TensorFlow 训练好模型参数的保存和恢复代码

TensorFlow 训练好模型参数的保存和恢复代码，之前就在想模型不应该每次要个结果都要重新训练一遍吧，应该训练一次就可以一直使用吧。

TensorFlow 提供了 Saver 类，可以进行保存和恢复。下面是 TensorFlow-Examples 项目中提供的保存和恢复代码。

Save and Restore a model using TensorFlow. This example is using the MNIST database of handwritten digits ( http://yann.lecun.com/exdb/mnist/) Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' from __future__ import print_function # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) import tensorflow as tf # Parameters learning_rate = 0.001 batch_size = 100 display_step = 1 model_path = "/tmp/model.ckpt" # Network Parameters n_hidden_1 = 256 # 1st layer number of features n_hidden_2 = 256 # 2nd layer number of features n_input = 784 # MNIST data input (img shape: 28*28) n_classes = 10 # MNIST total classes (0-9 digits) # tf Graph input x = tf.placeholder("float", [None, n_input]) y = tf.placeholder("float", [None, n_classes]) # Create model def multilayer_perceptron(x, weights, biases): # Hidden layer with RELU activation layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) layer_1 = tf.nn.relu(layer_1) # Hidden layer with RELU activation layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) layer_2 = tf.nn.relu(layer_2) # Output layer with linear activation out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] return out_layer # Store layers weight & bias weights = { 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes])) } 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_classes])) } # Construct model pred = multilayer_perceptron(x, weights, biases) # Define loss and optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Initializing the variables init = tf.global_variables_initializer() # 'Saver' op to save and restore all the variables saver = tf.train.Saver() # Running first session print("Starting 1st session...") with tf.Session() as sess: # Initialize variables sess.run(init) # Training cycle for epoch in range(3): avg_cost = 0. total_batch = int(mnist.train.num_examples/batch_size) # Loop over all batches for i in range(total_batch): batch_x, batch_y = mnist.train.next_batch(batch_size) # Run optimization op (backprop) and cost op (to get loss value) _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y}) # Compute average loss avg_cost += c / total_batch # Display logs per epoch step if epoch % display_step == 0: print("Epoch:", '%04d' % (epoch+1), "cost=", \ "{:.9f}".format(avg_cost)) print("First Optimization Finished!") # Test model correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # Calculate accuracy accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})) # Save model weights to disk save_path = saver.save(sess, model_path) print("Model saved in file: %s" % save_path) # Running a new session print("Starting 2nd session...") with tf.Session() as sess: # Initialize variables sess.run(init) # Restore model weights from previously saved model saver.restore(sess, model_path) print("Model restored from file: %s" % save_path) # Resume training for epoch in range(7): avg_cost = 0. total_batch = int(mnist.train.num_examples / batch_size) # Loop over all batches for i in range(total_batch): batch_x, batch_y = mnist.train.next_batch(batch_size) # Run optimization op (backprop) and cost op (to get loss value) _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y}) # Compute average loss avg_cost += c / total_batch # Display logs per epoch step if epoch % display_step == 0: print("Epoch:", '%04d' % (epoch + 1), "cost=", \ "{:.9f}".format(avg_cost)) print("Second Optimization Finished!") # Test model correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # Calculate accuracy accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print("Accuracy:", accuracy.eval( {x: mnist.test.images, y: mnist.test.labels})) 

博客： http://www.tensorflownews.com/2017/07/16/save-and-restore-a-model-using-tensorflow/