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# -*- coding: utf-8 -*- import numpy as np import tensorflow as tf # 导入 MINST 数据集 from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) n_input = 28 # MNIST data 输入 (img shape: 28*28) n_steps = 28 # timesteps n_hidden = 128 # hidden layer num of features n_classes = 10 # MNIST 列别 (0-9 ,一共10类) batch_size = 128 tf.reset_default_graph() # tf Graph input x = tf.placeholder("float", [None, n_steps, n_input]) y = tf.placeholder("float", [None, n_classes]) stacked_rnn = [] for i in range(3): stacked_rnn.append(tf.contrib.rnn.LSTMCell(n_hidden)) mcell = tf.contrib.rnn.MultiRNNCell(stacked_rnn) x1 = tf.unstack(x, n_steps, 1) outputs, states = tf.contrib.rnn.static_rnn(mcell, x1, dtype=tf.float32) pred = tf.contrib.layers.fully_connected(outputs[-1],n_classes,activation_fn = None) learning_rate = 0.001 # 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) # Evaluate model correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) training_iters = 100000 display_step = 10 # 启动session with tf.Session() as sess: sess.run(tf.global_variables_initializer()) step = 1 # Keep training until reach max iterations while step * batch_size < training_iters: batch_x, batch_y = mnist.train.next_batch(batch_size) # Reshape data to get 28 seq of 28 elements batch_x = batch_x.reshape((batch_size, n_steps, n_input)) # Run optimization op (backprop) sess.run(optimizer, feed_dict={x: batch_x, y: batch_y}) if step % display_step == 0: # 计算批次数据的准确率 acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y}) # Calculate batch loss loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y}) print ("Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc)) step += 1 print (" Finished!") # 计算准确率 for 128 mnist test images test_len = 100 test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input)) test_label = mnist.test.labels[:test_len] print ("Testing Accuracy:", sess.run(accuracy, feed_dict={x: test_data, y: test_label}))
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