Train model

.gitignore

@@ -8,3 +8,5 @@ papers/*
 data/train/characters/dataset.csv
 requirement.txt
 GettingStart.ipynb
+log_train
+__pycache__

checkpoint

@@ -0,0 +1,2 @@
+model_checkpoint_path: "viet_ocr_brain.ckpt"
+all_model_checkpoint_paths: "viet_ocr_brain.ckpt"

model.py

@@ -158,7 +158,7 @@ def train(self, learning_rate, training_epochs, batch_size, keep_prob):
         print('Training finished!')
 
         saver = tf.train.Saver()
-        save_path = saver.save(self.sess, "viet_ocr_brain.ckpt")
+        save_path = saver.save(self.sess, "./viet_ocr_brain.ckpt")
         print("Trainned model is saved in file: %s" % save_path)
 
     def evaluate(self, batch_size, keep_prob):

predict.py

@@ -27,17 +27,18 @@ def predict(character_image):
     return (probs[0, idx], idx)
 
 
-ds = DataSet(test_prob=1, one_hot=False)
-characters = DataGenerator().get_list_characters()
+if __name__ == '__main__':
+    ds = DataSet(test_prob=1, one_hot=False)
+    characters = DataGenerator().get_list_characters()
 
-x, y = ds.next_batch_test(1)
+    x, y = ds.next_batch_test(1)
 
-print('x.shape', x.shape)
-print('y.shape', y.shape)
+    print('x.shape', x.shape)
+    print('y.shape', y.shape)
 
-prob, idx = predict(x)
+    prob, idx = predict(x)
 
-print('Input character: ', characters[int(y[0])])
-print('Predicted: ', characters[idx], ' with probability = ', prob, '%')
-print('Result: ', characters[int(y[0])] == characters[idx])
-print('-' * 10)
+    print('Input character: ', characters[int(y[0])])
+    print('Predicted: ', characters[idx], ' with probability = ', prob, '%')
+    print('Result: ', characters[int(y[0])] == characters[idx])
+    print('-' * 10)

requirements.txt

@@ -0,0 +1,23 @@
+absl-py==0.1.12
+astor==0.6.2
+bleach==1.5.0
+certifi==2018.1.18
+cycler==0.10.0
+gast==0.2.0
+grpcio==1.10.0
+html5lib==0.9999999
+kiwisolver==1.0.1
+Markdown==2.6.11
+matplotlib==2.2.2
+numpy==1.14.2
+pandas==0.22.0
+Pillow==5.0.0
+protobuf==3.5.2.post1
+pyparsing==2.2.0
+python-dateutil==2.7.1
+pytz==2018.3
+six==1.11.0
+tensorboard==1.6.0
+tensorflow==1.6.0
+termcolor==1.1.0
+Werkzeug==0.14.1