Willian-Zhang · May 11, 2018 08:55 · Willian-Zhang · May 11, 2018
diff --git a/mnist-cnn.md b/mnist-cnn.md
diff --git a/mnist-cnn.py b/mnist-cnn.py
 '''Trains a simple convnet on the MNIST dataset.

 Gets to 99.25% test accuracy after 12 epochs
 (there is still a lot of margin for parameter tuning).
 16 seconds per epoch on a GRID K520 GPU.
 '''

 from __future__ import print_function
 import keras
 from keras.datasets import mnist
 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Flatten
 from keras.layers import Conv2D, MaxPooling2D
 from keras import backend as K

 batch_size = 128
 num_classes = 10
 epochs = 12

 # input image dimensions
 img_rows, img_cols = 28, 28

 # the data, shuffled and split between train and test sets
 (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)
    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)
    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')

 # convert class vectors to binary class matrices
 y_train = keras.utils.to_categorical(y_train, num_classes)
 y_test = keras.utils.to_categorical(y_test, num_classes)

 model = Sequential()
 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, activation='relu'))
 model.add(Dropout(0.5))
 model.add(Dense(num_classes, activation='softmax'))

 model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

 model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          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])
	'''Trains a simple convnet on the MNIST dataset.

	Gets to 99.25% test accuracy after 12 epochs
	(there is still a lot of margin for parameter tuning).
	16 seconds per epoch on a GRID K520 GPU.
	'''

	from __future__ import print_function
	import keras
	from keras.datasets import mnist
	from keras.models import Sequential
	from keras.layers import Dense, Dropout, Flatten
	from keras.layers import Conv2D, MaxPooling2D
	from keras import backend as K

	batch_size = 128
	num_classes = 10
	epochs = 12

	# input image dimensions
	img_rows, img_cols = 28, 28

	# the data, shuffled and split between train and test sets
	(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)
	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)
	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')

	# convert class vectors to binary class matrices
	y_train = keras.utils.to_categorical(y_train, num_classes)
	y_test = keras.utils.to_categorical(y_test, num_classes)

	model = Sequential()
	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, activation='relu'))
	model.add(Dropout(0.5))
	model.add(Dense(num_classes, activation='softmax'))

	model.compile(loss=keras.losses.categorical_crossentropy,
	optimizer=keras.optimizers.Adadelta(),
	metrics=['accuracy'])

	model.fit(x_train, y_train,
	batch_size=batch_size,
	epochs=epochs,
	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])