教程 | 基於LSTM實現手寫數字識別
- 2019 年 10 月 7 日
- 筆記
基於tensorflow，如何實現一個簡單的循環神經網絡，完成手寫數字識別，附完整演示代碼。
LSTM網絡構建
01
基於tensorflow實現簡單的LSTM網絡，完成mnist手寫數字數據集訓練與識別。這個其中最重要的構建一個LSTM網絡，tensorflow已經給我們提供相關的API， 我們只要使用相關API就可以輕鬆構建一個簡單的LSTM網絡。
首先定義輸入與目標標籤
# create RNN network X = tf.placeholder(shape=[None, time_steps, num_features], dtype=tf.float32) Y = tf.placeholder(shape=[None, 10], dtype=tf.float32)
其中
- None: 表示batchsize的大小或者數目
- time_steps: 網絡把輸出重新輸入的次數
- num_features: 輸入矩陣/神經元
構建LSTM單元
lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)
其中：
lstm_cell 表示 LSTM 的單元 num_hidden : 隱藏層節點數目 forget_bias: 遺忘門中要加上的增益偏置 outputs: 網絡輸出 states：狀態
這樣我們就構建好一個LSTM循環神經網絡了，它的執行過程是很魔幻的。簡直是神奇！以後再說。
代碼程序執行與輸出
02
完整的代碼演示分為如下幾個部分：
- 加載數據集
- 創建LSTM網絡
- 訓練網絡
- 執行測試
import tensorflow as tf from tensorflow.contrib import rnn import numpy as np from tensorflow.examples.tutorials.mnist import input_data print(tf.__version__) mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) num_hidden = 128 time_steps = 28 num_features = 28 num_classes = 10 batch_size = 128 # create RNN network X = tf.placeholder(shape=[None, time_steps, num_features], dtype=tf.float32) Y = tf.placeholder(shape=[None, 10], dtype=tf.float32) # Define weights weights = { 'out': tf.Variable(tf.random_normal([num_hidden, num_classes])) } biases = { 'out': tf.Variable(tf.random_normal([num_classes])) } def rnn_network(x, weights, biases): x = tf.unstack(x, time_steps, 1) lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) return tf.matmul(outputs[-1], weights['out']) + biases['out'] # 輸入預測 logits = rnn_network(X, weights, biases) prediction = tf.nn.softmax(logits) # 定義損失函數與優化器 loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( logits=logits, labels=Y)) optimizer = tf.train.AdamOptimizer() train_op = optimizer.minimize(loss_op) # 計算識別精度 correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # 開始訓練 with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for step in range(1, 5001): 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, time_steps, num_features)) # Run optimization op (backprop) sess.run(train_op, feed_dict={X: batch_x, Y: batch_y}) if step % 1000 == 0 or step == 1: # Calculate batch loss and accuracy loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, Y: batch_y}) print("Step " + str(step) + ", Loss= " + "{:.4f}".format(loss) + ", Training Accuracy= " + "{:.3f}".format(acc)) print("Optimization Finished!") # 使用測試數據集測試訓練號的模型， 測試128張手寫數字圖像 test_len = 128 test_data = mnist.test.images[:test_len].reshape((-1, time_steps, num_features)) test_label = mnist.test.labels[:test_len] print("Testing Accuracy:", sess.run(accuracy, feed_dict={X: test_data, Y: test_label}))
運行輸出如下：
