教程 | 基于LSTM实现手写数字识别

OpenCV学堂

发布于 2019-09-26 15:57:03

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文章被收录于专栏：贾志刚-OpenCV学堂贾志刚-OpenCV学堂

基于tensorflow，如何实现一个简单的循环神经网络，完成手写数字识别，附完整演示代码。

LSTM网络构建

基于tensorflow实现简单的LSTM网络，完成mnist手写数字数据集训练与识别。这个其中最重要的构建一个LSTM网络，tensorflow已经给我们提供相关的API，我们只要使用相关API就可以轻松构建一个简单的LSTM网络。

首先定义输入与目标标签

# 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的大小或者数目
time_steps: 网络把输出重新输入的次数
num_features: 输入矩阵/神经元

构建LSTM单元

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 的单元 num_hidden : 隐藏层节点数目 forget_bias: 遗忘门中要加上的增益偏置 outputs: 网络输出 states：状态

这样我们就构建好一个LSTM循环神经网络了，它的执行过程是很魔幻的。简直是神奇！以后再说。

代码程序执行与输出

完整的代码演示分为如下几个部分：

加载数据集
创建LSTM网络
训练网络
执行测试

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']


# 输入预测
logits = rnn_network(X, weights, biases)
prediction = tf.nn.softmax(logits)

# 定义损失函数与优化器
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)

# 计算识别精度
correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# 开始训练
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!")

    # 使用测试数据集测试训练号的模型， 测试128张手写数字图像
    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}))

运行输出如下：