lstm = rnn.basiclstmcell(lstm_size, forget_bias=1.0, state_is_tuple=true)
x_split =tf.split(xr, time_step_size, 0)t ，lstm cell會產生兩個內部狀態 ctct
和htht （關於rnn與lstm的介紹可參考：
迴圈神經網路與lstm
）。當state_is_tuple=true時，上面講到的狀態ctct
和htht 就是分開記錄，放在乙個二元tuple中返回，如果這個引數沒有設定或設定成false，兩個狀態就按列連線起來返回。官方說這種形式馬上就要被deprecated了，所有我們在使用lstm的時候要加上state_is_tuple=true。
lstm = tf.nnlstm = tf.nn.rnn_cell.multirnncell([lstm] * num_layers, state_is_tuple=true)
# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorflow.contrib import rnn
import numpy as np
import input_data
# configuration
# o * w + b -> 10 labels for each image, o[? 28], w[28 10], b[10]
# ^ (o: output 28 vec from 28 vec input)
# |
# +-+ +-+ +--+
# |1|->|2|-> ... |28| time_step_size = 28
# +-+ +-+ +--+
# ^ ^ ... ^
# | | |
# img1:[28] [28] ... [28]
# img2:[28] [28] ... [28]
# img3:[28] [28] ... [28]
# ...
# img128 or img256 (batch_size or test_size 256)
# each input size = input_vec_size=lstm_size=28
# configuration variables
input_vec_size = lstm_size = 28
# 輸入向量的維度
time_step_size = 28
# 迴圈層長度
batch_size = 128
test_size = 256
def init_weights(shape):
return tf.variable(tf.random_normal(shape, stddev=0.01))
def model(x, w, b, lstm_size):
# x, input shape: (batch_size, time_step_size, input_vec_size)
# xt shape: (time_step_size, batch_size, input_vec_size)
xt = tf.transpose(x, [1, 0, 2]) # permute time_step_size and batch_size,[28, 128, 28]
# xr shape: (time_step_size * batch_size, input_vec_size)
xr = tf.reshape(xt, [-1, lstm_size]) # each row has input for each lstm cell (lstm_size=input_vec_size)
# each array shape: (batch_size, input_vec_size)
x_split = tf.split(xr, time_step_size, 0) # split them to time_step_size (28 arrays),shape = [(128, 28),(128, 28)...]
# make lstm with lstm_size (each input vector size). num_units=lstm_size; forget_bias=1.0
lstm = rnn.basiclstmcell(lstm_size, forget_bias=1.0, state_is_tuple=true)
# get lstm cell output, time_step_size (28) arrays with lstm_size output: (batch_size, lstm_size)
# rnn..static_rnn()的輸出對應於每乙個timestep，如果只關心最後一步的輸出，取outputs[-1]即可
outputs, _states = rnn.static_rnn(lstm, x_split, dtype=tf.float32) # 時間序列上每個cell的輸出:[... shape=(128, 28)..]
# linear activation
# get the last output
return tf.matmul(outputs[-1], w) + b, lstm.state_size # state size to initialize the stat
mnist = input_data.read_data_sets("mnist_data/", one_hot=true) # 讀取資料
# mnist.train.images是乙個55000 * 784維的矩陣, mnist.train.labels是乙個55000 * 10維的矩陣
trx, try, tex, tey = mnist.train
.images, mnist.train
.labels, mnist.test
.images, mnist.test
.labels
# 將每張圖用乙個28x28的矩陣表示,(55000,28,28,1)
trx = trx.reshape(-1, 28, 28)
tex = tex.reshape(-1, 28, 28)
x = tf.placeholder("float", [none, 28, 28])
y = tf.placeholder("float", [none, 10])
# get lstm_size and output 10 labels
w = init_weights([lstm_size, 10]) # 輸出層權重矩陣28×10
b = init_weights([10]) # 輸出層bais
py_x, state_size = model(x, w, b, lstm_size)
cost = tf.reduce_mean(tf.nn
.softmax_cross_entropy_with_logits(logits=py_x, labels=y))
train_op = tf.train
.rmspropoptimizer(0.001, 0.9).minimize(cost)
predict_op = tf.argmax(py_x, 1)
session_conf = tf.configproto()
session_conf.gpu_options.allow_growth = true
# launch the graph in a session
with tf.session(config=session_conf) as sess:
# you need to initialize all variables
tf.global_variables_initializer().run()
for i in range(100):
for start, end in zip(range(0, len(trx), batch_size), range(batch_size, len(trx)+1, batch_size)):
sess.run(train_op, feed_dict=)
test_indices = np.arange(len(tex)) # get a test batch
np.random
.shuffle(test_indices)
test_indices = test_indices[0:test_size]
print(i, np.mean(np.argmax(tey[test_indices], axis=1) ==
sess.run(predict_op, feed_dict=)))
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