TensorflowMac上Tensorflow卷积与反卷积

Posted 2022-11-21 Taily老段

卷积

tf.nn.conv2d是TensorFlow里面实现卷积的函数，参考文档对它的介绍并不是很详细，实际上这是搭建卷积神经网络比较核心的一个方法，非常重要

tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, name=None)

除去name参数用以指定该操作的name，与方法有关的一共五个参数：
第一个参数input：指需要做卷积的输入图像，它要求是一个Tensor，具有[batch, in_height, in_width, in_channels]这样的shape，具体含义是[训练时一个batch的图片数量, 图片高度, 图片宽度, 图像通道数]，注意这是一个4维的Tensor，要求类型为float32和float64其中之一

第二个参数filter：相当于CNN中的卷积核，它要求是一个Tensor，具有[filter_height, filter_width, in_channels, out_channels]这样的shape，具体含义是[卷积核的高度，卷积核的宽度，图像通道数，卷积核个数]，要求类型与参数input相同，有一个地方需要注意，第三维in_channels，就是参数input的第四维

第三个参数strides：卷积时在图像每一维的步长，这是一个一维的向量，长度4

第四个参数padding：string类型的量，只能是"SAME","VALID"其中之一，这个值决定了不同的卷积方式（后面会介绍）

第五个参数：use_cudnn_on_gpu:bool类型，是否使用cudnn加速，默认为true

结果返回一个Tensor，这个输出，就是我们常说的feature map

详情见：https://blog.csdn.net/mao_xiao_feng/article/details/53444333

 

反卷积

现在通过stride=2来进行反卷积，使得尺寸由原来的3*3变为6*6.那么在Tensorflow框架中，反卷积的过程如下(不同框架在裁剪这步可能不一样)：

大致步骤就是：先填充0，然后进行卷积，卷积过程跟上一篇文章讲述的一致。最后一步还要进行裁剪。

将输出通道设置为多个，这样反卷积更符合实际场景。

详情见：https://blog.csdn.net/jacke121/article/details/80207796

---

实现一个从1x3x3x1到1x5x5x1的反卷积，kernel大小为2:

#coding:utf-8
import os 
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import tensorflow as tf
import numpy as np

# the format of value: [NHWC]
value = tf.reshape(tf.constant([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]), [1, 3, 3, 1]) 
# the format of filter: [height, width, output_channels, input_channels]
filter = tf.reshape(tf.constant([[1., 0.], [0., 1.]]), [2, 2, 1, 1])
# the format of output_shape: [NHWC]
output_shape = [1, 5, 5, 1]
# the format of strides: [1, stride, stride, 1]
strides = [1, 2, 2, 1]
padding = 'SAME'
# define the transpose conv op
transpose_conv = tf.nn.conv2d_transpose(value=value, filter=filter, output_shape=output_shape, strides=strides, padding=padding)
sess = tf.Session()
sess.run(transpose_conv)

(py27tf) bash-3.2$ python
Python 2.7.15 |Anaconda, Inc.| (default, Dec 14 2018, 13:10:39) 
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> import numpy as np
>>> 
>>> # the format of value: [NHWC]
... value = tf.reshape(tf.constant([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]), [1, 3, 3, 1]) 
>>> # the format of filter: [height, width, output_channels, input_channels]
... filter = tf.reshape(tf.constant([[1., 0.], [0., 1.]]), [2, 2, 1, 1])
>>> # the format of output_shape: [NHWC]
... output_shape = [1, 5, 5, 1]
>>> # the format of strides: [1, stride, stride, 1]
... strides = [1, 2, 2, 1]
>>> padding = 'SAME'
>>> # define the transpose conv op
... transpose_conv = tf.nn.conv2d_transpose(value=value, filter=filter, output_shape=output_shape, strides=strides, padding=padding)
>>> sess = tf.Session()
2018-12-29 16:13:44.769841: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-12-29 16:13:44.770191: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 8. Tune using inter_op_parallelism_threads for best performance.
>>> sess.run(transpose_conv)
array([[[[1.],
         [0.],
         [2.],
         [0.],
         [3.]],

        [[0.],
         [1.],
         [0.],
         [2.],
         [0.]],

        [[4.],
         [0.],
         [5.],
         [0.],
         [6.]],

        [[0.],
         [4.],
         [0.],
         [5.],
         [0.]],

        [[7.],
         [0.],
         [8.],
         [0.],
         [9.]]]], dtype=float32)
>>> 

 实现一个从1x3x3x1到1x5x5x1的反卷积，kernel大小为3:

import tensorflow as tf
import numpy as np

# the format of value: [NHWC]
value = tf.reshape(tf.constant([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]), [1, 3, 3, 1]) 
# the format of filter: [height, width, output_channels, input_channels]
filter = tf.reshape(tf.constant([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]), [3, 3, 1, 1])
# the format of output_shape: [NHWC]
output_shape = [1, 5, 5, 1]
# the format of strides: [1, stride, stride, 1]
strides = [1, 2, 2, 1]
padding = 'SAME'
# define the transpose conv op
transpose_conv = tf.nn.conv2d_transpose(value=value, filter=filter, output_shape=output_shape, strides=strides, padding=padding)
sess = tf.Session()
sess.run(transpose_conv)

>>> import tensorflow as tf
>>> import numpy as np
>>> 
>>> # the format of value: [NHWC]
... value = tf.reshape(tf.constant([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]), [1, 3, 3, 1]) 
>>> # the format of filter: [height, width, output_channels, input_channels]
... filter = tf.reshape(tf.constant([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]), [3, 3, 1, 1])
>>> # the format of output_shape: [NHWC]
... output_shape = [1, 5, 5, 1]
>>> # the format of strides: [1, stride, stride, 1]
... strides = [1, 2, 2, 1]
>>> padding = 'SAME'
>>> # define the transpose conv op
... transpose_conv = tf.nn.conv2d_transpose(value=value, filter=filter, output_shape=output_shape, strides=strides, padding=padding)
>>> sess = tf.Session()
2018-12-29 16:17:56.936252: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-12-29 16:17:56.936597: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 8. Tune using inter_op_parallelism_threads for best performance.
>>> sess.run(transpose_conv)
array([[[[ 1.],
         [ 0.],
         [ 2.],
         [ 0.],
         [ 3.]],

        [[ 0.],
         [ 6.],
         [ 0.],
         [ 8.],
         [ 0.]],

        [[ 4.],
         [ 0.],
         [ 5.],
         [ 0.],
         [ 6.]],

        [[ 0.],
         [12.],
         [ 0.],
         [14.],
         [ 0.]],

        [[ 7.],
         [ 0.],
         [ 8.],
         [ 0.],
         [ 9.]]]], dtype=float32)
>>> 

对于在网络中的使用，最需要注意的一点就是filter中的output_channel和input_channel的顺序，TF会根据stride自动调整在输入数据的中间padding的数量，在满足 padding, output_size, input_size, stride的四者关系的情况下，放心使用就好。理解了stride的含义就可以随便调整自己想要的网络结构啦。 

---

出现报错，在代码中加入下面代码；

2018-12-29 16:05:43.918087: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-12-29 16:05:43.918440: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 8. Tune using inter_op_parallelism_threads for best performance.

import os 
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import os  
os.environ["TF_CPP_MIN_LOG_LEVEL"]='1' # 这是默认的显示等级，显示所有信息  
os.environ["TF_CPP_MIN_LOG_LEVEL"]='2' # 只显示 warning 和 Error   
os.environ["TF_CPP_MIN_LOG_LEVEL"]='3' # 只显示 Error