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@ -37,7 +37,23 @@ import numpy as np
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learning_rate = 0.001 #学习率
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num_steps = 2000 # 训练步数
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batch_size = 128 # 训练数据批的大小
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# Network Parameters，网络参数
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num_input = 784 # MNIST数据输入 (img shape: 28*28)
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num_classes = 10 # MNIST所有类别 (0-9 digits)
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dropout = 0.75 # Dropout, probability to keep units = (1-p)，保留神经元相应的概率为(1-p)=(1-0.75)=0.25
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# Create the neural network，创建深度神经网络
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def conv_net(x_dict, n_classes, dropout, reuse, is_training):
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# Define a scope for reusing the variables，确定命名空间
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with tf.variable_scope('ConvNet', reuse=reuse):
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# TF Estimator类型的输入为像素
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x = x_dict['images']
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# MNIST数据输入格式为一位向量，包含784个特征 (28*28像素)
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# 用reshape函数改变形状以匹配图像的尺寸 [高 x 宽 x 通道数]
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# 输入张量的尺度为四维: [(每一)批数据的数目, 高，宽，通道数]
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x = tf.reshape(x, shape=[-1, 28, 28, 1])
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# 卷积层，32个卷积核，尺寸为5x5
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