-- coding: utf-8 --

import tensorflow as tf
from tensorflow.contrib import rnn

导入 MINST 数据集

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("/data/", one_hot=True)

参数设置

learning_rate = 0.001
training_iters = 100000
batch_size = 128
display_step = 10

Network Parameters

n_input = 28 # MNIST data 输入 (img shape: 28*28)
n_steps = 28 # timesteps
n_hidden = 128 # hidden layer num of features
n_classes = 10 # MNIST 列别 (0-9 ，一共10类)

tf.reset_default_graph()

tf Graph input

x = tf.placeholder(“float”, [None, n_steps, n_input])
y = tf.placeholder(“float”, [None, n_classes])

x1 = tf.unstack(x, n_steps, 1)
lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)

outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x1,

dtype=tf.float32)

outputs, _, _ = rnn.stack_bidirectional_rnn([lstm_fw_cell],[lstm_bw_cell], x1,

dtype=tf.float32)

stacked_rnn = []
stacked_bw_rnn = []
for i in range(3):
lstm_fw_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True,
reuse=tf.get_variable_scope().reuse)
lstm_fw_cell = tf.nn.rnn_cell.DropoutWrapper(lstm_fw_cell, input_keep_prob=0.5, output_keep_prob=0.5, )
lstm_bw_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0, reuse=tf.get_variable_scope().reuse
, state_is_tuple=True) # , state_is_tuple = True
lstm_bw_cell = tf.nn.rnn_cell.DropoutWrapper(lstm_bw_cell, input_keep_prob=0.5, output_keep_prob=0.5)
stacked_rnn.append(lstm_fw_cell)
stacked_bw_rnn.append(lstm_bw_cell)

outputs, _, _ = rnn.stack_bidirectional_rnn(stacked_rnn,stacked_bw_rnn, x1,

dtype=tf.float32)

‘’’
mcell=tf.nn.rnn_cell.MultiRNNCell(stacked_rnn)
mcell = tf.nn.rnn_cell.DropoutWrapper(mcell, output_keep_prob=0.5, input_keep_prob=1.0)
mcell_bw=tf.nn.rnn_cell.MultiRNNCell(stacked_bw_rnn)
mcell_bw = tf.nn.rnn_cell.DropoutWrapper(mcell_bw, output_keep_prob=0.5, input_keep_prob=1.0)
‘’’
#法一
mcell = tf.nn.rnn_cell.MultiRNNCell(stacked_rnn)
mcell_bw = tf.nn.rnn_cell.MultiRNNCell(stacked_bw_rnn)
inint_state = mcell.zero_state(batch_size, tf.float32)
inint_state2 = mcell_bw.zero_state(batch_size, tf.float32)

‘’’
#法二
def lstm_call():
cell = tf.nn.rnn_cell.LSTMCell(num_units=n_hidden, reuse=tf.get_variable_scope().reuse)
return tf.nn.rnn_cell.DropoutWrapper(cell, output_keep_prob=0.5)

mcell = tf.nn.rnn_cell.MultiRNNCell(cells=[lstm_call() for i in range(3)])
inint_state = mcell.zero_state(batch_size, tf.float32)
mcell_bw = tf.nn.rnn_cell.MultiRNNCell(cells=[lstm_call() for i in range(3)])
inint_state2 = mcell_bw.zero_state(batch_size, tf.float32)
‘’’
outputs, _, _ = rnn.stack_bidirectional_dynamic_rnn([mcell], [mcell_bw], x, initial_states_fw=[inint_state],
initial_states_bw=[inint_state2], dtype=tf.float32)#！！！注意四处[]
print(outputs[0].shape, outputs.shape)
outputs = tf.transpose(outputs, [1, 0, 2])
print(outputs[0].shape, outputs.shape)
pred = tf.contrib.layers.fully_connected(outputs[-1], n_classes, activation_fn=None)

Define loss and optimizer

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

Evaluate model

correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

启动session

with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
step = 1
# Keep training until reach max iterations
while step * batch_size < training_iters:
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, n_steps, n_input))
# Run optimization op (backprop)
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
if step % display_step == 0:
# 计算批次数据的准确率
acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})
# Calculate batch loss
loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})
print("Iter " + str(step * batch_size) + “, Minibatch Loss= " +
“{:.6f}”.format(loss) + “, Training Accuracy= " +
“{:.5f}”.format(acc))
step += 1
print(” Finished!”)

# 计算准确率 for 128 mnist test images
test_len = 128
test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))
test_label = mnist.test.labels[:test_len]
print("Testing Accuracy:", \sess.run(accuracy, feed_dict={x: test_data, y: test_label}))

tf.nn.rnn_cell.DropoutWrapper用法细节案例2相关推荐

tf.nn.rnn_cell.DropoutWrapper用法细节案例1
前言:前面介绍了LSTM,下面介绍LSTM的几种变种双向RNN Bidirectional RNN(双向RNN)假设当前t的输出不仅仅和之前的序列有关,并且还与之后的序列有关,例如:预测一个语句中 ...
deep_learning 03. tf.nn.rnn_cell.MultiRNNCell()
开始的话: 从基础做起,不断学习,坚持不懈,加油. 一位爱生活爱技术来自火星的程序汪前面两节讲了两个基础的BasicRNNCell和BasicLSTMCell,今天就来看下怎么把这些简单的cell给 ...
成功解决没有tf.nn.rnn_cell属性
成功解决没有tf.nn.rnn_cell属性目录解决问题解决思路解决方法解决问题没有tf.nn.rnn_cell属性解决思路由于不同的TensorFlow版本之间某些函数的用法引起的错 ...
【TensorFlow】tf.nn.softmax_cross_entropy_with_logits的用法
[TensorFlow]tf.nn.softmax_cross_entropy_with_logits的用法 from:https://blog.csdn.net/mao_xiao_feng/arti ...
python中tf.abs_python – Tensorflow：替换tf.nn.rnn_cell._linear(输入,大小,0,范围)
我试图从 https://arxiv.org/pdf/1609.05473.pdf开始运行SequenceGAN( https://github.com/LantaoYu/SeqGAN). 修复明显的 ...
tf.nn.embedding_lookup()的用法
函数: tf.nn.embedding_lookup( params, ids, partition_strategy='mod', name=None, validate_indices=True, ...
tf.nn.in_top_k的用法
http://blog.csdn.net/uestc_c2_403/article/details/73187915 tf.nn.in_top_k组要是用于计算预测的结果和实际结果的是否相等,返回一个 ...
with tf.Session() as sess、和tf.nn.tanh的用法以及作用
tf.nn.tanh 双曲正切曲线一个Session可能会拥有一些资源,例如Variable或者Queue.当我们不再需要该session的时候,需要将这些资源进行释放.有两种方式, 调用sessio ...
tf.nn.moments( ) 的用法
def moments(x,axes,shift=None, # pylint: disable=unused-argumentname=None,keep_dims=False) x: 形如:[ba ...

tf.nn.rnn_cell.DropoutWrapper用法细节案例2

-- coding: utf-8 --

导入 MINST 数据集

参数设置

Network Parameters

tf Graph input

outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x1,

dtype=tf.float32)

outputs, _, _ = rnn.stack_bidirectional_rnn([lstm_fw_cell],[lstm_bw_cell], x1,

dtype=tf.float32)

outputs, _, _ = rnn.stack_bidirectional_rnn(stacked_rnn,stacked_bw_rnn, x1,

dtype=tf.float32)

Define loss and optimizer

Evaluate model

启动session

tf.nn.rnn_cell.DropoutWrapper用法细节案例2相关推荐

最新文章

热门文章