expert.py 文件源码

def init_model():
    global x, y

    # Input layer
    x = tf.placeholder(tf.float32, [None, 784])

    # First convolutional layer
    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    x_image = tf.reshape(x, [-1, 28, 28, 1])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)

    # Second convolutional layer
    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)

    # First fully connected layer
    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])
    h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    # Dropout
    keep_prob = tf.placeholder_with_default(1.0, [])
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    # Output layer
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])
    y = tf.matmul(h_fc1_drop, W_fc2) + b_fc2