Update 2 mnist examples

examples/mnist_cnn.py

@@ -9,74 +9,62 @@ from __future__ import print_function
 import numpy as np
 np.random.seed(1337)  # for reproducibility
 
+import keras
 from keras.datasets import mnist
 from keras.models import Sequential
-from keras.layers import Dense, Dropout, Activation, Flatten
-from keras.layers import Convolution2D, MaxPooling2D
-from keras.utils import np_utils
+from keras.layers import Dense, Dropout, Flatten
+from keras.layers import Conv2D, MaxPooling2D
 from keras import backend as K
 
 batch_size = 128
-nb_classes = 10
-nb_epoch = 12
+num_classes = 10
+num_epoch = 12
 
 # input image dimensions
 img_rows, img_cols = 28, 28
-# number of convolutional filters to use
-nb_filters = 32
-# size of pooling area for max pooling
-pool_size = (2, 2)
-# convolution kernel size
-kernel_size = (3, 3)
 
 # the data, shuffled and split between train and test sets
-(X_train, y_train), (X_test, y_test) = mnist.load_data()
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
 
 if K.image_data_format() == 'channels_first':
-    X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
-    X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
+    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
+    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
     input_shape = (1, img_rows, img_cols)
 else:
-    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
-    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
+    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
+    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
     input_shape = (img_rows, img_cols, 1)
 
-X_train = X_train.astype('float32')
-X_test = X_test.astype('float32')
-X_train /= 255
-X_test /= 255
-print('X_train shape:', X_train.shape)
-print(X_train.shape[0], 'train samples')
-print(X_test.shape[0], 'test samples')
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
+print('x_train shape:', x_train.shape)
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
 
 # convert class vectors to binary class matrices
-Y_train = np_utils.to_categorical(y_train, nb_classes)
-Y_test = np_utils.to_categorical(y_test, nb_classes)
+y_train = keras.utils.np_utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.np_utils.to_categorical(y_test, num_classes)
 
 model = Sequential()
-
-model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
-                        border_mode='valid',
-                        input_shape=input_shape))
-model.add(Activation('relu'))
-model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
-model.add(Activation('relu'))
-model.add(MaxPooling2D(pool_size=pool_size))
+model.add(Conv2D(32, kernel_size=(3, 3),
+                 activation='relu',
+                 input_shape=input_shape))
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Dropout(0.25))
-
 model.add(Flatten())
-model.add(Dense(128))
-model.add(Activation('relu'))
+model.add(Dense(128, activation='relu'))
 model.add(Dropout(0.5))
-model.add(Dense(nb_classes))
-model.add(Activation('softmax'))
+model.add(Dense(num_classes, activation='softmax'))
 
-model.compile(loss='categorical_crossentropy',
-              optimizer='adadelta',
+model.compile(loss=keras.losses.categorical_crossentropy,
+              optimizer=keras.optimizers.Adadelta(),
               metrics=['accuracy'])
 
-model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
-          verbose=1, validation_data=(X_test, Y_test))
-score = model.evaluate(X_test, Y_test, verbose=0)
-print('Test score:', score[0])
+model.fit(x_train, y_train, batch_size=batch_size, num_epoch=num_epoch,
+          verbose=1, validation_data=(x_test, y_test))
+score = model.evaluate(x_test, y_test, verbose=0)
+print('Test loss:', score[0])
 print('Test accuracy:', score[1])

examples/mnist_mlp.py

@@ -11,40 +11,37 @@ np.random.seed(1337)  # for reproducibility
 
 from keras.datasets import mnist
 from keras.models import Sequential
-from keras.layers.core import Dense, Dropout, Activation
+from keras.layers import Dense, Dropout, Activation
 from keras.optimizers import RMSprop
 from keras.utils import np_utils
 
 
 batch_size = 128
-nb_classes = 10
-nb_epoch = 20
+num_classes = 10
+num_epoch = 20
 
 # the data, shuffled and split between train and test sets
-(X_train, y_train), (X_test, y_test) = mnist.load_data()
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
 
-X_train = X_train.reshape(60000, 784)
-X_test = X_test.reshape(10000, 784)
-X_train = X_train.astype('float32')
-X_test = X_test.astype('float32')
-X_train /= 255
-X_test /= 255
-print(X_train.shape[0], 'train samples')
-print(X_test.shape[0], 'test samples')
+x_train = x_train.reshape(60000, 784)
+x_test = x_test.reshape(10000, 784)
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
 
 # convert class vectors to binary class matrices
-Y_train = np_utils.to_categorical(y_train, nb_classes)
-Y_test = np_utils.to_categorical(y_test, nb_classes)
+y_train = np_utils.to_categorical(y_train, num_classes)
+y_test = np_utils.to_categorical(y_test, num_classes)
 
 model = Sequential()
-model.add(Dense(512, input_shape=(784,)))
-model.add(Activation('relu'))
+model.add(Dense(512, activation='relu', input_shape=(784,)))
 model.add(Dropout(0.2))
-model.add(Dense(512))
-model.add(Activation('relu'))
+model.add(Dense(512, activation='relu'))
 model.add(Dropout(0.2))
-model.add(Dense(10))
-model.add(Activation('softmax'))
+model.add(Dense(10, activation='softmax'))
 
 model.summary()
 
@@ -52,9 +49,9 @@ model.compile(loss='categorical_crossentropy',
               optimizer=RMSprop(),
               metrics=['accuracy'])
 
-history = model.fit(X_train, Y_train,
-                    batch_size=batch_size, nb_epoch=nb_epoch,
-                    verbose=1, validation_data=(X_test, Y_test))
-score = model.evaluate(X_test, Y_test, verbose=0)
-print('Test score:', score[0])
+history = model.fit(x_train, y_train,
+                    batch_size=batch_size, num_epoch=num_epoch,
+                    verbose=1, validation_data=(x_test, y_test))
+score = model.evaluate(x_test, y_test, verbose=0)
+print('Test loss:', score[0])
 print('Test accuracy:', score[1])