TensorFlow RNN 教程和代码

分析：

看 TensorFlow 也有一段时间了，准备按照 GitHub 上的教程，敲出来，顺便整理一下思路。

RNN部分

定义参数，包括数据相关，训练相关。
定义模型，损失函数，优化函数。
训练，准备数据，输入数据，输出结果。

代码：

#!/usr/bin/env python

# -*- coding: utf-8 -*-

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

from tensorflow.contrib import rnn

mnist=input_data.read_data_sets("./data",one_hot=True)

training_rate=0.001

training_iters=100000

batch_size=128

display_step=10

n_input=28

n_steps=28

n_hidden=128

n_classes=10

x=tf.placeholder("float",[None,n_steps,n_input])

y=tf.placeholder("float",[None,n_classes])

weights={'out':tf.Variable(tf.random_normal([n_hidden,n_classes]))}

biases={'out':tf.Variable(tf.random_normal([n_classes]))}

def RNN(x,weights,biases):

   x=tf.unstack(x,n_steps,1)

   lstm_cell=rnn.BasicLSTMCell(n_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']

pred=RNN(x,weights,biases)

cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y))

optimizer=tf.train.AdamOptimizer(learning_rate=training_rate).minimize(cost)

correct_pred=tf.equal(tf.argmax(pred,1),tf.argmax(y,1))

accuaracy=tf.reduce_mean(tf.cast(correct_pred,tf.float32))

init=tf.global_variables_initializer()

with tf.Session() as sess:

   sess.run(init)

   step=1

   while step*batch_size<training_iters:

      batch_x,batch_y=mnist.train.next_batch(batch_size)

      batch_x=batch_x.reshape(batch_size,n_steps,n_input)

      sess.run(optimizer,feed_dict={x:batch_x,y:batch_y})

      if step%display_step==0:

         acc=sess.run(accuaracy,feed_dict={x:batch_x,y:batch_y})

         loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})

         print("Iter " + str(step * batch_size) + ", Minibatch Loss= " + \

               "{:.6f}".format(loss) + ", Training Accuracy= " + \

               "{:.5f}".format(acc))

      step+=1

输出：

/anaconda/bin/python2.7 /Users/xxxx/PycharmProjects/TF_3/tf_rnn.py

Extracting ./data/train-images-idx3-ubyte.gz

Extracting ./data/train-labels-idx1-ubyte.gz

Extracting ./data/t10k-images-idx3-ubyte.gz

Extracting ./data/t10k-labels-idx1-ubyte.gz

2017-07-15 16:41:15.125981: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.

2017-07-15 16:41:15.125994: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.

2017-07-15 16:41:15.125997: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.

2017-07-15 16:41:15.126002: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.

Iter 1280, Minibatch Loss= 1.842738, Training Accuracy= 0.33594

Iter 2560, Minibatch Loss= 1.489123, Training Accuracy= 0.50000

Iter 3840, Minibatch Loss= 1.300060, Training Accuracy= 0.57812

Iter 5120, Minibatch Loss= 1.244872, Training Accuracy= 0.62500

Iter 6400, Minibatch Loss= 0.947143, Training Accuracy= 0.71094

Iter 7680, Minibatch Loss= 0.709695, Training Accuracy= 0.75781

Iter 8960, Minibatch Loss= 0.799844, Training Accuracy= 0.76562

Iter 10240, Minibatch Loss= 0.594611, Training Accuracy= 0.83594

Iter 11520, Minibatch Loss= 0.529350, Training Accuracy= 0.82031

Iter 12800, Minibatch Loss= 0.624426, Training Accuracy= 0.82031

Iter 14080, Minibatch Loss= 0.481889, Training Accuracy= 0.82812

Iter 15360, Minibatch Loss= 0.449692, Training Accuracy= 0.84375

Iter 16640, Minibatch Loss= 0.418820, Training Accuracy= 0.85938

Iter 17920, Minibatch Loss= 0.412161, Training Accuracy= 0.85156

Iter 19200, Minibatch Loss= 0.256099, Training Accuracy= 0.90625

Iter 20480, Minibatch Loss= 0.227309, Training Accuracy= 0.90625

Iter 21760, Minibatch Loss= 0.431014, Training Accuracy= 0.85938

Iter 23040, Minibatch Loss= 0.377097, Training Accuracy= 0.87500

Iter 24320, Minibatch Loss= 0.268153, Training Accuracy= 0.89844

Iter 25600, Minibatch Loss= 0.170557, Training Accuracy= 0.95312

Iter 26880, Minibatch Loss= 0.286947, Training Accuracy= 0.91406

Iter 28160, Minibatch Loss= 0.189623, Training Accuracy= 0.94531

Iter 29440, Minibatch Loss= 0.228949, Training Accuracy= 0.95312

Iter 30720, Minibatch Loss= 0.157198, Training Accuracy= 0.94531

Iter 32000, Minibatch Loss= 0.205744, Training Accuracy= 0.93750

Iter 33280, Minibatch Loss= 0.195218, Training Accuracy= 0.92188

Iter 34560, Minibatch Loss= 0.177956, Training Accuracy= 0.92969

Iter 35840, Minibatch Loss= 0.131563, Training Accuracy= 0.96875

Iter 37120, Minibatch Loss= 0.215156, Training Accuracy= 0.92969

Iter 38400, Minibatch Loss= 0.232274, Training Accuracy= 0.94531

Iter 39680, Minibatch Loss= 0.324053, Training Accuracy= 0.91406

Iter 40960, Minibatch Loss= 0.196385, Training Accuracy= 0.93750

Iter 42240, Minibatch Loss= 0.151221, Training Accuracy= 0.95312

Iter 43520, Minibatch Loss= 0.242021, Training Accuracy= 0.95312

Iter 44800, Minibatch Loss= 0.304008, Training Accuracy= 0.90625

Iter 46080, Minibatch Loss= 0.185177, Training Accuracy= 0.93750

Iter 47360, Minibatch Loss= 0.190960, Training Accuracy= 0.94531

Iter 48640, Minibatch Loss= 0.141995, Training Accuracy= 0.94531

Iter 49920, Minibatch Loss= 0.199995, Training Accuracy= 0.94531

Iter 51200, Minibatch Loss= 0.193773, Training Accuracy= 0.92188

Iter 52480, Minibatch Loss= 0.151757, Training Accuracy= 0.94531

Iter 53760, Minibatch Loss= 0.153755, Training Accuracy= 0.94531

Iter 55040, Minibatch Loss= 0.141472, Training Accuracy= 0.93750

Iter 56320, Minibatch Loss= 0.168057, Training Accuracy= 0.96094

Iter 57600, Minibatch Loss= 0.135691, Training Accuracy= 0.96094

Iter 58880, Minibatch Loss= 0.097003, Training Accuracy= 0.97656

Iter 60160, Minibatch Loss= 0.274090, Training Accuracy= 0.92188

Iter 61440, Minibatch Loss= 0.147230, Training Accuracy= 0.95312

Iter 62720, Minibatch Loss= 0.106019, Training Accuracy= 0.96094

Iter 64000, Minibatch Loss= 0.101133, Training Accuracy= 0.97656

Iter 65280, Minibatch Loss= 0.169548, Training Accuracy= 0.93750

Iter 66560, Minibatch Loss= 0.101966, Training Accuracy= 0.96094

Iter 67840, Minibatch Loss= 0.106501, Training Accuracy= 0.96875

Iter 69120, Minibatch Loss= 0.082817, Training Accuracy= 0.96875

Iter 70400, Minibatch Loss= 0.192926, Training Accuracy= 0.96094

Iter 71680, Minibatch Loss= 0.086935, Training Accuracy= 0.96875

Iter 72960, Minibatch Loss= 0.052052, Training Accuracy= 0.98438

Iter 74240, Minibatch Loss= 0.129968, Training Accuracy= 0.95312

Iter 75520, Minibatch Loss= 0.058070, Training Accuracy= 0.99219

Iter 76800, Minibatch Loss= 0.089518, Training Accuracy= 0.96875

Iter 78080, Minibatch Loss= 0.106092, Training Accuracy= 0.98438

Iter 79360, Minibatch Loss= 0.223101, Training Accuracy= 0.92188

Iter 80640, Minibatch Loss= 0.069419, Training Accuracy= 0.97656

Iter 81920, Minibatch Loss= 0.050585, Training Accuracy= 0.99219

Iter 83200, Minibatch Loss= 0.048002, Training Accuracy= 0.98438

Iter 84480, Minibatch Loss= 0.094293, Training Accuracy= 0.96875

Iter 85760, Minibatch Loss= 0.152253, Training Accuracy= 0.96094

Iter 87040, Minibatch Loss= 0.085382, Training Accuracy= 0.97656

Iter 88320, Minibatch Loss= 0.147018, Training Accuracy= 0.95312

Iter 89600, Minibatch Loss= 0.099780, Training Accuracy= 0.96094

Iter 90880, Minibatch Loss= 0.118362, Training Accuracy= 0.93750

Iter 92160, Minibatch Loss= 0.110498, Training Accuracy= 0.96094

Iter 93440, Minibatch Loss= 0.077664, Training Accuracy= 0.98438

Iter 94720, Minibatch Loss= 0.070865, Training Accuracy= 0.96094

Iter 96000, Minibatch Loss= 0.156309, Training Accuracy= 0.94531

Iter 97280, Minibatch Loss= 0.116825, Training Accuracy= 0.94531

Iter 98560, Minibatch Loss= 0.099852, Training Accuracy= 0.96875

Iter 99840, Minibatch Loss= 0.116358, Training Accuracy= 0.96875

Process finished with exit code 0

原文链接：http://www.tensorflownews.com/2017/07/15/tensorflow-rnn-turorial-mnist-code/

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