深度學習學習心得-6

CNN CODE

	# 設定所需 library
	import numpy as np
	from keras.models import Sequential
	from keras.layers.core import Dense, Dropout, Activation
	from keras.layers import Conv2D, MaxPooling2D, Flatten
	from keras.optimizers import SGD, Adam
	from keras.utils import np_utils
	from keras.datasets import mnist
	import matplotlib.pyplot as plt

	# categorical_crossentropy
	def load_data():
	(x_train, y_train), (x_test, y_test) = mnist.load_data()
	number = 10000
	x_train = x_train[0:number]
	y_train = y_train[0:number]
	x_train = x_train.reshape(number, 28 , 28,1)
	x_test = x_test.reshape(number, 28 , 28,1)
	x_train = x_train.astype('float32')
	x_test = x_test.astype('float32')
	# convert class vectors to binary class matrices
	y_train = np_utils.to_categorical(y_train, 10)
	y_test = np_utils.to_categorical(y_test, 10)
	x_train = x_train
	x_test = x_test
	# x_test=np.random.normal(x_test)
	x_train = x_train/255
	x_test = x_test/255
	return (x_train, y_train), (x_test, y_test)

	def show_train_history(train_history, train, validation):
	plt.plot(train_history.history[train])
	plt.plot(train_history.history[validation])
	plt.title('Train History')
	plt.ylabel(train)
	plt.xlabel('Epoch')
	plt.legend(['train', 'validation'], loc='upper left')
	plt.show()

	(x_train, y_train), (x_test, y_test) = load_data()
	# define network structure
	model = Sequential()
	model.add(Conv2D(25,(3,3), activation='relu', padding='same',input_shape=(28,28,1)))
	model.add(MaxPooling2D(pool_size = (2, 2),dim_ordering="th"))
	#model.add(Dropout(0.5))
	model.add(Conv2D(50,(3,3), activation='relu', padding='same'))
	model.add(MaxPooling2D(pool_size=(2,2),dim_ordering="th"))
	model.add(Flatten())
	model.add(Dense(units=10, activation='softmax'))

	model.compile(loss='categorical_crossentropy',optimizer='adam', metrics=['acc'])
	train_history = model.fit(x_train, y_train, batch_size=100, epochs=20,validation_split=0.2)

	result = model.evaluate(x_train, y_train)
	print('Train Accuracy:', result[1])
	result = model.evaluate(x_test, y_test)
	print('Test Accuracy:', result[1])

	show_train_history(train_history, 'acc', 'val_acc')
	show_train_history(train_history, 'loss', 'val_loss')

	from keras.models import load_model
	# creates a HDF5 file
	model.save('CNN-1.h5')
	if load_model('CNN-1.h5'):
	print('Yeee~')