实现一个简单的RNN,并对所用到的所有知识点进行讲解

tf.unstack

请看本人这篇博客

tf.nn.rnn_cell.LSTMCell 和 tf.contrib.rnn.LSTMCell

请看本人这篇博客

tf.nn.static_rnn 和 tf.contrib.rnn.static_rnn

请看本人这篇博客

实现一个简单的RNN

代码来自这里，我稍微改了一些

#!/usr/bin/env python
# coding: utf-8


from __future__ import print_function
import tensorflow as tf


from tensorflow.contrib import rnn



# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/home/xhm/PycharmProjects/tensorflow_study/mnist_data/", one_hot=True)



# Training Parameters
learning_rate = 0.001
training_steps = 10000
batch_size = 128
display_step = 200

# Network Parameters
num_input = 28 # MNIST data input (img shape: 28*28)
timesteps = 28 # timesteps
num_hidden = 128 # hidden layer num of features
num_classes = 10 # MNIST total classes (0-9 digits)



#神经网络的输入
X=tf.placeholder("float",[None,timesteps,num_input])
Y=tf.placeholder("float",[None,num_classes])


def RNN(x):
    x = tf.unstack(x, timesteps, 1)
    lstm_cell=tf.nn.rnn_cell.BasicLSTMCell(num_hidden,forget_bias=1.0)
    outputs,states=rnn.static_rnn(lstm_cell,x,dtype=tf.float32)
    out=tf.layers.dense(outputs[-1],num_classes,use_bias=True)
    return out


logits=RNN(X)

pred_classes=tf.argmax(tf.nn.softmax(logits),axis=-1)
loss_op=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=Y))
optimizer=tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op=optimizer.minimize(loss_op)

accuracy=tf.reduce_mean(tf.cast(tf.equal(pred_classes,tf.argmax(Y,-1)),tf.float32))

init=tf.global_variables_initializer()


# Start training
with tf.Session() as sess:

    # Run the initializer
    sess.run(init)

    for step in range(1, training_steps+1):
        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, timesteps, num_input))

        # Run optimization op (backprop)
        sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
        if step % display_step == 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) + ", Minibatch Loss= " +                   "{:.4f}".format(loss) + ", Training Accuracy= " +                   "{:.3f}".format(acc))

    print("Optimization Finished!")

    # Calculate accuracy for 128 mnist test images
    test_len = 128
    test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input))
    test_label = mnist.test.labels[:test_len]
    print("Testing Accuracy:",         sess.run(accuracy, feed_dict={X: test_data, Y: test_label}))