tensorflow中的 Conv1d Conv2d Conv3d

  def __init__(self,
               filters,
               kernel_size,
               strides=1,
               padding='valid',
               data_format='channels_last',
               dilation_rate=1,
               groups=1,
               activation=None,
               use_bias=True,
               kernel_initializer='glorot_uniform',
               bias_initializer='zeros',
               kernel_regularizer=None,
               bias_regularizer=None,
               activity_regularizer=None,
               kernel_constraint=None,
               bias_constraint=None,
               **kwargs):

参数：

filters:滤波器的个数。

kernel_size:卷积核的大小

strides:表示步

padding:填充。填充的方式，valid, causal  or  same

Conv1d

1维卷积常用于序列模型或者是自然语言处理

super(Conv1D, self).__init__(
        rank=1,
        filters=filters,
        kernel_size=kernel_size,
        strides=strides,
        padding=padding,
        data_format=data_format,
        dilation_rate=dilation_rate,
        groups=groups,
        activation=activations.get(activation),
        use_bias=use_bias,
        kernel_initializer=initializers.get(kernel_initializer),
        bias_initializer=initializers.get(bias_initializer),
        kernel_regularizer=regularizers.get(kernel_regularizer),
        bias_regularizer=regularizers.get(bias_regularizer),
        activity_regularizer=regularizers.get(activity_regularizer),
        kernel_constraint=constraints.get(kernel_constraint),
        bias_constraint=constraints.get(bias_constraint),
        **kwargs)

输入方式不同于pytorch. 

填充方式未valid 

import tensorflow as tf

input=tf.random.normal((6,1),mean=0,stddev=1)
input=tf.reshape(input,(1,6,1))
print('输入维度',input.shape)

model=tf.keras.layers.Conv1D(filters=32,kernel_size=3,strides=1,activation='relu',input_shape=input.shape[1:],padding='valid')
output=model(input)
print('输出维度',output.shape)

 

 填充方式为same

import tensorflow as tf

input=tf.random.normal((6,1),mean=0,stddev=1)
input=tf.reshape(input,(1,6,1))
print('输入维度',input.shape)

model=tf.keras.layers.Conv1D(filters=32,kernel_size=3,strides=1,activation='relu',input_shape=input.shape[1:],padding='same')
output=model(input)
print('输出维度',output.shape)

 

Conv2d

输入数据为图像数据。输入格式为h,w,c。比如输入为（28，28，1），28x28表示单通道的图像。1表示的图像的通道（即表示有几个这样的图像）

类比图像数据，Conv1d的输入为（6，1），其中6表示向量的大小，1表示有一个这样的向量

import tensorflow as tf

input=tf.random.normal((28,28,1),mean=0,stddev=1)
input=tf.reshape(input,(1,28,28,1))
print('输入维度',input.shape)
model=tf.keras.layers.Conv2D(filters=32,kernel_size=3,strides=1,activation='relu',input_shape=input.shape[1:],padding='same')
output=model(input)
print('输出维度',output.shape)

  

空洞率（dilation_rate）。即在原始卷积核中间填充0，使卷积核的大小发生变化。

>>> # With `dilation_rate` as 2.
>>> input_shape = (4, 28, 28, 3)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv2D(
... 2, 3, activation='relu', dilation_rate=2, input_shape=input_shape[1:])(x)
>>> print(y.shape)
(4, 24, 24, 2)

Conv3d

 spatial convolution over volumes  体积上的空间卷积

>>> # The inputs are 28x28x28 volumes with a single channel, and the
>>> # batch size is 4
>>> input_shape =(4, 28, 28, 28, 1)
>>> x = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Conv3D(
... 2, 3, activation='relu', input_shape=input_shape[1:])(x)
>>> print(y.shape)
(4, 26, 26, 26, 2)

import tensorflow as tf

input=tf.random.normal((28,28,28,1),mean=0,stddev=1)
input=tf.reshape(input,(1,28,28,28,1))
print('输入维度',input.shape)

model=tf.keras.layers.Conv3D(filters=32,kernel_size=3,strides=1,activation='relu',input_shape=input.shape[1:],padding='same')
output=model(input)
print('输出维度',output.shape)