tesnsorflow 使用LSTM进行分类的例程
import tensorflow as tf
import sys
from tensorflow.examples.tutorials.mnist import input_data
# this is data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
# hyperparameters
lr = 0.001
training_iters = 100000
batch_size = 128
n_inputs = 28 # MNIST data input (img shape: 28*28)
n_steps = 28 # time steps
n_hidden_units = 128 # neurons in hidden layer
n_classes = 10 # MNIST classes (0-9 digits)
# tf Graph input
x = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
y = tf.placeholder(tf.float32, [None, n_classes])
# Define weights
weights = {
# (28, 128)
'in': tf.Variable(tf.random_normal([n_inputs, n_hidden_units])),
# (128, 10)
'out': tf.Variable(tf.random_normal([n_hidden_units, n_classes]))
}
biases = {
# (128, )
'in': tf.Variable(tf.constant(0.1, shape=[n_hidden_units, ])),
# (10, )
'out': tf.Variable(tf.constant(0.1, shape=[n_classes, ]))
}
def RNN(X, weights, biases):
# hidden layer for input to cell
########################################
#X(128 batch,28 steps,28 inputs)
#==>(128*28,28 inputs)
X = tf.reshape(X,[-1,n_inputs])
#==>(128 batch*28 steps,128 hidden)
X_in = tf.matmul(X,weights['in'])+biases['in']
#==>(128 batch,28 steps,128 hidden)
X_in = tf.reshape(X_in,[-1,n_steps,n_hidden_units])
# cell
##########################################
#same to define active function
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_units,forget_bias=1.0,state_is_tuple=True)
#lstm cell is divided into two parts(c_state,m_state)
_init_state = lstm_cell.zero_state(batch_size,dtype=tf.float32)
#choose rnn how to work,lstm just is one kind of rnn,use lstm_cell for active function,set initial_state
outputs,states = tf.nn.dynamic_rnn(lstm_cell,X_in,initial_state=_init_state,time_major=False)
# hidden layer for output as the final results
#############################################
results = tf.matmul(states[1],weights['out']) + biases['out']
#unpack to list [(batch,outputs)]*steps
#outputs = tf.unpack(tf.transpose(outputs,[1,0,2])) # state is the last outputs
#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(pred, 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))
init = tf.initialize_all_variables()
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说明：
1、该LSTM模型的结构是28*128*10
def RNN(X, weights, biases):
# hidden layer for input to cell
########################################
#X(128 batch,28 steps,28 inputs)
#==>(128*28,28 inputs)
X = tf.reshape(X,[-1,n_inputs])
#==>(128 batch*28 steps,128 hidden)
X_in = tf.matmul(X,weights['in'])+biases['in']
#==>(128 batch,28 steps,128 hidden)
X_in = tf.reshape(X_in,[-1,n_steps,n_hidden_units])
# cell
##########################################
#same to define active function
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_units,forget_bias=1.0,state_is_tuple=True)
#lstm cell is divided into two parts(c_state,m_state)
_init_state = lstm_cell.zero_state(batch_size,dtype=tf.float32)
#choose rnn how to work,lstm just is one kind of rnn,use lstm_cell for active function,set initial_state
outputs,states = tf.nn.dynamic_rnn(lstm_cell,X_in,initial_state=_init_state,time_major=False)
# hidden layer for output as the final results
#############################################
results = tf.matmul(states[1],weights['out']) + biases['out']
#unpack to list [(batch,outputs)]*steps
#outputs = tf.unpack(tf.transpose(outputs,[1,0,2])) # state is the last outputs
#results = tf.matmul(outputs[-1],weights['out']) + biases['out']
return results
1、
#X(128 batch,28 steps,28 inputs)
#==>(128*28,28 inputs)
X = tf.reshape(X,[-1,n_inputs])
#==>(128 batch*28 steps,128 hidden)
X_in = tf.matmul(X,weights['in'])+biases['in']
#==>(128 batch,28 steps,128 hidden)
X_in = tf.reshape(X_in,[-1,n_steps,n_hidden_units])
2、
# cell
##########################################
#same to define active function
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_units,forget_bias=1.0,state_is_tuple=True)
#lstm cell is divided into two parts(c_state,m_state)
_init_state = lstm_cell.zero_state(batch_size,dtype=tf.float32)
3、
#choose rnn how to work,lstm just is one kind of rnn,use lstm_cell for active function,set initial_state
outputs,states = tf.nn.dynamic_rnn(lstm_cell,X_in,initial_state=_init_state,time_major=False)
4、
# hidden layer for output as the final results
#############################################
results = tf.matmul(states[1],weights['out']) + biases['out']
5、
init = tf.initialize_all_variables()
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
6、使用LSTM单元的RNN也有前向过程和反向传播过程,只是前向过程是在时序上进行前向传播。所以,使用LSTM单元的RNN必须要记录每个step,LSTM单元的激活值和状态。