tuple_rect_SNR.py 文件源码

def Affine(name_scope,input_tensor,out_channels, relu=True, init_sess=None):
    input_shape = input_tensor.get_shape().as_list()
    input_channels = input_shape[-1]
    with tf.name_scope(name_scope):
        weights = tf.Variable(
            tf.truncated_normal([input_channels, out_channels],
                                stddev=1.0 / math.sqrt(float(input_channels))),name='weights')
        biases = tf.Variable(tf.zeros([out_channels]),name='biases')
        if init_sess is not None: init_sess.run(tf.initialize_variables([weights,biases]))
        if relu: return tf.nn.relu(tf.matmul(input_tensor, weights) + biases)
        else: return tf.matmul(input_tensor, weights) + biases