fix compatibility of tensorflow

Author: MorvanZhou

kerasTUT/6-CNN_example.py

@@ -10,6 +10,10 @@
 
 # 6 - CNN example
 
+# to try tensorflow, un-comment following two lines
+# import os
+# os.environ['KERAS_BACKEND']='tensorflow'
+
 import numpy as np
 np.random.seed(1337)  # for reproducibility
 from keras.datasets import mnist
@@ -23,8 +27,8 @@
 (X_train, y_train), (X_test, y_test) = mnist.load_data()
 
 # data pre-processing
-X_train = X_train.reshape(-1, 1, 28, 28)
-X_test = X_test.reshape(-1, 1, 28, 28)
+X_train = X_train.reshape(-1, 1,28, 28)
+X_test = X_test.reshape(-1, 1,28, 28)
 y_train = np_utils.to_categorical(y_train, nb_classes=10)
 y_test = np_utils.to_categorical(y_test, nb_classes=10)
 
@@ -37,8 +41,9 @@
     nb_row=5,
     nb_col=5,
     border_mode='same',     # Padding method
+    dim_ordering='th',      # if use tensorflow, to set the input dimension order to theano ("th") style, but you can change it.
     input_shape=(1,         # channels
-                 28, 28)    # height & width
+                 28, 28,)    # height & width
 ))
 model.add(Activation('relu'))
 

kerasTUT/7-RNN_Classifier_example.py

@@ -10,6 +10,10 @@
 
 # 8 - RNN Classifier example
 
+# to try tensorflow, un-comment following two lines
+# import os
+# os.environ['KERAS_BACKEND']='tensorflow'
+
 import numpy as np
 np.random.seed(1337)  # for reproducibility
 
@@ -43,8 +47,11 @@
 
 # RNN cell
 model.add(SimpleRNN(
-    batch_input_shape=(BATCH_SIZE, TIME_STEPS, INPUT_SIZE),       # Or: input_dim=INPUT_SIZE, input_length=TIME_STEPS,
+    # for batch_input_shape, if using tensorflow as the backend, we have to put None for the batch_size.
+    # Otherwise, model.evaluate() will get error.
+    batch_input_shape=(None, TIME_STEPS, INPUT_SIZE),       # Or: input_dim=INPUT_SIZE, input_length=TIME_STEPS,
     output_dim=CELL_SIZE,
+    unroll=True,
 ))
 
 # output layer

kerasTUT/8-RNN_LSTM_Regressor_example.py

@@ -10,6 +10,9 @@
 
 # 8 - RNN LSTM Regressor example
 
+# to try tensorflow, un-comment following two lines
+# import os
+# os.environ['KERAS_BACKEND']='tensorflow'
 import numpy as np
 np.random.seed(1337)  # for reproducibility
 import matplotlib.pyplot as plt

kerasTUT/9-Autoencoder_example.py

@@ -10,6 +10,9 @@
 
 # 9 - Autoencoder example
 
+# to try tensorflow, un-comment following two lines
+# import os
+# os.environ['KERAS_BACKEND']='tensorflow'
 import numpy as np
 np.random.seed(1337)  # for reproducibility