学习进度笔记12
TensorFlow双向循环神经网络
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}))