Tensorflow +rnn

最新推荐文章于 2024-09-22 10:49:42 发布

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最新推荐文章于 2024-09-22 10:49:42 发布

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本文介绍了一个基于TensorFlow的LSTM网络实现过程，该网络用于对MNIST手写数字数据集进行分类。通过定义网络结构、训练流程及评估方法，最终实现了较高的测试准确性。

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import tensorflow as tf
from tensorflow.examples.tutorials.mnist import  input_data
from tensorflow.contrib import rnn

# 载入数据集
mnist = input_data.read_data_sets('F:\Pycharm projection\MNIST_data', one_hot=True)

# 输入图片28*28
n_inputs = 28  # 输入一行，一行有28个数据
max_time = 28  # 一共28行
lstm_size =100  # 隐层单元
n_classes =10  # 10个分类
batch_size =50  # 每批次50个样本
n_batch = mnist.train.num_examples//batch_size  #  计算一共有多少个批次

# 这里的none是表示第一个维度可以是任意的长度
x = tf.placeholder(tf.float32,[None,784])
# 正确的标签
y = tf.placeholder(tf.float32,[None,10])

# 初始化权值
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]))

# 定义RNN网络
def RNN(X,weights,biases):
    # inputs=[batch_size, max_time, n_inputs]
    inputs = tf.reshape(X, [-1, max_time, n_inputs])
    # 定义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


# 计算RNN的返回结果
prediction = RNN(x, weights, biases)
# 损失函数
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
# 使用AdamOptimizer进行优化
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# 结果存放在一个布尔型列表中
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1))  # argmax返回一维张量中最大的值所在的位置
# 求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))  # 把correct_prediction变为float32类型
# 初始化
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(6):
        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))

出错提示：File "F:/Pycharm projection/tensorflowbasic/LSTM.py", line 30, in RNN

lstm_cell = tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(lstm_size)

AttributeError: module 'tensorflow.contrib.rnn' has no attribute 'core_rnn_cell'

解决方法来自：

《炼数成金》.第七课递归神经网络LSTM的讲解，以及LSTM网络的使用学习笔记

运行结果：Iter 0, Testing Accuracy= 0.7668
Iter 1, Testing Accuracy= 0.8915
Iter 2, Testing Accuracy= 0.9045
Iter 3, Testing Accuracy= 0.9087
Iter 4, Testing Accuracy= 0.9301
Iter 5, Testing Accuracy= 0.9334