基于基于Tensorflow一维卷积用法详解一维卷积用法详解
我就废话不多说了,大家还是直接看代码吧!
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为步长
print(conv1.shape)#宽度计算(width-kernel_size+1)/strides ,(10-3+1)/2=4 (64,4,32)
conv2 = tf.nn.conv1d(input,w,2,'SAME') #步长为2
print(conv2.shape)#宽度计算width/strides 10/2=5 (64,5,32)
conv3 = tf.nn.conv1d(input,w,1,'SAME') #步长为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)时,conv1的shape
以下是input_shape=(1,10,1), w = (3,1,3)时,conv1的shape
补充知识:补充知识:tensorflow中一维卷积中一维卷积conv1d处理语言序列举例处理语言序列举例
tf.nn.conv1d::
函数形式: tf.nn.conv1d(value, filters, stride, padding, use_cudnn_on_gpu=None, data_format=None,
name=None):
程序举例:
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是步长。
tf.global_variables_initializer().run()
out = sess.run(conv1)
print(out)
注:一维卷积中padding=’SAME’只在输入的末尾填充0
tf.layters.conv1d::
函数形式:tf.layters.conv1d(inputs, filters, kernel_size, strides=1, padding=’valid’, data_format=’channels_last’,
dilation_rate=1, activation=None, use_bias=True,…)
程序举例:
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)
二维卷积实现一维卷积二维卷积实现一维卷积:
评论0