ops.py 文件源码

def conv2d(x, num_kernels, kernel_h=5, kernel_w=5, strides=2, padding="VALID", name="conv2d",
           use_bn=True, activation=tf.nn.relu, alpha=None, is_train=True, stddv=0.02):
    """
    Wrapper function for convolutional layer
    """
    n, h, w, c = x.get_shape().as_list()
    with tf.variable_scope(name):
        w = tf.get_variable(name="weight", initializer=tf.truncated_normal_initializer(stddev=stddv),
                            shape=(kernel_h, kernel_w, c, num_kernels))
        bias = tf.get_variable(name="bias", initializer=tf.constant_initializer(0.01), shape=num_kernels)
        y = tf.nn.conv2d(x, w, (1, strides, strides, 1), padding)
        y = tf.nn.bias_add(y, bias)

        if use_bn:
            y = batch_norm(y, tf.get_variable_scope().name, is_train)

        print("Convolutional 2D Layer %s, kernel size %s, output size %s Reuse:%s"
              % (tf.get_variable_scope().name, (kernel_h, kernel_w, c, num_kernels), y.get_shape().as_list(),
                 tf.get_variable_scope().reuse))
        if alpha is None:
            y = activation(y)
        else:
            y = activation(y, alpha)
    return y