tf_redshifts.py 文件源码

def add_layer(inputs, in_size, out_size, n_layer, activation_function=None):
    # add one more layer and return the output of this layer
    layer_name = 'layer%s' % n_layer
    with tf.variable_scope(layer_name):
        with tf.variable_scope('weights'):
            Weights = tf.get_variable(shape=[in_size, out_size], name='W', initializer=xavier_initializer())
            tf.histogram_summary(layer_name + '/weights', Weights)
        with tf.variable_scope('biases'):
            biases = tf.get_variable(shape=[1, out_size], name='b', initializer=xavier_initializer())
            tf.histogram_summary(layer_name + '/biases', biases)
        with tf.variable_scope('Wx_plus_b'):
            Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b, )
        tf.histogram_summary(layer_name + '/outputs', outputs)
        return outputs


# Make up some real data