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基于Tensorflow一維卷積用法詳解

更新時(shí)間：2020年05月22日 10:14:54 作者：星夜孤帆

這篇文章主要介紹了基于Tensorflow一維卷積用法詳解，具有很好的參考價(jià)值，希望對(duì)大家有所幫助。一起跟隨小編過(guò)來(lái)看看吧

我就廢話不多說(shuō)了，大家還是直接看代碼吧！

import tensorflow as tf
import numpy as np
input = tf.constant(1,shape=(64,10,1),dtype=tf.float32,name='input')#shape=(batch,in_width,in_channels)
w = tf.constant(3,shape=(3,1,32),dtype=tf.float32,name='w')#shape=(filter_width,in_channels,out_channels)
conv1 = tf.nn.conv1d(input,w,2,'VALID') #2為步長(zhǎng)
print(conv1.shape)#寬度計(jì)算(width-kernel_size+1)/strides ,(10-3+1)/2=4 (64,4,32)
conv2 = tf.nn.conv1d(input,w,2,'SAME') #步長(zhǎng)為2
print(conv2.shape)#寬度計(jì)算width/strides 10/2=5 (64,5,32)
conv3 = tf.nn.conv1d(input,w,1,'SAME') #步長(zhǎng)為1
print(conv3.shape) # (64,10,32)
with tf.Session() as sess:
 print(sess.run(conv1))
 print(sess.run(conv2))
 print(sess.run(conv3))

以下是input_shape=(1,10,1), w = (3,1,1)時(shí)，conv1的shape

以下是input_shape=(1,10,1), w = (3,1,3)時(shí)，conv1的shape

補(bǔ)充知識(shí)：tensorflow中一維卷積conv1d處理語(yǔ)言序列舉例

tf.nn.conv1d：

函數(shù)形式： tf.nn.conv1d(value, filters, stride, padding, use_cudnn_on_gpu=None, data_format=None, name=None):

程序舉例：

import tensorflow as tf
import numpy as np
sess = tf.InteractiveSession()
 
# --------------- tf.nn.conv1d -------------------
inputs=tf.ones((64,10,3)) # [batch, n_sqs, embedsize]
w=tf.constant(1,tf.float32,(5,3,32)) # [w_high, embedsize, n_filers]
conv1 = tf.nn.conv1d(inputs,w,stride=2 ,padding='SAME') # conv1=[batch, round(n_sqs/stride), n_filers],stride是步長(zhǎng)。
 
tf.global_variables_initializer().run()
out = sess.run(conv1)
print(out)

注：一維卷積中padding='SAME'只在輸入的末尾填充0

tf.layters.conv1d：

函數(shù)形式：tf.layters.conv1d(inputs, filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, activation=None, use_bias=True，...)

程序舉例：

import tensorflow as tf
import numpy as np
sess = tf.InteractiveSession()
 
# --------------- tf.layters.conv1d -------------------
inputs=tf.ones((64,10,3)) # [batch, n_sqs, embedsize]
num_filters=32
kernel_size =5
conv2 = tf.layers.conv1d(inputs, num_filters, kernel_size,strides=2, padding='valid',name='conv2') # shape = (batchsize, round(n_sqs/strides),num_filters)
tf.global_variables_initializer().run()
out = sess.run(conv2)
print(out)

二維卷積實(shí)現(xiàn)一維卷積:

import tensorflow as tf
sess = tf.InteractiveSession()
def conv2d(x, W):
 return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')
def max_pool_1x2(x):
 return tf.nn.avg_pool(x, ksize=[1,1,2,1], strides=[1,1,2,1], padding='SAME')
'''
ksize = [x, pool_height, pool_width, x]
strides = [x, pool_height, pool_width, x]
'''
 
x = tf.Variable([[1,2,3,4]], dtype=tf.float32)
x = tf.reshape(x, [1,1,4,1]) #這一步必不可少，否則會(huì)報(bào)錯(cuò)說(shuō)維度不一致；
'''
[batch, in_height, in_width, in_channels] = [1,1,4,1]
'''
 
W_conv1 = tf.Variable([1,1,1],dtype=tf.float32) # 權(quán)重值
W_conv1 = tf.reshape(W_conv1, [1,3,1,1]) # 這一步同樣必不可少
'''
[filter_height, filter_width, in_channels, out_channels]
'''
h_conv1 = conv2d(x, W_conv1) # 結(jié)果：[4,8,12,11]
h_pool1 = max_pool_1x2(h_conv1)
tf.global_variables_initializer().run()
print(sess.run(h_conv1)) # 結(jié)果array([6,11.5])x

兩種池化操作：

# 1:stride max pooling
convs = tf.expand_dims(conv, axis=-1) # shape=[?,596,256,1]
smp = tf.nn.max_pool(value=convs, ksize=[1, 3, self.config.num_filters, 1], strides=[1, 3, 1, 1],
     padding='SAME') # shape=[?,299,256,1]
smp = tf.squeeze(smp, -1) # shape=[?,299,256]
smp = tf.reshape(smp, shape=(-1, 199 * self.config.num_filters))
 
# 2: global max pooling layer
gmp = tf.reduce_max(conv, reduction_indices=[1], name='gmp')

不同核尺寸卷積操作：

kernel_sizes = [3,4,5] # 分別用窗口大小為3/4/5的卷積核
with tf.name_scope("mul_cnn"):
 pooled_outputs = []
 for kernel_size in kernel_sizes:
  # CNN layer
  conv = tf.layers.conv1d(embedding_inputs, self.config.num_filters, kernel_size, name='conv-%s' % kernel_size)
  # global max pooling layer
  gmp = tf.reduce_max(conv, reduction_indices=[1], name='gmp')
  pooled_outputs.append(gmp)
 self.h_pool = tf.concat(pooled_outputs, 1) #池化后進(jìn)行拼接

以上這篇基于Tensorflow一維卷積用法詳解就是小編分享給大家的全部?jī)?nèi)容了，希望能給大家一個(gè)參考，也希望大家多多支持腳本之家。

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