import tensorflow as tf


        h_conv1 = self.conv2d('h_conv1', s, W_conv1, 4, b_conv1)  # 构造第一个卷积层输出为conv1
        h_pool1 = self.max_pool_2x2('h_pool1', h_conv1)

        h_conv2 = self.conv2d('h_conv2', h_pool1, W_conv2, 2, b_conv2)

        h_conv3 = self.conv2d('h_conv3', h_conv2, W_conv3, 1, b_conv3)

        h_conv3_flat = tf.reshape(h_conv3, [-1, 1600], 'h_conv3_flat')
        h_fc1 = tf.nn.relu(tf.add(tf.matmul(h_conv3_flat, W_fc1), b_fc1, 'h_fc1'))

        readout = tf.add(tf.matmul(h_fc1, W_fc2), b_fc2, 'h_fc2')

        return s, readout, h_fc1

    def creat_optimizer(self,readout):
