Tensorflow2 tf.nn.con2d()进行卷积运算及其可视化

Posted 2021-09-08 空中旋转篮球

1.tf.nn.con2d()函数介绍

tf.nn.con2d()函数参数如下图所示：

tf.nn.conv2d(
    input,
    filter,
    strides,
    padding,
    use_cudnn_on_gpu=True,
    data_format='NHWC',
    dilations=[1, 1, 1, 1],
    name=None
)

参数说明参考：https://tensorflow.google.cn/api_docs/python/tf/nn/conv2d

Args
input	A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A Tensor of rank at least 4. The dimension order is interpreted according to the value of data_format; with the all-but-inner-3 dimensions acting as batch dimensions. See below for details.
filters	A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
strides	An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
padding	Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top,pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0],[pad_top, pad_bottom], [pad_left, pad_right]].
data_format	An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: batch_shape + [height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: batch_shape + [channels, height, width].
dilations	An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
name	A name for the operation (optional).

2.tf.nn.con2d()卷积运算

2.1 使用数据

flower_photos数据集中选一张玫瑰花图片

2.2代码实现卷积运算

2.2.1 使用引用包

import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import tensorflow as tf

2.2.2显示读取显示图片

data = Image.open("roses_483444865_65962cea07_m.jpg")	#返回一个PIL图像对象
plt.imshow(data)
plt.show()

2.2.3对图片进行维度变换

变换为input参数所需格式，即：

[batch_size，filter_height, filter_width, in_channels]

这里是[1,240,180,3]

x=np.array(data)
x=x/255
x=x.reshape(1,240,180,3)
print(x.shape)

 2.2.4 数组转化为张量

image_tensor=tf.convert_to_tensor(x)
x_input = tf.cast(image_tensor,tf.float32)

 2.2.5设置filter参数

这里使用kernel_in命名，即卷积核。维度为[3,2,1,3]([filter_height, filter_width, in_channels, out_channels])out_channel需要和input参数最后一个相同。

padding包含"SAME"和"VALID"，"SAME"输出结果保持一致的维度，"VALID"根据步长等参数有所变化。

kernel_in = np.array([
     [[[1, 1, 1]], [[1, 1, 1]]],
     [[[1, 1, 1]], [[1, 1, 1]]],
    [[[1, 1, 1]], [[1, 1, 1]]]])
kernel= tf.constant(kernel_in, dtype=tf.float32)
z=tf.nn.conv2d(x_input, kernel, strides=[1,1,1,1], padding='SAME')

2.2.6 结果显示

x_conv2d=np.array(z)
x_conv2d=x_conv2d.reshape(240,180,3)
#x_conv2d=np.array(x_conv2d,dtype=float)
print(x_conv2d)
plt.imshow(x_conv2d)
plt.show()

 2.2.7 不同卷积核结果显示

变换kernel

kernel_in = np.array([
     [[[-1, 0, 1]],[[-2, 0, 2]],[[-1, 0, 1]]],
     [[[-1, 0, 1]],[[-2, 0, 2]],[[-1, 0, 1]]],
    [[[-1, 0, 1]],[[-2, 0, 2]],[[-1, 0, 1]]]])

 3.完整代码

import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import tensorflow as tf

data = Image.open("roses_483444865_65962cea07_m.jpg")	#返回一个PIL图像对象
plt.imshow(data)
plt.show()

x=np.array(data)
x=x/255
x=x.reshape(1,240,180,3)


image_tensor=tf.convert_to_tensor(x)
x_input = tf.cast(image_tensor,tf.float32)

print("x_in{}",x_input.shape)

kernel_in = np.array([
     [[[-1, 0, 1]],[[-2, 0, 2]],[[-1, 0, 1]]],
     [[[-1, 0, 1]],[[-2, 0, 2]],[[-1, 0, 1]]],
    [[[-1, 0, 1]],[[-2, 0, 2]],[[-1, 0, 1]]]])
kernel = tf.constant(kernel_in, dtype=tf.float32)
z=tf.nn.conv2d(x_input, kernel, strides=[1,1,1,1], padding='SAME')

x_conv2d=np.array(z)
x_conv2d=x_conv2d.reshape(240,180,3)
plt.imshow(x_conv2d)
plt.show()