misc commits

Author: rrlyman

15-01-01 459_Mont_Lyman.jpg

@@ -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

README.md

@@ -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 ####################################')
+