吴裕雄--天生自然TensorFlow高层封装:使用TFLearn处理MNIST数据集实现LeNet-5模型
作者:互联网
# 1. 通过TFLearn的API定义卷机神经网络。 import tflearn import tflearn.datasets.mnist as mnist from tflearn.layers.conv import conv_2d, max_pool_2d from tflearn.layers.estimator import regression from tflearn.layers.core import input_data, dropout, fully_connected trainX, trainY, testX, testY = mnist.load_data(data_dir="F:\\TensorFlowGoogle\\201806-github\\datasets\\MNIST_data", one_hot=True) # 将图像数据resize成卷积卷积神经网络输入的格式。 trainX = trainX.reshape([-1, 28, 28, 1]) testX = testX.reshape([-1, 28, 28, 1]) # 构建神经网络。 net = input_data(shape=[None, 28, 28, 1], name='input') net = conv_2d(net, 32, 5, activation='relu') net = max_pool_2d(net, 2) net = conv_2d(net, 64, 5, activation='relu') net = max_pool_2d(net, 2) net = fully_connected(net, 500, activation='relu') net = fully_connected(net, 10, activation='softmax') # 定义学习任务。指定优化器为sgd,学习率为0.01,损失函数为交叉熵。 net = regression(net, optimizer='sgd', learning_rate=0.01,loss='categorical_crossentropy')
# 2. 通过TFLearn的API训练神经网络。 # 通过定义的网络结构训练模型,并在指定的验证数据上验证模型的效果。 model = tflearn.DNN(net, tensorboard_verbose=0) model.fit(trainX, trainY, n_epoch=10,validation_set=([testX, testY]),show_metric=True)
标签:import,TFLearn,28,2d,tflearn,LeNet,TensorFlow,net,data 来源: https://www.cnblogs.com/tszr/p/12070148.html