TF之RNN：基于顺序的RNN分类案例对手写数字图片mnist数据集实现高精度预测—Jason niu

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

lr=0.001

training_iters=100000

batch_size=128

n_inputs=28

n_steps=28

n_hidden_units=128

n_classes=10

x=tf.placeholder(tf.float32, [None,n_steps,n_inputs])

y=tf.placeholder(tf.float32, [None,n_classes])

weights ={

    'in':tf.Variable(tf.random_normal([n_inputs,n_hidden_units])),

    'out':tf.Variable(tf.random_normal([n_hidden_units,n_classes])),

    }

biases ={

    'in':tf.Variable(tf.constant(0.1,shape=[n_hidden_units,])),

    'out':tf.Variable(tf.constant(0.1,shape=[n_classes,])),

    }

def RNN(X,weights,biases):

    X=tf.reshape(X,[-1,n_inputs])

    X_in=tf.matmul(X,weights['in'])+biases['in']

    X_in=tf.reshape(X_in,[-1,n_steps,n_hidden_units])

    lstm_cell=tf.nn.rnn_cell.BasicLSTMCell(n_hidden_units,forget_bias=1.0,state_is_tuple=True)

    __init__state=lstm_cell.zero_state(batch_size, dtype=tf.float32)

    outputs,states=tf.nn.dynamic_rnn(lstm_cell,X_in,initial_state=__init__state,time_major=False)

    outputs=tf.unpack(tf.transpose(outputs, [1,0,2]))

    results=tf.matmul(outputs[-1],weights['out'])+biases['out']

    return results

pred =RNN(x,weights,biases)

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

train_op=tf.train.AdamOptimizer(lr).minimize(cost)

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

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


 

with tf.Session() as sess:

    sess.run(init)

    step=0

    while step*batch_size < training_iters:

        batch_xs,batch_ys=mnist.train.next_batch(batch_size)

        batch_xs=batch_xs.reshape([batch_size,n_steps,n_inputs])

        sess.run([train_op],feed_dict={

            x:batch_xs,

            y:batch_ys,})

        if step%20==0:

            print(sess.run(accuracy,feed_dict={

                x:batch_xs,

                y:batch_ys,}))

        step+=1