nn.py 文件源码

def conv(inp, name, filter_size, out_channels, stride=1,
         padding='SAME', nonlinearity=None, init_scale=1.0, dilation=None):
    """Convolutional layer.
    If tf.GLOBAL['init'] is true, this creates the layers paramenters (g, b, W) : L(x) = g|W| (*) x + b

    Args:
      x: input tensor
      name (str): variable scope name
      filter_size (int pair): filter size
      out_channels (int): number of output channels
      strid (int): horizontal and vertical stride
      padding (str): padding mode
      nonlinearity (func): activation function
      init_scale: initial scale for the weights and bias variables
      dilation: optional dilation rate
    """
    with tf.variable_scope(name):
        strides = [1, stride, stride, 1]
        in_channels = inp.get_shape().as_list()[3]

        if tf.GLOBAL['init']:
            V = get_variable('V', shape=tuple(filter_size) + (in_channels, out_channels), dtype=tf.float32,
                             initializer=tf.random_normal_initializer(0, 0.05), trainable=True)
            V_norm = tf.nn.l2_normalize(V.initialized_value(), [0, 1, 2])
            if dilation is None:
                out = tf.nn.conv2d(inp, V_norm, strides, padding)
            else:
                assert(stride == 1)
                out = tf.nn.atrous_conv2d(inp, V_norm, dilation, padding)
            m_init, v_init = tf.nn.moments(out, [0, 1, 2])
            scale_init = init_scale / tf.sqrt(v_init + 1e-8)
            g = get_variable('g', shape=None, dtype=tf.float32, initializer=scale_init, trainable=True, regularizer=tf.contrib.layers.l2_regularizer(tf.GLOBAL['reg']))
            b = get_variable('b', shape=None, dtype=tf.float32, initializer=-m_init * scale_init, trainable=True, regularizer=tf.contrib.layers.l2_regularizer(tf.GLOBAL['reg']))
            out = tf.reshape(scale_init, [1, 1, 1, out_channels]) * (out - tf.reshape(m_init, [1, 1, 1, out_channels]))
            if nonlinearity is not None:
                out = nonlinearity(out)

        else:
            V, g, b = get_variable('V'), get_variable('g'), get_variable('b')
            tf.assert_variables_initialized([V, g, b])
            W = g[None, None, None] * tf.nn.l2_normalize(V, [0, 1, 2])
            if dilation is None:
                out = tf.nn.conv2d(inp, W, strides, padding) + b[None, None, None]
            else:
                assert(stride == 1)
                out = tf.nn.atrous_conv2d(inp, W, dilation, padding) + b[None, None, None]
            if nonlinearity is not None:
                out = nonlinearity(out)

        return out