我在重新整理模型时遇到问题。我训练了模型并使用此代码保存了模型。我不太确定这是否是正确的方法，我将不胜感激。当我尝试还原模型时会发生问题。我只需要预测，就不会再接受过培训了。从模型中恢复参数需要花费很多时间。在我仅需要预测的前提下，如何改进模型保护程序或模型恢复程序以使其快速完成。

X = tf.placeholder(tf.float32, [None, 56, 56, 1])
Y_ = tf.placeholder(tf.float32, [None, 36])

L1 = 432
L2 = 72
L3 = 36

W1 = tf.Variable(tf.truncated_normal([3136, L1], stddev=0.1))
b1 = tf.Variable(tf.zeros([L1]))
W2 = tf.Variable(tf.truncated_normal([L1, L2], stddev=0.1))
b2 = tf.Variable(tf.zeros([L2]))
W3 = tf.Variable(tf.truncated_normal([L2, L3], stddev=0.1))
b3 = tf.Variable(tf.zeros([L3]))

XX = tf.reshape(X, [-1, 3136])

Y1 = tf.nn.sigmoid(tf.matmul(XX, W1) + b1)
Y1 = tf.nn.dropout(Y1, keep_prob=0.8)
Y2 = tf.nn.sigmoid(tf.matmul(Y1, W2) + b2)
Y2 = tf.nn.dropout(Y2, keep_prob=0.8)
Ylogits = tf.matmul(Y2, W3) + b3
Y = tf.nn.softmax(Ylogits)

cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=Ylogits, labels=Y_)
cross_entropy = tf.reduce_mean(cross_entropy) * 100
correct_prediction = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
train_step = tf.train.GradientDescentOptimizer(0.0001).minimize(cross_entropy)

allweights = tf.concat([tf.reshape(W1, [-1]), tf.reshape(W2, [-1]), tf.reshape(W3, [-1])], 0)
allbiases = tf.concat([tf.reshape(b1, [-1]), tf.reshape(b2, [-1]), tf.reshape(b3, [-1])], 0)

init = tf.global_variables_initializer()

saver = tf.train.Saver()

def next_batch(x, y, batch, step):
    x_temp = x[cur_step:(step+batch)]
    y_temp = np.squeeze(y[step:(step + batch)])
    return x_temp, y_temp


with tf.Session() as sess:
    sess.run(init)
    cur_step = 0
    for i in range(NUM_ITERS + 1):
        batch_X, batch_Y = next_batch(train_xx, train_yy, BATCH, cur_step)
        if i % DISPLAY_STEP == 0:
            acc_trn, loss_trn, w, b = sess.run([accuracy, cross_entropy, allweights, allbiases], feed_dict={X: batch_X, Y_: batch_Y})
            acc_tst, loss_tst = sess.run([accuracy, cross_entropy], feed_dict={X: test_xx, Y_: test_yy})

        sess.run(train_step, feed_dict={X: batch_X, Y_: batch_Y})
    save_path = saver.save(sess, "abc/model")

恢复：

X = tf.placeholder(tf.float32, [None, 56, 56, 1])
Y_ = tf.placeholder(tf.float32, [None, 36])

L1 = 432
L2 = 72
L3 = 36

W1 = tf.Variable(tf.truncated_normal([3136, L1], stddev=0.1))
b1 = tf.Variable(tf.zeros([L1]))

W2 = tf.Variable(tf.truncated_normal([L1, L2], stddev=0.1))
b2 = tf.Variable(tf.zeros([L2]))

W3 = tf.Variable(tf.truncated_normal([L2, L3], stddev=0.1))
b3 = tf.Variable(tf.zeros([L3]))


XX = tf.reshape(X, [-1, 3136])

Y1 = tf.nn.sigmoid(tf.matmul(XX, W1) + b1)
Y1 = tf.nn.dropout(Y1, keep_prob=0.8)
Y2 = tf.nn.sigmoid(tf.matmul(Y1, W2) + b2)
Y2 = tf.nn.dropout(Y2, keep_prob=0.8)
Ylogits = tf.matmul(Y2, W3) + b3
Y = tf.nn.softmax(Ylogits)

with tf.Session() as sess:
    saver = tf.train.Saver()
    saver = tf.train.import_meta_graph('model.meta')
    saver.restore(sess, 'model')

编辑：也许使用Google Colab的GPU训练模型，然后将其还原到我的PC上这一事实很重要。