.py b/p131_principal_component_analysis.py
index 8a379b5..c4b8a00 100644
--- a/p131_principal_component_analysis.py
+++ b/p131_principal_component_analysis.py
@@ -130,7 +130,7 @@
 X_train_pca = pca.fit_transform(X_train_image)
 X_test_pca = pca.transform(X_test_image)
 
-lr = LogisticRegression()
+lr = LogisticRegression(solver='liblinear',multi_class='auto')
 logistic_fitted =lr.fit(X_train_pca, y_train)
 
 print('\nPCA Train Accuracy: {:4.6f}, n_components={}'.format(accuracy_score(y_train, logistic_fitted.predict(X_train_pca)),pca.n_components))
@@ -149,7 +149,7 @@
 X_train_pca = pca.fit_transform(X_train_image)
 X_test_pca = pca.transform(X_test_image)
 
-lr = LogisticRegression()
+lr = LogisticRegression(solver='liblinear',multi_class='auto')
 logistic_fitted = lr.fit(X_train_pca, y_train)
 
 y_train_pred = logistic_fitted.predict(X_train_pca)
@@ -191,7 +191,7 @@
 X_train_pca = pca.fit_transform(X_train_image)
 X_test_pca = pca.transform(X_test_image)
 
-lr = LogisticRegression()
+lr = LogisticRegression(solver='liblinear',multi_class='auto')
 logistic_fitted=lr.fit(X_train_pca, y_train)
 y_train_pred = logistic_fitted.predict(X_train_pca)
 y_test_pred = logistic_fitted.predict(X_test_pca)
diff --git a/p141_linear_descriminant_analsys.py b/p141_linear_descriminant_analsys.py
index 3e87962..1d50af8 100644
--- a/p141_linear_descriminant_analsys.py
+++ b/p141_linear_descriminant_analsys.py
@@ -61,6 +61,7 @@
 from sklearn.discriminant_analysis import  LinearDiscriminantAnalysis as LDA   
 print_limit = 20
 chars_to_train = range(48,58)
+n_classes = len(chars_to_train)
 columnsXY=range(0,20)
 column_str = 'column_sum{}'.format(list(columnsXY))
 
@@ -107,7 +108,7 @@
 S_W = np.zeros((d, d))
 for label, mv in zip(unique_labels, mean_vecs):
     class_scatter = np.zeros((d, d))
-    for row in X_train_std[[y_train == label]]:
+    for row in X_train_std[y_train == label]:
         row, mv = row.reshape(d, 1), mv.reshape(d, 1)
         class_scatter += (row-mv).dot((row-mv).T)
     S_W += class_scatter
@@ -195,7 +196,7 @@
 X_test_lda =  lda.transform(X_test_std)
 
 from sklearn.linear_model import LogisticRegression
-lr = LogisticRegression()
+lr = LogisticRegression(solver='liblinear', multi_class='auto')
 lr = lr.fit(X_train_lda, y_train)
 
 title = 'Linear Descriminant Analysis Training Set'
@@ -208,13 +209,13 @@
 
 ###############################################################################
 n_components = 10
-lda = LDA(n_components=n_components)
+lda = LDA(n_components=min(n_components,n_classes-1))
 X_train_lda = lda.fit_transform(X_train_std, y_train)
 X_test_lda = lda.transform(X_test_std)
 
 print ('n_components={}'.format(lda.n_components))
 
-lr = LogisticRegression()
+lr = LogisticRegression(solver='liblinear', multi_class='auto')
 logistic_fitted = lr.fit(X_train_lda, y_train)
 
 from sklearn.metrics import accuracy_score
@@ -233,13 +234,13 @@
 
 ###############################################################################
 n_components = 10
-lda = LDA(n_components=n_components, solver='eigen')
+lda = LDA(n_components=n_components-1, solver='eigen')
 X_train_lda = lda.fit_transform(X_train_std, y_train)
 X_test_lda = lda.transform(X_test_std)
 
 print ('n_components={}'.format(lda.n_components))
 
-lr = LogisticRegression()
+lr = LogisticRegression(solver='liblinear', multi_class='auto')
 logistic_fitted = lr.fit(X_train_lda, y_train)
 
 from sklearn.metrics import accuracy_score
diff --git a/p154_pca_nonlinear_mapings.py b/p154_pca_nonlinear_mapings.py
index c7da287..3d92e9d 100644
--- a/p154_pca_nonlinear_mapings.py
+++ b/p154_pca_nonlinear_mapings.py
@@ -83,8 +83,7 @@ def rbf_kernel_pca1(X, gamma, n_components):
     eigvals, eigvecs = eigh(K)
 
     # Collect the top k eigenvectors (projected samples)
-    X_pc = np.column_stack((eigvecs[:, -i]
-                            for i in range(1, n_components + 1)))
+    X_pc = np.column_stack([eigvecs[:, -i] for i in range(1, n_components + 1)])
 
     return X_pc
 
@@ -269,7 +268,7 @@ def rbf_kernel_pca(X, gamma, n_components):
     eigvals, eigvecs = eigh(K)
 
     # Collect the top k eigenvectors (projected samples)
-    alphas = np.column_stack((eigvecs[:,-i] for i in range(1,n_components+1)))
+    alphas = np.column_stack([eigvecs[:,-i] for i in range(1,n_components+1)])
     
     # Collect the corresponding eigenvalues
     lambdas = [eigvals[-i] for i in range(1,n_components+1)]
diff --git a/p177_k_fold_cross_validation.py b/p177_k_fold_cross_validation.py
index c6bf4b5..6a32210 100644
--- a/p177_k_fold_cross_validation.py
+++ b/p177_k_fold_cross_validation.py
@@ -1,4 +1,4 @@
-'''k_fold_cross_validation.py
+'''k_fold_model_selection.py
 k fold cross validation splits the training set into n parts and uses a 
 different 1/n of the test set for each iteration.  It is good for
 tuning  parameters as all samples are used, reducing the variance of the 
@@ -48,12 +48,13 @@
 import ocr_utils
 import matplotlib.pyplot as plt
 import numpy as np
-from sklearn.cross_validation import StratifiedKFold
+from sklearn.model_selection import StratifiedKFold
 from sklearn.discriminant_analysis import  LinearDiscriminantAnalysis as LDA  
 
 if __name__ == '__main__':
     #charsToTrain=range(48,58)
     chars_to_train = range(48,58)
+    n_classes = len(chars_to_train)
     
     num_chars = 3000 #limit the number to speed up the calculation
     
@@ -75,7 +76,7 @@
     # y_train, X_train, y_test,  X_test, labels  = ocr_utils.load_E13B(chars_to_train = charsToTrain , columns=range(0,20), nChars=1000, test_size=0.3,random_state=0)  
     
     from sklearn.linear_model import LogisticRegression
-    from sklearn.cross_validation import train_test_split
+    from sklearn.model_selection import train_test_split
     
     X_train , X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.3, random_state=0)
     
@@ -99,28 +100,24 @@
     for num_PCA in num_planes:
         print ('number of Principal Components = {}'.format(num_PCA))
         pipe_lr = Pipeline([('scl', StandardScaler()),
-                    ('pca', PCA(n_components=num_PCA)),
-                    ('clf', LogisticRegression(random_state=1))])
-        
+                    ('pca', PCA(n_components=num_PCA, svd_solver='full')),
+                    ('clf', LogisticRegression(random_state=1,multi_class='auto', solver='liblinear'))])
+
         pipe_lr.fit(X_train, y_train)
         print('Test Accuracy: %.3f' % pipe_lr.score(X_test, y_test))
         
-    
-        
-        kfold = StratifiedKFold(y=y_train, 
-                                n_folds=10,
-                                random_state=1)
+        kfold = StratifiedKFold(n_splits=10, random_state=1)
         
         scores = []
-        for k, (train, test) in enumerate(kfold):
-            pipe_lr.fit(X_train[train], y_train[train])
-            score = pipe_lr.score(X_train[test], y_train[test])
+        for train_index, test_index in kfold.split(X_train, y_train):
+            pipe_lr.fit(X_train[train_index], y_train[train_index])
+            score = pipe_lr.score(X_train[test_index], y_train[test_index])
             scores.append(score)
             #print ('train {} samples: {}'.format(len(train), train))
             #print('Fold: %s, Class dist.: %s, Acc: %.3f' % (k+1, np.bincount(y_train[train])[list(charsToTrain)], score))
             
         print('\nCV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))
-        from sklearn.cross_validation import cross_val_score
+        from sklearn.model_selection import cross_val_score
         
         scores = cross_val_score(estimator=pipe_lr, 
                                  X=X_train, 
@@ -153,21 +150,21 @@
     for num_LDA in num_planes:
         print ('number of Principal Components = {}'.format(num_LDA))
         pipe_lr = Pipeline([('scl', StandardScaler()),
-                    ('lda', LDA(n_components=num_LDA)),
-                    ('clf', LogisticRegression(random_state=1))])
-        
+                    ('lda', LDA(n_components=min(num_LDA,n_classes-1), solver='eigen')),
+                    ('clf', LogisticRegression(random_state=1,multi_class='auto',solver='liblinear'))])
+
+        kys = pipe_lr.get_params().keys()  
+        print(kys)      
+#         pipe_lr.set_params(lda__solver='eigen',clf__solver='liblinear',clf__multi_class='auto')             
         pipe_lr.fit(X_train, y_train)
         print('Test Accuracy: %.3f' % pipe_lr.score(X_test, y_test))
-    
         
-        kfold = StratifiedKFold(y=y_train, 
-                                n_folds=10,
-                                random_state=1)
+        kfold = StratifiedKFold(n_splits=10, random_state=1)
         
         scores = []
-        for k, (train, test) in enumerate(kfold):
-            pipe_lr.fit(X_train[train], y_train[train])
-            score = pipe_lr.score(X_train[test], y_train[test])
+        for train_index, test_index in kfold.split(X_train, y_train):
+            pipe_lr.fit(X_train[train_index], y_train[train_index])
+            score = pipe_lr.score(X_train[test_index], y_train[test_index])            
             scores.append(score)
             #print ('train {} samples: {}'.format(len(train), train))
             #print('Fold: %s, Class dist.: %s, Acc: %.3f' % (k+1, np.bincount(y_train[train])[list(charsToTrain)], score))
diff --git a/p181_learning_curves.py b/p181_learning_curves.py
index 281587e..811a9ef 100644
--- a/p181_learning_curves.py
+++ b/p181_learning_curves.py
@@ -42,7 +42,7 @@
 @author: richard lyman
 '''
 import matplotlib.pyplot as plt
-from sklearn.learning_curve import learning_curve
+from sklearn.model_selection import learning_curve
 import numpy as np
 import ocr_utils  
 from sklearn.preprocessing import StandardScaler
@@ -55,7 +55,7 @@
     y_train, X_train, y_test,  X_test, labels  = ocr_utils.load_E13B(chars_to_train = (48,51) , columns=(9,17), random_state=0) 
           
     pipe_lr = Pipeline([('scl', StandardScaler()),
-                ('clf', LogisticRegression(penalty='l2', random_state=0))])
+                ('clf', LogisticRegression(penalty='l2', random_state=0, solver='lbfgs'))])
 
     train_sizes, train_scores, test_scores =\
                     learning_curve(estimator=pipe_lr, 
@@ -99,7 +99,7 @@
     plt.tight_layout()
     ocr_utils.show_figures(plt,title)
 
-    from sklearn.learning_curve import validation_curve
+    from sklearn.model_selection import validation_curve
 
     param_range = [0.001, 0.01, 0.1, 1.0, 10.0, 100.0]
     train_scores, test_scores = validation_curve(
diff --git a/p186_grid_search.py b/p186_grid_search.py
index 344b807..cb2f00a 100644
--- a/p186_grid_search.py
+++ b/p186_grid_search.py
@@ -35,7 +35,7 @@
 import ocr_utils
 from sklearn.preprocessing import StandardScaler
 from sklearn.pipeline import Pipeline
-from sklearn.grid_search import GridSearchCV
+from sklearn.model_selection import GridSearchCV
 from sklearn.svm import SVC
 
 if __name__ == '__main__':	
diff --git a/p189_nested_cross_validation.py b/p189_nested_cross_validation.py
index 694f0c4..17d55e6 100644
--- a/p189_nested_cross_validation.py
+++ b/p189_nested_cross_validation.py
@@ -55,8 +55,8 @@
 import ocr_utils
 from sklearn.preprocessing import StandardScaler
 from sklearn.pipeline import Pipeline
-from sklearn.cross_validation import cross_val_score
-from sklearn.grid_search import GridSearchCV
+from sklearn.model_selection import cross_val_score
+from sklearn.model_selection import GridSearchCV
 from sklearn.svm import SVC
 if __name__ == '__main__':
         y_train, X_train, y_test,  X_test, labels  = ocr_utils.load_E13B(chars_to_train = (48,51) , test_size=0.3, columns=(9,17), random_state=0) 
diff --git a/p193_model_precision_recall.py b/p193_model_precision_recall.py
index 25e8766..5413066 100644
--- a/p193_model_precision_recall.py
+++ b/p193_model_precision_recall.py
@@ -49,9 +49,9 @@
 from sklearn.preprocessing import StandardScaler
 from sklearn.pipeline import Pipeline
 from sklearn.svm import SVC
-from sklearn.grid_search import GridSearchCV
+from sklearn.model_selection import GridSearchCV
 from sklearn.metrics import make_scorer,precision_score, recall_score, f1_score
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 
 if __name__ == '__main__':
     y, X, y_test,  X_test, labels  = ocr_utils.load_E13B(chars_to_train = (48,51) , columns=(9,17), random_state=0) 
diff --git a/p194_receiver_operating_characteristic.py b/p194_receiver_operating_characteristic.py
index 44d69a0..5bc308f 100644
--- a/p194_receiver_operating_characteristic.py
+++ b/p194_receiver_operating_characteristic.py
@@ -45,10 +45,12 @@
 from sklearn.preprocessing import StandardScaler
 from sklearn.linear_model import LogisticRegression
 from sklearn.pipeline import Pipeline
-from sklearn.cross_validation import StratifiedKFold
+from sklearn.model_selection import StratifiedKFold
 from sklearn.decomposition import PCA
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.metrics import make_scorer,precision_score,roc_curve, auc
+from sklearn.metrics import roc_auc_score, accuracy_score
+from sklearn.model_selection import cross_val_score    
 
 if __name__ == '__main__':
 
@@ -64,32 +66,48 @@
 
     pipe_lr = Pipeline([('scl', StandardScaler()),
                         ('pca', PCA(n_components=2)),
-                        ('clf', LogisticRegression(penalty='l2',random_state=0,C=100.0))])
+                        ('clf', LogisticRegression(penalty='l2',random_state=0,C=100.0, solver='lbfgs'))])
 
     # X_train2 = X_train[:, [4, 14]]
     X_train2 = X_train
-    cv = StratifiedKFold(y_train,n_folds=3,random_state=1)
+    
+    kfold = StratifiedKFold(n_splits=3, random_state=1)
+        
+#         scores = []
+#         for train_index, test_index in kfold.split(X_train, y_train):
+#             pipe_lr.fit(X_train[train_index], y_train[train_index])
+#             score = pipe_lr.score(X_train[test_index], y_train[test_index])            
+#             scores.append(score)
+    
+    
+    
+    
+    
+    
+    
+#     cv = StratifiedKFold(y_train,n_folds=3,random_state=1)
     fig = plt.figure(figsize=(7, 5))
 
     mean_tpr = 0.0
     mean_fpr = np.linspace(0, 1, 100)
     all_tpr = []
-
-    for i, (train, test) in enumerate(cv):
-        probas = pipe_lr.fit(X_train2[train], 
-                             y_train[train]).predict_proba(X_train2[test])
+    i=0
+    for train_index, test_index in kfold.split(X_train, y_train):
+        probas = pipe_lr.fit(X_train2[train_index], 
+                             y_train[train_index]).predict_proba(X_train2[test_index])
         
-        fpr, tpr, thresholds = roc_curve(y_train[test], 
+        fpr, tpr, thresholds = roc_curve(y_train[test_index], 
                                          probas[:, 1], 
                                          pos_label=1)
         mean_tpr += interp(mean_fpr, fpr, tpr)
         mean_tpr[0] = 0.0
         roc_auc = auc(fpr, tpr)
+        i=i+1
         plt.plot(fpr, 
                  tpr, 
                  lw=1, 
                  label='ROC fold %d (area = %0.2f)' 
-                        % (i+1, roc_auc))
+                        % (i, roc_auc))
 
     plt.plot([0, 1], 
              [0, 1], 
@@ -97,7 +115,7 @@
              color=(0.6, 0.6, 0.6), 
              label='random guessing')
 
-    mean_tpr /= len(cv)
+    mean_tpr /= kfold.get_n_splits(X_train)
     mean_tpr[-1] = 1.0
     mean_auc = auc(mean_fpr, mean_tpr)
     plt.plot(mean_fpr, mean_tpr, 'k--',
@@ -123,8 +141,6 @@
     pipe_lr = pipe_lr.fit(X_train2, y_train)
     # y_pred2 = pipe_lr.predict(X_test[:, [4, 14]])
     y_pred2 = pipe_lr.predict(X_test)
-    from sklearn.metrics import roc_auc_score, accuracy_score
-
 
     print('ROC AUC: %.3f' % roc_auc_score(y_true=y_test, y_score=y_pred2))
     print('Accuracy: %.3f' % accuracy_score(y_true=y_test, y_pred=y_pred2))
@@ -133,10 +149,10 @@
     # for more than 2 classes for GridSearch
     # i.e. applies a binary scoring technique to multiclasses
     pos_label=range(48,58)
-    pre_scorer = make_scorer(score_func=precision_score, 
-                             pos_label=pos_label, 
-                             greater_is_better=True, 
-                             average='micro')
+#     pre_scorer = make_scorer(score_func=precision_score, 
+#                              pos_label=pos_label, 
+#                              greater_is_better=True, 
+#                              average='micro')
 
     from sklearn.svm import SVC
     y_train, X_train, y_test,  X_test, labels  = ocr_utils.load_E13B(chars_to_train = pos_label , nChars=4000, columns=(9,17), random_state=0) 
@@ -149,15 +165,15 @@
                      {'clf__C': c_gamma_range, 
                       'clf__gamma': c_gamma_range, 
                       'clf__kernel': ['rbf'],}]
-    from sklearn.grid_search import GridSearchCV
+    from sklearn.model_selection import GridSearchCV
     gs = GridSearchCV(estimator=pipe_svc, 
                                 param_grid=param_grid, 
-                                scoring=pre_scorer,
+                                scoring='accuracy',
                                 cv=5,
                                 n_jobs=-1)
 
-    from sklearn.cross_validation import cross_val_score
-    scores = cross_val_score(gs, X_train, y_train, scoring=pre_scorer, cv=5)
+
+    scores = cross_val_score(gs, X_train, y_train, scoring='accuracy', cv=5)
     print('\nSupport Vector Cross Validation accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))
 
     gs = gs.fit(X_train, y_train)
diff --git a/p206_majority_vote_classifier.py b/p206_majority_vote_classifier.py
index 221dc52..e404fa7 100644
--- a/p206_majority_vote_classifier.py
+++ b/p206_majority_vote_classifier.py
@@ -44,13 +44,12 @@
 from sklearn.base import BaseEstimator
 from sklearn.base import ClassifierMixin
 from sklearn.preprocessing import LabelEncoder
-from sklearn.externals import six
 from sklearn.base import clone
 from sklearn.pipeline import _name_estimators
 import numpy as np       
 import ocr_utils 
-from sklearn.cross_validation import train_test_split
-from sklearn.cross_validation import cross_val_score
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import cross_val_score
 from sklearn.linear_model import LogisticRegression
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.neighbors import KNeighborsClassifier 
@@ -59,6 +58,7 @@
 from sklearn.metrics import roc_curve
 from sklearn.metrics import auc
 import matplotlib.pyplot as plt
+from sklearn.model_selection import GridSearchCV
 
 class MajorityVoteClassifier(BaseEstimator, 
                              ClassifierMixin):
@@ -182,8 +182,8 @@ def get_params(self, deep=True):
             return super(MajorityVoteClassifier, self).get_params(deep=False)
         else:
             out = self.named_classifiers.copy()
-            for name, step in six.iteritems(self.named_classifiers):
-                for key, value in six.iteritems(step.get_params(deep=True)):
+            for name, step in self.named_classifiers.items():
+                for key, value in step.get_params(deep=True).items():
                     out['%s__%s' % (name, key)] = value
             return out
 
@@ -204,7 +204,7 @@ def get_params(self, deep=True):
 
 clf1 = LogisticRegression(penalty='l2', 
                           C=0.001, 
-                          random_state=0)
+                          random_state=0, solver='lbfgs')
 
 clf2 = DecisionTreeClassifier(max_depth=1, 
                               criterion='entropy', 
@@ -216,6 +216,7 @@ def get_params(self, deep=True):
 
 pipe1 = Pipeline([['sc', StandardScaler()],
                   ['clf', clf1]])
+kys = pipe1.get_params()
 
 pipe3 = Pipeline([['sc', StandardScaler()],
                   ['clf', clf3]])
@@ -314,7 +315,7 @@ def get_params(self, deep=True):
 pprint.pprint(mv_clf.get_params())
 print()
   
-from sklearn.grid_search import GridSearchCV
+
 
 params = {'decisiontreeclassifier__max_depth': [1, 2],
           'pipeline-1__clf__C': [0.001, 0.1, 100.0]}
@@ -325,9 +326,13 @@ def get_params(self, deep=True):
                     scoring='roc_auc')
 grid.fit(X_train, y_train)
 
-for params, mean_score, scores in grid.grid_scores_:
+params=grid.cv_results_['params']
+mean_scores=grid.cv_results_['mean_test_score']
+scores =    grid.cv_results_['std_test_score']
+
+for i in range(len(params)):
     print("%0.6f+/-%0.6f %r"
-            % (mean_score, scores.std() / 2, sorted(params.items())))
+            % (mean_scores[i], scores[i] / 2, sorted(params[i].items())))
 print('\nBest parameters: %s' % sorted(grid.best_params_.items()))
 print('Best Accuracy: %.6f' % grid.best_score_)
 
diff --git a/p221_bagging_bootstrap_samples.py b/p221_bagging_bootstrap_samples.py
index 1f0a945..cc34df9 100644
--- a/p221_bagging_bootstrap_samples.py
+++ b/p221_bagging_bootstrap_samples.py
@@ -35,7 +35,7 @@
 
 from sklearn.preprocessing import LabelEncoder
 import ocr_utils
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 import numpy as np
 import matplotlib.pyplot as plt
 if __name__ == '__main__':
diff --git a/p229_adaboost.py b/p229_adaboost.py
index cf97b71..f4852f0 100644
--- a/p229_adaboost.py
+++ b/p229_adaboost.py
@@ -41,7 +41,7 @@
 from sklearn.ensemble import AdaBoostClassifier
       
 import ocr_utils
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.preprocessing import LabelEncoder
diff --git a/p411_keras.py b/p411_keras.py
index 823dd3a..d5216ba 100644
--- a/p411_keras.py
+++ b/p411_keras.py
@@ -50,25 +50,22 @@ def do_keras(X_train,X_test, y_train_ohe, y_train,y_test):
     
     model = Sequential()
     model.add(Dense(input_dim=X_train.shape[1], 
-                    output_dim=50, 
-                    init='uniform', 
+                    units=50,  
                     activation='tanh'))
     
     model.add(Dense(input_dim=50, 
-                    output_dim=50, 
-                    init='uniform', 
+                    units=50, 
                     activation='tanh'))
     
     model.add(Dense(input_dim=50, 
-                    output_dim=y_train_ohe.shape[1], 
-                    init='uniform', 
+                    units=y_train_ohe.shape[1],  
                     activation='softmax'))
     
     sgd = SGD(lr=0.001, decay=1e-7, momentum=.9)
     model.compile(loss='categorical_crossentropy', optimizer=sgd,metrics=["accuracy"])
     
     model.fit(X_train, y_train_ohe, 
-              nb_epoch=50, 
+              epochs=50, 
               batch_size=300, 
               verbose=2, 
               validation_split=0.1
diff --git a/p51_standard_scalar.py b/p51_standard_scalar.py
index 1a8cca4..89e8014 100644
--- a/p51_standard_scalar.py
+++ b/p51_standard_scalar.py
@@ -38,6 +38,7 @@
 import ocr_utils
 from sklearn.preprocessing import StandardScaler
 from sklearn.linear_model import Perceptron
+from sklearn.model_selection import train_test_split
 
 
 #############################################################################
@@ -51,7 +52,7 @@
 
 print('Class labels:', np.unique(y))
 
-from sklearn.cross_validation import train_test_split
+
 #############################################################################
 # standardize the features
 
@@ -63,7 +64,7 @@
 X_train_std = sc.transform(X_train)
 X_test_std = sc.transform(X_test)
 
-ppn = Perceptron(n_iter=40, eta0=0.1, random_state=0)
+ppn = Perceptron(max_iter=40, eta0=0.1, random_state=0)
 ppn.fit(X_train_std, y_train)
 
 y_pred = ppn.predict(X_test_std)
diff --git a/p62_logistic_regression.py b/p62_logistic_regression.py
index e92e491..6333395 100644
--- a/p62_logistic_regression.py
+++ b/p62_logistic_regression.py
@@ -56,7 +56,7 @@
 import matplotlib.pyplot as plt
 from sklearn.preprocessing import StandardScaler
 from sklearn.linear_model import LogisticRegression
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 
 y, X, y_test,  X_test, labels  = ocr_utils.load_E13B(chars_to_train = (48,49,50) , columns=(9,17),nChars=500) 
 
@@ -108,7 +108,7 @@ def cost_0(z):
 sc.fit(X_train)
 X_train_std = sc.transform(X_train)
 X_test_std = sc.transform(X_test)
-lr = LogisticRegression(C=1000.0, random_state=0)
+lr = LogisticRegression(C=1000.0, random_state=0, solver='lbfgs',multi_class='auto')
 lr.fit(X_train_std, y_train)
 X_combined_std = np.vstack((X_train_std, X_test_std))
 y_combined = np.hstack((y_train, y_test))
@@ -123,7 +123,7 @@ def cost_0(z):
 
 weights, params = [], []
 for c in np.arange(0, 5):
-    lr = LogisticRegression(C=10**c, random_state=0)
+    lr = LogisticRegression(C=10**c, random_state=0, solver='lbfgs',multi_class='auto')
     lr.fit(X_train_std, y_train)
     weights.append(lr.coef_[0])
     params.append(10**c)
@@ -132,7 +132,7 @@ def cost_0(z):
 title = 'regression_path'
 weights, params = [], []
 for c in np.arange(0, 5):
-    lr = LogisticRegression(C=10**c, random_state=0)
+    lr = LogisticRegression(C=10**c, random_state=0, solver='lbfgs',multi_class='auto')
     lr.fit(X_train_std, y_train)
     weights.append(lr.coef_[1])
     params.append(10**c)
diff --git a/p73_support_vector_machine.py b/p73_support_vector_machine.py
index 4643414..27a03b3 100644
--- a/p73_support_vector_machine.py
+++ b/p73_support_vector_machine.py
@@ -51,6 +51,7 @@
 svm = SVC(kernel='linear', C=1.0, random_state=0)
 svm.fit(X_train_std, y_train)
 
+
 ocr_utils.plot_decision_regions(X=X_combined_std, 
                                          y=y_combined,                       
                                          classifier=svm, 
diff --git a/q2_tensorflow_mnist.py b/q2_tensorflow_mnist.py
index fa9ff46..a5493da 100644
--- a/q2_tensorflow_mnist.py
+++ b/q2_tensorflow_mnist.py
@@ -33,7 +33,8 @@
 import numpy as np
 import pandas as pd
 import n1_2cnv1fc as nnetwork      
-import tensorflow as tf  
+from tensorflow.compat import v1 as tf
+#import tf
 dtype = np.float32
     
 if True:
diff --git a/q3_removing_affine_distortion.py b/q3_removing_affine_distortion.py
index 85d5f37..93b7855 100644
--- a/q3_removing_affine_distortion.py
+++ b/q3_removing_affine_distortion.py
@@ -93,7 +93,7 @@ def shear(X, skew):
 # run a Logistic Regression on the raw features with 20 rows, 20 columns
 
 from sklearn.linear_model import LogisticRegression
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 
 X_train , X_test, y_train, y_test = train_test_split(images_reshaped, ys, test_size=0.3, random_state=0)
 
@@ -146,7 +146,7 @@ def shear(X, skew):
 #########################################################################
 # run Linear Discriminant Analysis first then Logistic Regression
 
-from sklearn.discriminant_analysis import  LinearDiscriminantAnalysis as LDA
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
 n_components = 2
 lda = LDA(n_components=n_components)
 
diff --git a/q4_Theano_mlp.py b/q4_Theano_mlp.py
index f0278c7..caed736 100644
--- a/q4_Theano_mlp.py
+++ b/q4_Theano_mlp.py
@@ -20,6 +20,7 @@
 import time
 import numpy as np
 import theano
+# must comment out downsampling in pool.py because pip install did not include downsample
 import theano.tensor as T
 import lasagne
 import ocr_utils
diff --git a/q6_tensorflow_residual3x4.py b/q6_tensorflow_residual3x4.py
index 951ff47..d38cb8f 100644
--- a/q6_tensorflow_residual3x4.py
+++ b/q6_tensorflow_residual3x4.py
@@ -29,7 +29,7 @@
 import numpy as np
 import pandas as pd
 import n1_residual3x4 as nnetwork      
-import tensorflow as tf  
+from tensorflow.compat import v1 as tf
 dtype = np.float32
 #with tf.device('/GPU:0'):
 #with tf.device('/cpu:0'): 
diff --git a/q8_tika.py b/q8_tika.py
index b1c7f93..1cc29f7 100644
--- a/q8_tika.py
+++ b/q8_tika.py
@@ -28,6 +28,7 @@
 #os.putenv( 'TIKA_CLIENT_ONLY','True') #- if set to True, then TIKA_SERVER_JAR is ignored, and relies on the value for TIKA_SERVER_ENDPOINT and treats Tika like a REST client.
 #os.putenv( 'TIKA_TRANSLATOR','org/apache/tika/language/translate/') #- set to the fully qualified class name (defaults to Lingo24) for the Tika translator implementation.
 #os.putenv( 'TIKA_SERVER_CLASSPATH','/home/richard/.m2/repository/org/apache/tika/tika-server/1.13/tika-server-1.13.jar') #- set to a string (delimited by ':' for each additional path) to prepend to the Tika server jar path.
+#os.putenv('TESSDATA_PREFIX','/usr/share/tesseract-ocr/4.00/tessdata/')
 tika.initVM()
 from tika import parser
 parsed = parser.from_buffer("comme çi comme ça")
diff --git a/q9_tensorflow_gpu_test.py b/q9_tensorflow_gpu_test.py
index 84a255b..d5725ce 100644
--- a/q9_tensorflow_gpu_test.py
+++ b/q9_tensorflow_gpu_test.py
@@ -1,17 +1,22 @@
 # Creates a graph.
 import tensorflow as tf
+#from tensorflow.compat import v1 as tf
 
+#sess = tf.InteractiveSession()  
+@tf.function
+def d(a,b):
+    return tf.matmul(a, b)
 
 a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
 b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
-c = tf.matmul(a, b)
+#c = tf.matmul(a, b)
 # Creates a session with log_device_placement set to True.
-sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
-# Runs the op.
-
-tens1 = tf.constant([ [[1,2],[2,3]], [[3,4],[5,6]] ]) 
-print (sess.run(tens1)[1,1,0])
 
-print (sess.run(c))
+# Runs the op.
 
+# tens1 = tf.constant([ [[1,2],[2,3]], [[3,4],[5,6]] ]) 
+# print (sess.run(tens1)[1,1,0])
+# self._sess.run(tf.initialize_all_variables())
+for i in range(100000):
+    d(a,b)
 print ('\n########################### No Errors ####################################')
\ No newline at end of file

From fedce1fcdc5e924bac3f279ccad902fec52dfa88 Mon Sep 17 00:00:00 2001
From: richard lyman <richard.r.lyman@gmail.com>
Date: Fri, 12 Feb 2021 07:36:36 -0800
Subject: [PATCH 6/6] Show versions of installation

---
 README.md | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index b92b17c..7454fb0 100644
--- a/README.md
+++ b/README.md
@@ -21,6 +21,19 @@ run_batch.sc is a bash script that runs all of the programs in the directory
 The script file will create many files in the folder, /tmp/plots.
 
 
-Python 3.4
+Python 3.8
 Anaconda3
 Linux or Windows
+cuda                               11.2.1
+h5py                               2.10.0
+Keras                              2.4.3
+Lasagne                            0.1
+matplotlib                         3.3.2
+numpy                              1.19.2
+pytesseract                        0.3.7
+sklearn                            0.0
+tensorflow                         2.4.1
+tesseract                          4.1.1
+Theano                             1.0.5
+tika                               1.24
+