myUtils.py 文件源码

def conv(inputs, kernel_shape, bias_shape, strides, w_i, b_i=None, activation=tf.nn.relu):
    # ??tf.layers
    # relu1 = tf.layers.conv2d(input_imgs, filters=24, kernel_size=[5, 5], strides=[2, 2],
    #                          padding='SAME', activation=tf.nn.relu,
    #                          kernel_initializer=w_i, bias_initializer=b_i)
    weights = tf.get_variable('weights', shape=kernel_shape, initializer=w_i)
    conv = tf.nn.conv2d(inputs, weights, strides=strides, padding='SAME')
    if bias_shape is not None:
        biases = tf.get_variable('biases', shape=bias_shape, initializer=b_i)
        return activation(conv + biases) if activation is not None else conv + biases
    return activation(conv) if activation is not None else conv


# ???bias??????relu