保存和读取 TensorFlow 模型
训练一个模型的时间很长。但是你一旦关闭了 TensorFlow session，你所有训练的权重和偏置项都丢失了。如果你计划在之后重新使用这个模型，你需要重新训练！
幸运的是，TensorFlow 可以让你通过一个叫 tf.train.Saver 的类把你的进程保存下来。这个类可以把任何 tf.Variable 存到你的文件系统。
保存模型的代码
# Remove previous Tensors and Operations
# 移除之前的 Tensors 和运算
tf.reset_default_graph()
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
learning_rate = 0.001
n_input = 784 # MNIST 数据输入 (图片尺寸: 28*28)
n_classes = 10 # MNIST 总计类别 (数字 0-9)
# Import MNIST data
# 加载 MNIST 数据
mnist = input_data.read_data_sets('.', one_hot=True)
# Features and Labels
# 特征和标签
features = tf.placeholder(tf.float32, [None, n_input])
labels = tf.placeholder(tf.float32, [None, n_classes])
# Weights & bias
# 权重和偏置项
weights = tf.Variable(tf.random_normal([n_input, n_classes]), name="my_weights_0605")
bias = tf.Variable(tf.random_normal([n_classes]), name="my_bias_0605")
# Logits - xW + b
logits = tf.add(tf.matmul(features, weights), bias)
# Define loss and optimizer
# 定义损失函数和优化器
cost = tf.reduce_mean(\
tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\
.minimize(cost)
# Calculate accuracy
# 计算准确率
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
import math
save_file = './train_model_2018_06_05.ckpt'
batch_size = 128
n_epochs = 20
saver = tf.train.Saver()
# Launch the graph
# 启动图
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# Training cycle
# 训练循环
for epoch in range(n_epochs):
total_batch = int(math.ceil(mnist.train.num_examples / batch_size))
# Loop over all batches
# 遍历所有 batch
for i in range(total_batch):
batch_features, batch_labels = mnist.train.next_batch(batch_size)
sess.run(
optimizer,
feed_dict={features: batch_features, labels: batch_labels})
# Print status for every 10 epochs
# 每运行10个 epoch 打印一次状态
if epoch % 10 == 0:
valid_accuracy = sess.run(
accuracy,
feed_dict={
features: mnist.validation.images,
labels: mnist.validation.labels})
print('Epoch {:<3} - Validation Accuracy: {}'.format(
epoch,
valid_accuracy))
# Save the model
# 保存模型
saver.save(sess, save_file)
print('Trained Model Saved.')
恢复之前保存的模型代码
# Remove the previous weights and bias
# 移除之前的权重和偏置项
tf.reset_default_graph()
n_input = 784 # MNIST 数据输入 (图片尺寸: 28*28)
n_classes = 10 # MNIST 总计类别 (数字 0-9)
# Two Variables: weights and bias
# 两个变量：权重和偏置项
weights = tf.Variable(tf.random_normal([n_input, n_classes]), name="my_weights_0605")
bias = tf.Variable(tf.random_normal([n_classes]), name="my_bias_0605")
save_file = './train_model_2018_06_05.ckpt'
saver = tf.train.Saver()
# Print the name of Weights and Bias
# 打印权重和偏置项的名称
print('Load Weights: {}'.format(weights.name))
print('Load Bias: {}'.format(bias.name))
with tf.Session() as sess:
# Load the weights and bias - No Error
# 加载权重和偏置项 - 没有报错
saver.restore(sess, save_file)
print('Loaded Weights and Bias successfully.')
