我有张量流的CNN网络 . 我正在保存模型并尝试恢复 . 我读了很多关于保存和恢复模型的建议 . 我能够为前馈网络做到这一点，但CNN证明有点困难 . 以下是代码的一些片段

1. Import the Data (External Source)

2. Main CNN code

def conv_net(a):

    with tf.variable_scope("conv_cell", reuse=tf.AUTO_REUSE):        
        conv11 = tf.layers.conv1d(a, 50, 2, activation=tf.nn.relu, name='conv11')    
        conv1 = tf.layers.max_pooling1d(conv11, 2, 1, name='conv1'
        conv21 = tf.layers.conv1d(conv1, 30, 2, activation=tf.nn.relu, name='conv21')
        conv2 = tf.layers.max_pooling1d(conv21, 2, 1, name='conv2')

        fc11 = tf.contrib.layers.flatten(conv2)
        fc1 = tf.layers.dense(fc11, 180, name='conv3')
        outputs1 = tf.layers.dense(fc1, timesteps/10, name='conv4')
        outputs = tf.reshape(outputs1, [-1, 1])

    return outputs    

Y_Pred1 = conv_net(X)    
Y_Pred = tf.reshape(Y_Pred1, [-1, TimeWindow, num_input])  

Loss = tf.reduce_sum((Y - Y_Pred)**2)
optimizer = tf.train.AdamOptimizer(Learning_rate).minimize(Loss)

3. Main Session

with tf.Session() as sess:
    sess.run(init)
    saver = tf.train.Saver()

------------------------------------------（训练）----- -------------------------------------------

save_path = saver.save(sess, './Save_Network/my_model')
    print("Model saved in file: %s" % save_path)

4. My Question

我不确定，它节省了什么 . 因为我没有单独定义重量和偏差 . 重量和偏差由内置命令“tf.layers.conv1d”内部定义 . 我不知道如何恢复它们 . 我尝试了以下恢复，并按预期不起作用 .

with tf.Session() as sess:
    new_saver = tf.train.import_meta_graph('./Save_Network/my_model.meta')
    new_saver.restore(sess, tf.train.latest_checkpoint('./Save_Network'))

    def conv_net(a):

        conv11 = tf.layers.conv1d(a, 50, 2, activation=tf.nn.relu)    
        conv1 = tf.layers.max_pooling1d(conv11, 2, 1)                  

        conv21 = tf.layers.conv1d(conv1, 30, 2, activation=tf.nn.relu) 
        conv2 = tf.layers.max_pooling1d(conv21, 2, 1)

        fc11 = tf.contrib.layers.flatten(conv2)
        fc1 = tf.layers.dense(fc11, 180)

        outputs1 = tf.layers.dense(fc1, timesteps/10)
        outputs = tf.reshape(outputs1, [-1, 1])

        return outputs    

Y_Pred1 = conv_net(X)    
Y_Pred = tf.reshape(Y_Pred1, [-1, TimeWindow, num_input])

4. Error I get FailedPreconditionError（参见上面的回溯）：尝试使用未初始化的值conv1d / kernel [[Node：conv1d / kernel / read = IdentityT = DT_FLOAT，_device = "/job:localhost/replica:0/task:0/device:CPU:0"]]

  • 我尝试使用init = tf.global_variables_initializer（）sess.run（init）初始化变量

那没用 .

我如何能够访问这些权重和偏见并恢复它们 .

tensorflow restore convolution