TensorFlow——LSTM長短期記憶神經網路處理Mnist資料集
阿新 • • 發佈:2018-12-30
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data from tensorflow.contrib import rnn # 載入資料集 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # 輸入圖片是28*28 n_inputs = 28 # 輸入一行,一行有28個數據(28個畫素點),即輸入序列長度為28 max_time = 28 # 一共28行 lstm_size = 100 # 隱層單元 n_classes = 10 # 10個分類 batch_size = 50 # 每批次50個樣本 n_batch = mnist.train.num_examples // batch_size # 計算一共有多少個批次 # 這裡的none表示第一個維度可以是任意的長度 x = tf.placeholder(tf.float32, [None, 784]) # 正確的標籤 y = tf.placeholder(tf.float32, [None, 10]) # 初始化權值 weights = tf.Variable(tf.truncated_normal([lstm_size, n_classes], stddev=0.1)) # 初始化偏置值 biases = tf.Variable(tf.constant(0.1, shape=[n_classes])) # 定義RNN網路 def RNN(X, weights, biases): inputs = tf.reshape(X, [-1, max_time, n_inputs]) # 定義LSTM基本CELL lstm_cell = rnn.BasicLSTMCell(lstm_size) # final_state[0]是cell state # final_state[1]是hidden_state outputs, final_state = tf.nn.dynamic_rnn(lstm_cell, inputs, dtype=tf.float32) results = tf.nn.softmax(tf.matmul(final_state[1], weights) + biases) return results # 計算RNN的返回結果 prediction = RNN(x, weights, biases) # 損失函式 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y)) # 使用AdamOptimizer進行優化 train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) # 結果存放在一個布林型列表中 correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1)) # argmax返回一維張量中最大的值所在的位置 # 求準確率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # 把correct_prediction變為float32型別 # 初始化 init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) for epoch in range(21): for batch in range(n_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys}) acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}) print("Iter " + str(epoch) + ", Testing Accuracy= " + str(acc))