updated

Author: MorvanZhou

kerasTUT/6-CNN_example.py

@@ -37,29 +37,29 @@
 
 # Conv layer 1 output shape (32, 28, 28)
 model.add(Convolution2D(
-    nb_filter=32,
-    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
+    batch_input_shape=(64, 1, 28, 28),
+    filters=32,
+    kernel_size=5,
+    strides=1,
+    padding='same',     # Padding method
+    data_format='channels_first',
 ))
 model.add(Activation('relu'))
 
 # Pooling layer 1 (max pooling) output shape (32, 14, 14)
 model.add(MaxPooling2D(
-    pool_size=(2, 2),
-    strides=(2, 2),
-    border_mode='same',    # Padding method
+    pool_size=2,
+    strides=2,
+    padding='same',    # Padding method
+    data_format='channels_first',
 ))
 
 # Conv layer 2 output shape (64, 14, 14)
-model.add(Convolution2D(64, 5, 5, border_mode='same'))
+model.add(Convolution2D(64, 5, strides=1, padding='same', data_format='channels_first'))
 model.add(Activation('relu'))
 
 # Pooling layer 2 (max pooling) output shape (64, 7, 7)
-model.add(MaxPooling2D(pool_size=(2, 2), border_mode='same'))
+model.add(MaxPooling2D(2, 2, 'same', data_format='channels_first'))
 
 # Fully connected layer 1 input shape (64 * 7 * 7) = (3136), output shape (1024)
 model.add(Flatten())
@@ -80,7 +80,7 @@
 
 print('Training ------------')
 # Another way to train the model
-model.fit(X_train, y_train, epochs=1, batch_size=32,)
+model.fit(X_train, y_train, epochs=1, batch_size=64,)
 
 print('\nTesting ------------')
 # Evaluate the model with the metrics we defined earlier