ops.py 文件源码

def linear(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False):
    shape = input_.get_shape().as_list()

    #mat_shape=tf.stack([tf.shape(input_)[1],output_size])
    mat_shape=[shape[1],output_size]

    with tf.variable_scope(scope or "Linear"):
        #matrix = tf.get_variable("Matrix", [shape[1], output_size], tf.float32,
        matrix = tf.get_variable("Matrix", mat_shape, tf.float32,
                     tf.random_normal_initializer(stddev=stddev))
        bias = tf.get_variable("bias", [output_size],
                   initializer=tf.constant_initializer(bias_start))
        if with_w:
            return tf.matmul(input_, matrix) + bias, matrix, bias
        else:
            return tf.matmul(input_, matrix) + bias


#minibatch method that improves on openai
#because it doesn't fix batchsize:
#TODO: recheck when not sleepy