import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # 载入数据 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # 输入图片是28 n_input = 28 max_time = 28 lstm_size = 100 # 隐藏单元 n_class = 10 # 10个分类 batch_size = 50 # 每次50个样本 n_batch_size = mnist.train.num_examples // batch_size # 计算一共有多少批次 # 这里None表示第一个维度可以是任意长度 # 创建占位符 x = tf.placeholder(tf.float32,[None, 28*28]) # 正确的标签 y = tf.placeholder(tf.float32,[None, 10]) # 初始化权重 ,stddev为标准差 weight = tf.Variable(tf.truncated_normal([lstm_size, n_class], stddev=0.1)) # 初始化偏置层 biases = tf.Variable(tf.constant(0.1, shape=[n_class])) # 定义RNN网络 def RNN(X, weights, biases): # 原始数据为[batch_size,28*28] # input = [batch_size, max_time, n_input] input = tf.reshape(X,[-1, max_time, n_input ]) # 定义LSTM的基本单元 lstm_cell = tf.contrib.rnn.BasicLSTMCell(lstm_size) # final_state[0] 是cell state # final_state[1] 是hidden stat outputs, final_state = tf.nn.dynamic_rnn(lstm_cell, input, 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, weight, biases) # 损失函数 loss = 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(loss) # 将结果存下来 correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1)) # 计算正确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) # 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_size): # 取出下一批次数据 batch_xs,batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step, feed_dict={x: batch_xs,y: batch_ys}) if(batch%100==0): print(str(batch)+"/" + str(n_batch_size)) acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}) print("Iter" + str(epoch) + " ,Testing Accuracy = " + str(acc))
Tensorflow实现RNN(LSTM)手写数字识别
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