如何在 Tensorflow 上使用保存的模型保存模型、加载和预测？

Question

我是 python 和 Tensorflow 神经网络的新手，所以我不知道如何恢复保存的模型并进行预测。我从线跟随机器人的三个摄像头（X_test）收集了数据，它的轮速（左右）由模拟器为每个摄像头视图（y_test）设置。我需要训练一个神经网络来根据相机视图预测车轮速度。代码如下：

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

x_train = np.zeros((2392,3072))
y_train = np.zeros((2392,2))
x_test = np.zeros((2392,3072))
y_test = np.zeros((2392,2))

X_1_train = np.loadtxt('X_Treinamento_3.txt')
X_2_train = np.loadtxt('X_Treinamento_4.txt')
y_1_train = np.loadtxt('Vl_Vr_Treinamento_3.txt')
y_2_train = np.loadtxt('Vl_Vr_Treinamento_4.txt')

X_1_test = np.loadtxt('X_Teste_3.txt')
X_2_test = np.loadtxt('X_Teste_4.txt')
y_1_test = np.loadtxt('Vl_Vr_Teste_3.txt')
y_2_test = np.loadtxt('Vl_Vr_Teste_4.txt')

x_train = np.concatenate((X_1_train[4:1200,:],X_2_train[4:1200,:]))/255
x_test = np.concatenate((X_1_test[4:1200,:],X_2_test[4:1200,:]))/255
y_train = np.concatenate((y_1_train[4:1200,:],y_2_train[4:1200,:]))
y_test = np.concatenate((y_1_test[4:1200,:],y_2_test[4:1200,:]))

n_inputs = 32*32*3 #3 images 32x32 pixels
n_hidden_1 = 300 # 1st layer number of neurons
n_hidden_2 = 100 # 2nd layer number of neurons
n_outputs = 2

tf.compat.v1.disable_eager_execution()
X = tf.compat.v1.placeholder("float",[None,n_inputs])
Y = tf.compat.v1.placeholder("float",[None,n_outputs])

weights = {
    'h1': tf.Variable(tf.random.normal([n_inputs, n_hidden_1],stddev=0.1)),#4 inputs 10  nodes in h1 layer
    'h2': tf.Variable(tf.random.normal([n_hidden_1, n_hidden_2],stddev=0.1)),# 10 nodes in h2 layer
    'out': tf.Variable(tf.random.normal([n_hidden_2, n_outputs],stddev=0.1))# 1 ouput label
}
biases = {
    'b1': tf.Variable(tf.random.normal([n_hidden_1])),
    'b2': tf.Variable(tf.random.normal([n_hidden_2])),
    'out': tf.Variable(tf.random.normal([n_outputs]))
}

def neural_net(x):
    #hidden layer 1
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.tanh(layer_1)#activation
    #hideen layer 2
    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
    layer_2 = tf.nn.tanh(layer_2)#activation
    # output layer
    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
    return (out_layer)

Y_hat=neural_net(X)
loss_op=tf.losses.mean_squared_error(Y,Y_hat)#loss function
optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=1e-3)  # define optimizer # play around with learning rate
train_op = optimizer.minimize(loss_op)  # minimize losss

init = tf.compat.v1.global_variables_initializer()
epoch=2000

with tf.compat.v1.Session() as sess:
    sess.run(init)
    saver = tf.compat.v1.train.Saver()
    for i in range(0,epoch):
        sess.run(train_op,feed_dict={X:x_train,Y:y_train})
        loss=sess.run(loss_op,feed_dict={X:x_train,Y:y_train})
        if(i%100==0):
            print("epoch no "+str(i),(loss))
        pred=sess.run(Y_hat,feed_dict={X:x_test})
        saver.save(sess, "output")

所以，我需要您的帮助来检查我是否正确保存了模型，如果没有，如何保存它以及如何加载和使用保存的模型进行预测。

Answer 1

您正确保存它，但如果您想保存更多检查点（例如：可能最后一个检查点过度拟合，而前一个检查点将提供更好的测试数据结果），而不是您需要添加 global_step （您的检查点将被保存为output-0, output-2000 等等），更多信息请看这里docs

saver.save(sess, "output", global_step = i)

在您的情况下加载和进行预测将如下所示：

with tf.compat.v1.Session() as sess:
    sess.run(init)
    saver = tf.compat.v1.train.Saver()
    saver.restore(sess, "output")
    pred=sess.run(Y_hat,feed_dict={X:x_test})

“输出”是您保存的检查点的路径