q5_tensorflow_residual.py

"""# ==========================================================================

# 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 

The tutorial version of the program is modified in order to send some
features directly to the fully connected layer, thus bypassing the
convolution layer.

It has TWO convolution layers and THREE fully connected (2048 node) layers

Images go through convolution.  Everything else bypasses.

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'):    

    
if False:
    # 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': 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']    
    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

    # 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],)}   
        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 ####################################')