- Keras在MNIST数据集上实现LeNet-5模型
"""
Created on Tue Jul 10 20:04:03 2018
@author: muli
"""
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
from keras import backend as K
num_classes = 10
img_rows, img_cols = 28, 28
(trainX, trainY), (testX, testY) = mnist.load_data()
if K.image_data_format() == 'channels_first':
trainX = trainX.reshape(trainX.shape[0], 1, img_rows, img_cols)
testX = testX.reshape(testX.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
trainX = trainX.reshape(trainX.shape[0], img_rows, img_cols, 1)
testX = testX.reshape(testX.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
trainX = trainX.astype('float32')
testX = testX.astype('float32')
trainX /= 255.0
testX /= 255.0
trainY = keras.utils.to_categorical(trainY, num_classes)
testY = keras.utils.to_categorical(testY, num_classes)
model = Sequential()
model.add(Conv2D(32, kernel_size=(5, 5), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (5, 5), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(500, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.SGD(),
metrics=['accuracy'])
model.fit(trainX, trainY,
batch_size=128,
epochs=10,
validation_data=(testX, testY))
score = model.evaluate(testX, testY)
print('Test loss:', score[0])
print('Test accuracy:', score[1])