学习进度笔记15

TensorFlow双向循环神经网络

鉴于单向循环神经网络某些情况下的不足，提出了双向循环神经网络。因为是需要能关联未来的数据，而单向循环神经网络属于关联历史数据，所以对于未来数据提出反向循环神经网络，两个方向的网络结合到一起就能关联历史与未来了。

双向循环神经网络按时刻展开的结构如下，可以看到向前和向后层共同连接着输出层，其中包含了6个共享权值，分别为输入到向前层和向后层两个权值、向前层和向后层各自隐含层到隐含层的权值、向前层和向后层各自隐含层到输出层的权值。

可以由下列式子表示

实验内容：使用TensorFlow通过双向循环神经网络算法对手写数字进行识别。

 源代码：

from __future__ import print_function
import tensorflow as tf
from tensorflow.contrib import rnn
from tensorflow.examples.tutorials.mnist import input_data
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"

mnist=input_data.read_data_sets("/home/yxcx/tf_rnn",one_hot=True)

#Traning Parameters
learning_rate=0.001
training_step=10000
batch_size=128
display_step=400

#Network Parmeters
num_input=28
timestep=28
num_hidden=128
num_classes=10

#tf Graph input
X=tf.placeholder("float32",[None,timestep,num_input])
Y=tf.placeholder("float32",[None,num_classes])

#Define weights
weights={
    'out':tf.Variable(tf.random_normal([2*num_hidden,num_classes]))
}
biases={
    'out':tf.Variable(tf.random_normal([num_classes]))
}

def BiRNN(X,weights,biases):
    x=tf.unstack(X,timestep,1)

    #define lstm cells with tensorflow
    #Forward direction cell
    lstm_fw_cell=rnn.BasicLSTMCell(num_hidden,forget_bias=1.0)
    #Backward direction cell
    lstm_bw_cell=rnn.BasicLSTMCell(num_hidden,forget_bias=1.0)

    #Get lstm cell output
    try:
        outputs,_,_=rnn.static_bidirectional_rnn(lstm_fw_cell,lstm_bw_cell,x,dtype=tf.float32)
    except Exception:
        outputs=rnn.static_bidirectional_rnn(lstm_fw_cell,lstm_bw_cell,x,dtype=tf.float32)

    # Linaer activation,using rnn inner loop last output
    return tf.matmul(outputs[-1],weights['out'])+biases['out']

logits=BiRNN(X,weights,biases)
prediction=tf.nn.softmax(logits)

#Define loss and optimizer
loss_op=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=Y))
optimizer=tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
train_op=optimizer.minimize(loss_op)

#Evaluate model
correct_pred=tf.equal(tf.argmax(prediction,1),tf.argmax(Y,1))
accuracy=tf.reduce_mean(tf.cast(correct_pred,tf.float32))

#Initialize Variable
init=tf.global_variables_initializer()

#start training
with tf.Session() as sess:
    # Run the initializer
    sess.run(init)

    for step in range(1,training_step+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,timestep,num_input))
        # Run optimizetion op
        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)+ ",Minbatch 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,timestep,num_input))
    test_label=mnist.test.labels[:test_len]
    print("Test Accuracy:",sess.run(accuracy,feed_dict={X:test_data,Y:test_label}))

结果截图：