python-3.x - 在 TensorFlow 中保存神经网络的学习进度

Question

在 Python 中，Tensorflow：我使用 Tensorflow 训练并应用了一个神经网络，现在我需要保存它的进度以便在以后的时间点进一步训练它。

我使用了很多配置

saver = tf.train.Saver()
saver.restore()
tf.train.import_meta_graph('model/model.ckpt.meta')
tf.train.export_meta_graph('model/model.ckpt.meta')

ETC...

但它总是会产生错误。

这是我的代码。它类似于 Mnist 示例代码，但使用自定义生成的输入并具有单个连续的输出神经元。

x = tf.placeholder('float', [None, 4000]) # 4000 is my structure, just an example
y = tf.placeholder('float') # And I need a single, continuous output

def train_neural_network(x):

    testdata_images, testdata_labels = generate_training_data_batch(size = 10) 
    #Generates test data

    data=[]
    for i in range(how_many_batches):
        data.append(generate_training_data_batch(size = 10))
    #Generates training data


    prediction = neural_network_model(x) 
    # neural_network_model() is defined as a 4000x15x15x10x1 neural network
    cost = tf.reduce_mean( tf.square( tf.subtract(y, prediction) ) )
    optimizer = tf.train.AdamOptimizer(0.01).minimize(cost)

    hm_epochs = 10 
    saver = tf.train.Saver()

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        for epoch in range(hm_epochs):
            epoch_loss = 0

            for i in range(how_many_batches):
                epoch_x, epoch_y = data[i]

                _, c = sess.run([optimizer, cost], feed_dict = {x: epoch_x, y: epoch_y})
                epoch_loss += c


                accuracy1 = tf.subtract(y, prediction)
                result = sess.run(accuracy1, feed_dict={x: epoch_x, y: epoch_y})

                print(result)
                # This is just here so I can see what is going on

        saver.save(sess, 'model/model.ckpt')
        tf.train.export_meta_graph('model/model.ckpt.meta')

    tf.reset_default_graph()

稍后在同一个文件中，我想使用保存的神经网络进行一些预测：

train_neural_network(x)

X, Y = generate_training_data_batch(size = 1)

prediction = neural_network_model(x)

with tf.Session() as sess:

    tf.train.import_meta_graph('model/model.ckpt.meta')
    sess.run(tf.global_variables_initializer())

    thought = sess.run(prediction, feed_dict={x: X})

    print(Y, thought)

使用此版本，我收到错误消息

ValueError: Tensor("Variable:0", shape=(4000, 15), dtype=float32_ref) must be from the same graph as Tensor("Placeholder_35:0", shape=(?, 4000), dtype=float32).

我也收到错误消息，例如

ValueError: At least two variables have the same name: Variable/Adam

我正在寻找这个几周的解决方案，所以我会很欣慰地最终解决这个问题。

标签： python-3.xtensorflowsave