Python Tensorflow-保存和恢复模型是否应在同一程序中？

我在下面的代码中恢复了以前保存的模型。这样对吗？我在某个时候保存了一个模型，当我想要恢复它时，我不需要保存模型，因为我已经保存了一个模型。我的理解正确吗

import tensorflow as tf

    data, labels = cifar_tools.read_data('C:\\Users\\abc\\Desktop\\Testing')

    x = tf.placeholder(tf.float32, [None, 150 * 150])
    y = tf.placeholder(tf.float32, [None, 2])

    w1 = tf.Variable(tf.random_normal([5, 5, 1, 64]))
    b1 = tf.Variable(tf.random_normal([64]))

    w2 = tf.Variable(tf.random_normal([5, 5, 64, 64]))
    b2 = tf.Variable(tf.random_normal([64]))

    w3 = tf.Variable(tf.random_normal([38*38*64, 1024]))
    b3 = tf.Variable(tf.random_normal([1024]))

    w_out = tf.Variable(tf.random_normal([1024, 2]))
    b_out = tf.Variable(tf.random_normal([2]))

    def conv_layer(x,w,b):
        conv = tf.nn.conv2d(x,w,strides=[1,1,1,1], padding = 'SAME')
        conv_with_b = tf.nn.bias_add(conv,b)
        conv_out = tf.nn.relu(conv_with_b)
        return conv_out

    def maxpool_layer(conv,k=2):
        return tf.nn.max_pool(conv, ksize=[1,k,k,1], strides=[1,k,k,1], padding='SAME')

    def model():
        x_reshaped = tf.reshape(x, shape=[-1, 150, 150, 1])

        conv_out1 = conv_layer(x_reshaped, w1, b1)
        maxpool_out1 = maxpool_layer(conv_out1)
        norm1 = tf.nn.lrn(maxpool_out1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
        conv_out2 = conv_layer(norm1, w2, b2)
        norm2 = tf.nn.lrn(conv_out2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
        maxpool_out2 = maxpool_layer(norm2)

        maxpool_reshaped = tf.reshape(maxpool_out2, [-1, w3.get_shape().as_list()[0]])
        local = tf.add(tf.matmul(maxpool_reshaped, w3), b3)
        local_out = tf.nn.relu(local)

        out = tf.add(tf.matmul(local_out, w_out), b_out)
        return out

    model_op = model()

    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(model_op, y))
    train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)

    correct_pred = tf.equal(tf.argmax(model_op, 1), tf.argmax(y,1))
    accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        onehot_labels = tf.one_hot(labels, 2, on_value=1.,off_value=0.,axis=-1)
        onehot_vals = sess.run(onehot_labels)
        batch_size = len(data)
        # Restore model
        saver = tf.train.import_meta_graph('C:\\Users\\abc\\Desktop\\\Testing\\mymodel.meta')
        saver.restore(sess, tf.train.latest_checkpoint('./'))
        tf.add_to_collection("vars", w1)
        tf.add_to_collection("vars", b1)
        all_vars = tf.get_collection('vars')
        for v in all_vars:
            v_ = sess.run(v)
            print(v_)

    for j in range(0, 5):
        print('EPOCH', j)
        for i in range(0, len(data), batch_size):
            batch_data = data[i:i+batch_size, :]
            batch_onehot_vals = onehot_vals[i:i+batch_size, :]
            _, accuracy_val = sess.run([train_op, accuracy], feed_dict={x: batch_data, y: batch_onehot_vals})
            print(i, accuracy_val)

        print('DONE WITH EPOCH')

---

视情况而定。您想更新保存的模型吗？谢谢您的回复。我只想在不更新的情况下恢复保存的模型。那么，以前保存的模型不会消失