【Python机器学习】实验16 卷积、下采样、经典卷积网络

这篇具有很好参考价值的文章主要介绍了【Python机器学习】实验16 卷积、下采样、经典卷积网络。希望对大家有所帮助。如果存在错误或未考虑完全的地方,请大家不吝赐教,您也可以点击"举报违法"按钮提交疑问。

卷积、下采样、经典卷积网络

1. 对图像进行卷积处理

import cv2
path = 'data\instance\p67.jpg' 
input_img = cv2.imread(path)
import cv2 
import numpy as np 
#分别将三个通道进行卷积,然后合并通道

def conv(image, kernel): 
    conv_b = convolve(image[:, :, 0], kernel) 
    conv_g = convolve(image[:, :, 1], kernel) 
    conv_r = convolve(image[:, :, 2], kernel) 
    output = np.dstack([conv_b, conv_g, conv_r]) 
    return output


#卷积处理
def convolve(image, kernel): 
    h_kernel, w_kernel = kernel.shape 
    h_image, w_image = image.shape
    h_output = h_image - h_kernel + 1 
    w_output = w_image - w_kernel + 1 
    output = np.zeros((h_output, w_output), np.uint8) 
    for i in range(h_output): 
        for j in range(w_output): 
            output[i, j] = np.multiply(image[i:i + h_kernel, j:j + w_kernel], kernel).sum() 
    return output

if __name__ == '__main__': 
    path = 'data\instance\p67.jpg' 
    input_img = cv2.imread(path) 
    # 1.锐化卷积核 
    #kernel = np.array([[-1,-1,-1],[-1,9,-1],[-1,-1,-1]]) 
    # 2.模糊卷积核
    kernel = np.array([[0.1,0.1,0.1],[0.1,0.2,0.1],[0.1,0.1,0.1]])     
    output_img = conv(input_img, kernel)
    cv2.imwrite(path.replace('.jpg', '-processed.jpg'), output_img) 
    cv2.imshow('Output Image', output_img) 
    cv2.waitKey(0)

2. 池化

img = cv2.imread('data\instance\dog.jpg')
img.shape
(4064, 3216, 3)
import numpy as np
from PIL import Image
import cv2
import matplotlib.pyplot as plt

#均值池化
def AVGpooling(imgData, strdW, strdH):
    W,H = imgData.shape
    newImg = []
    for i in range(0,W,strdW):
        line = []
        for j in range(0,H,strdH):
            x = imgData[i:i+strdW,j:j+strdH]     #获取当前待池化区域
            avgValue=np.sum(x)/(strdW*strdH)  #求该区域的均值
            line.append(avgValue)     
        newImg.append(line)
    return np.array(newImg)

#最大池化
def MAXpooling(imgData, strdW, strdH):
    W,H = imgData.shape
    newImg = []
    for i in range(0,W,strdW):
        line = []
        for j in range(0,H,strdH):
            x = imgData[i:i+strdW,j:j+strdH]    #获取当前待池化区域
            maxValue=np.max(x)            #求该区域的最大值
            line.append(maxValue)        
        newImg.append(line)
    return np.array(newImg)

img = cv2.imread('data\instance\dog.jpg')
imgData= img[:,:,1]   #绿色通道


#显示原图
plt.subplot(221)
plt.imshow(img)
plt.axis('off')

#显示原始绿通道图
plt.subplot(222)
plt.imshow(imgData)
plt.axis('off')

#显示平均池化结果图
AVGimg = AVGpooling(imgData, 2, 2)
plt.subplot(223)
plt.imshow(AVGimg)
plt.axis('off')

#显示最大池化结果图
MAXimg = MAXpooling(imgData, 2, 2)
plt.subplot(224)
plt.imshow(MAXimg)
plt.axis('off')
plt.show()

【Python机器学习】实验16 卷积、下采样、经典卷积网络,《 机器学习 》,python,机器学习,网络

3. VGGNET

import numpy as np 
from tensorflow.keras import backend as K 
import matplotlib.pyplot as plt 
from tensorflow.keras.applications import vgg16   # Keras内置 VGG-16模块,直接可调用。 
from tensorflow.keras.preprocessing import image 
from tensorflow.keras.applications.vgg16 import preprocess_input
import math
input_size = 224   # 网络输入图像的大小,长宽相等 
kernel_size = 64   # 可视化卷积核的大小,长宽相等 
layer_vis = True    # 特征图是否可视化
kernel_vis = True   # 卷积核是否可视化
each_layer = False  # 卷积核可视化是否每层都做
which_layer = 1    # 如果不是每层都做,那么第几个卷积层
path = 'data\instance\p67.jpg' 


img = image.load_img(path, target_size=(input_size, input_size)) 
img = image.img_to_array(img) 
img = np.expand_dims(img, axis=0) 
img = preprocess_input(img)  #标准化预处理
model = vgg16.VGG16(include_top=True, weights='imagenet')


def network_configuration(): 
    all_channels = [64, 64, 64, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512, 512, 512, 512, 512] 
    down_sampling = [1, 1, 1 / 2, 1 / 2, 1 / 2, 1 / 4, 1 / 4, 1 / 4, 1 / 4, 1 / 8, 1 / 8, 1 / 8, 1 / 8, 1 / 16, 1 / 16, 1 / 16, 1 / 16, 1 / 32] 
    conv_layers = [1, 2, 4, 5, 7, 8, 9, 11, 12, 13, 15, 16, 17] 
    conv_channels = [64, 64, 128, 128, 256, 256, 256, 512, 512, 512, 512, 512, 512] 
    return all_channels, down_sampling, conv_layers, conv_channels

def layer_visualization(model, img, layer_num, channel, ds): 
    # 设置可视化的层 
    layer = K.function([model.layers[0].input], [model.layers[layer_num].output]) 
    f = layer([img])[0] 
    feature_aspect = math.ceil(math.sqrt(channel)) 
    single_size = int(input_size * ds)
    plt.figure(figsize=(8, 8.5)) 
    plt.suptitle('Layer-' + str(layer_num), fontsize=22)
    plt.subplots_adjust(left=0.02, bottom=0.02, right=0.98, top=0.94, wspace=0.05, hspace=0.05) 
    for i_channel in range(channel): 
        print('Channel-{} in Layer-{} is running.'.format(i_channel + 1, layer_num)) 
        show_img = f[:, :, :, i_channel] 
        show_img = np.reshape(show_img, (single_size, single_size)) 
        plt.subplot(feature_aspect, feature_aspect, i_channel + 1) 
        plt.imshow(show_img)  
        plt.axis('off') 
    fig = plt.gcf() 
    fig.savefig('data/instance/feature_kernel_images/layer_' + str(layer_num).zfill(2) + '.png', format='png', dpi=300)
    plt.show()
    
all_channels, down_sampling, conv_layers, conv_channels = network_configuration()
if layer_vis: 
    for i in range(len(all_channels)): 
        layer_visualization(model, img, i + 1, all_channels[i], down_sampling[i])

4. 采用预训练的Resnet实现猫狗识别

from tensorflow.keras.applications.resnet50 import ResNet50 
from tensorflow.keras.preprocessing import image 
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions 
import numpy as np 
from PIL import ImageFont, ImageDraw, Image 
import cv2
img_path = 'data\instance\dog.jpg'     #进行狗的判断
#img_path = 'cat.jpg'     #进行猫的判断
#img_path = 'deer.jpg'    #进行鹿的判断
weights_path = 'resnet50_weights.h5'
img = image.load_img(img_path, target_size=(224, 224)) 
x = image.img_to_array(img) 
x = np.expand_dims(x, axis=0) 
x = preprocess_input(x)
def get_model(): 
    model = ResNet50(weights=weights_path) 
    # 导入模型以及预训练权重
    print(model.summary()) # 打印模型概况 
    return model
model = get_model() 

Model: "resnet50"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_4 (InputLayer)           [(None, 224, 224, 3  0           []                               
                                )]                                                                
                                                                                                  
 conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_4[0][0]']                
                                                                                                  
 conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              
                                )                                                                 
                                                                                                  
 conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             
                                )                                                                 
                                                                                                  
 conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               
                                )                                                                 
                                                                                                  
 pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             
                                )                                                                 
                                                                                                  
 pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              
                                                                                                  
 conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             
                                                                                                  
 conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    
                                                                                                  
 conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             
                                                                                                  
 conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    
                                                                                                  
 conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      
                                                                  'conv2_block1_3_bn[0][0]']      
                                                                                                  
 conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       
                                                                                                  
 conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       
                                                                                                  
 conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    
                                                                                                  
 conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    
                                                                                                  
 conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       
                                                                  'conv2_block2_3_bn[0][0]']      
                                                                                                  
 conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       
                                                                                                  
 conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       
                                                                                                  
 conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    
                                                                                                  
 conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    
                                                                                                  
 conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       
                                                                  'conv2_block3_3_bn[0][0]']      
                                                                                                  
 conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       
                                                                                                  
 conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       
                                                                                                  
 conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    
                                                                                                  
 conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       
                                                                                                  
 conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    
                                                                                                  
 conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      
                                                                  'conv3_block1_3_bn[0][0]']      
                                                                                                  
 conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       
                                                                                                  
 conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       
                                                                                                  
 conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    
                                                                                                  
 conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    
                                                                                                  
 conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       
                                                                  'conv3_block2_3_bn[0][0]']      
                                                                                                  
 conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       
                                                                                                  
 conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       
                                                                                                  
 conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    
                                                                                                  
 conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    
                                                                                                  
 conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       
                                                                  'conv3_block3_3_bn[0][0]']      
                                                                                                  
 conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       
                                                                                                  
 conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       
                                                                                                  
 conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    
                                                                                                  
 conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    
                                                                                                  
 conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       
                                                                  'conv3_block4_3_bn[0][0]']      
                                                                                                  
 conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       
                                                                                                  
 conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       
                                                                                                  
 conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    
                                                                                                  
 conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      
                                )                                 'conv4_block1_3_bn[0][0]']      
                                                                                                  
 conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       
                                                                                                  
 conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    
                                                                                                  
 conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       
                                )                                 'conv4_block2_3_bn[0][0]']      
                                                                                                  
 conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       
                                                                                                  
 conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    
                                                                                                  
 conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       
                                )                                 'conv4_block3_3_bn[0][0]']      
                                                                                                  
 conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       
                                                                                                  
 conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    
                                                                                                  
 conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       
                                )                                 'conv4_block4_3_bn[0][0]']      
                                                                                                  
 conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       
                                                                                                  
 conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    
                                                                                                  
 conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       
                                )                                 'conv4_block5_3_bn[0][0]']      
                                                                                                  
 conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       
                                                                                                  
 conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    
                                                                                                  
 conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       
                                )                                 'conv4_block6_3_bn[0][0]']      
                                                                                                  
 conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       
                                                                                                  
 conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    
                                                                                                  
 conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       
                                                                                                  
 conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    
                                                                                                  
 conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      
                                                                  'conv5_block1_3_bn[0][0]']      
                                                                                                  
 conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       
                                                                                                  
 conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       
                                                                                                  
 conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    
                                                                                                  
 conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    
                                                                                                  
 conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       
                                                                  'conv5_block2_3_bn[0][0]']      
                                                                                                  
 conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       
                                                                                                  
 conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       
                                                                                                  
 conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    
                                                                                                  
 conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    
                                                                                                  
 conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       
                                                                  'conv5_block3_3_bn[0][0]']      
                                                                                                  
 conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       
                                                                                                  
 avg_pool (GlobalAveragePooling  (None, 2048)        0           ['conv5_block3_out[0][0]']       
 2D)                                                                                              
                                                                                                  
 predictions (Dense)            (None, 1000)         2049000     ['avg_pool[0][0]']               
                                                                                                  
==================================================================================================
Total params: 25,636,712
Trainable params: 25,583,592
Non-trainable params: 53,120
__________________________________________________________________________________________________
None
preds = model.predict(x)
1/1 [==============================] - 1s 854ms/step
print('Predicted:', decode_predictions(preds, top=5)[0])
Predicted: [('n02108422', 'bull_mastiff', 0.3921146), ('n02110958', 'pug', 0.2944119), ('n02093754', 'Border_terrier', 0.14356579), ('n02108915', 'French_bulldog', 0.057976846), ('n02099712', 'Labrador_retriever', 0.052499186)]

TensorFlow2.2基本应用

import tensorflow as tf
x=tf.random.normal([2,16])
w1=tf.Variable(tf.random.truncated_normal([16,8],stddev=0.1))
b1=tf.Variable(tf.zeros([8]))
o1=tf.matmul(x,w1)+b1
o1=tf.nn.relu(o1)
o1

<tf.Tensor: id=8263, shape=(2, 8), dtype=float32, numpy=
array([[0.16938789, 0. , 0.08883161, 0.14095941, 0.34751543,
0.353898 , 0. , 0.13356908],
[0. , 0. , 0.48546872, 0.37623546, 0.5447475 ,
0.21755993, 0.40121362, 0. ]], dtype=float32)>

from tensorflow.keras import layers
x=tf.random.normal([4,16*16])
fc=layers.Dense(5,activation=tf.nn.relu)
h1=fc(x)
h1

<tf.Tensor: id=8296, shape=(4, 5), dtype=float32, numpy=
array([[0. , 0. , 0. , 0.14286758, 0. ],
[0. , 2.2727172 , 0. , 0. , 0.34961763],
[0.1311972 , 0. , 1.4005635 , 0. , 0. ],
[0. , 1.7266206 , 0.64711714, 1.3494569 , 0. ]],
dtype=float32)>

#获取权值矩阵w
fc.kernel

<tf.Variable ‘dense/kernel:0’ shape=(256, 5) dtype=float32, numpy=
array([[-0.0339304 , 0.02273461, -0.12746884, 0.14963049, 0.00773269],
[-0.05978647, 0.07886668, -0.09110804, 0.14902723, 0.13007113],
[ 0.10187459, 0.13089484, 0.14367685, 0.12212327, -0.06235344],
…,
[ 0.10417426, 0.05112691, 0.12206474, 0.01141772, -0.05271714],
[ 0.03493455, -0.13473712, -0.01317982, -0.09485313, 0.04731715],
[ 0.12421742, 0.00030141, -0.00211757, -0.04196439, -0.03638943]],
dtype=float32)>

fc.bias

<tf.Variable ‘dense/bias:0’ shape=(5,) dtype=float32, numpy=array([0., 0., 0., 0., 0.], dtype=float32)>

fc.trainable_variables

[<tf.Variable ‘dense/kernel:0’ shape=(256, 5) dtype=float32, numpy=
array([[-0.0339304 , 0.02273461, -0.12746884, 0.14963049, 0.00773269],
[-0.05978647, 0.07886668, -0.09110804, 0.14902723, 0.13007113],
[ 0.10187459, 0.13089484, 0.14367685, 0.12212327, -0.06235344],
…,
[ 0.10417426, 0.05112691, 0.12206474, 0.01141772, -0.05271714],
[ 0.03493455, -0.13473712, -0.01317982, -0.09485313, 0.04731715],
[ 0.12421742, 0.00030141, -0.00211757, -0.04196439, -0.03638943]],
dtype=float32)>,
<tf.Variable ‘dense/bias:0’ shape=(5,) dtype=float32, numpy=array([0., 0., 0., 0., 0.], dtype=float32)>]

fc.variables

[<tf.Variable ‘dense/kernel:0’ shape=(256, 5) dtype=float32, numpy=
array([[-0.0339304 , 0.02273461, -0.12746884, 0.14963049, 0.00773269],
[-0.05978647, 0.07886668, -0.09110804, 0.14902723, 0.13007113],
[ 0.10187459, 0.13089484, 0.14367685, 0.12212327, -0.06235344],
…,
[ 0.10417426, 0.05112691, 0.12206474, 0.01141772, -0.05271714],
[ 0.03493455, -0.13473712, -0.01317982, -0.09485313, 0.04731715],
[ 0.12421742, 0.00030141, -0.00211757, -0.04196439, -0.03638943]],
dtype=float32)>,
<tf.Variable ‘dense/bias:0’ shape=(5,) dtype=float32, numpy=array([0., 0., 0., 0., 0.], dtype=float32)>]文章来源地址https://www.toymoban.com/news/detail-666264.html

5. 使用深度学习进行手写数字识别

import tensorflow as tf

#载入MNIST 数据集。
mnist = tf.keras.datasets.mnist
#拆分数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()
#将样本进行预处理,并从整数转换为浮点数
x_train, x_test = x_train / 255.0, x_test / 255.0

#使用tf.keras.Sequential将模型的各层堆叠,并设置参数
model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation='softmax')
])
#设置模型的优化器和损失函数
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
#训练并验证模型
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test,  y_test, verbose=2)
Train on 60000 samples
Epoch 1/5
60000/60000 [==============================] - 6s 95us/sample - loss: 0.2931 - accuracy: 0.9146
Epoch 2/5
60000/60000 [==============================] - 5s 77us/sample - loss: 0.1419 - accuracy: 0.9592
Epoch 3/5
60000/60000 [==============================] - 5s 78us/sample - loss: 0.1065 - accuracy: 0.9683
Epoch 4/5
60000/60000 [==============================] - 5s 78us/sample - loss: 0.0852 - accuracy: 0.9738
Epoch 5/5
60000/60000 [==============================] - 6s 100us/sample - loss: 0.0735 - accuracy: 0.9769
10000/1 - 0s - loss: 0.0338 - accuracy: 0.9795
[0.0666636555833742, 0.9795]

附:系列文章

实验 目录 直达链接
1 Numpy以及可视化回顾 https://want595.blog.csdn.net/article/details/131891689
2 线性回归 https://want595.blog.csdn.net/article/details/131892463
3 逻辑回归 https://want595.blog.csdn.net/article/details/131912053
4 多分类实践(基于逻辑回归) https://want595.blog.csdn.net/article/details/131913690
5 机器学习应用实践-手动调参 https://want595.blog.csdn.net/article/details/131934812
6 贝叶斯推理 https://want595.blog.csdn.net/article/details/131947040
7 KNN最近邻算法 https://want595.blog.csdn.net/article/details/131947885
8 K-means无监督聚类 https://want595.blog.csdn.net/article/details/131952371
9 决策树 https://want595.blog.csdn.net/article/details/131991014
10 随机森林和集成学习 https://want595.blog.csdn.net/article/details/132003451
11 支持向量机 https://want595.blog.csdn.net/article/details/132010861
12 神经网络-感知器 https://want595.blog.csdn.net/article/details/132014769
13 基于神经网络的回归-分类实验 https://want595.blog.csdn.net/article/details/132127413
14 手写体卷积神经网络 https://want595.blog.csdn.net/article/details/132223494
15 将Lenet5应用于Cifar10数据集 https://want595.blog.csdn.net/article/details/132223751
16 卷积、下采样、经典卷积网络 https://want595.blog.csdn.net/article/details/132223985

到了这里,关于【Python机器学习】实验16 卷积、下采样、经典卷积网络的文章就介绍完了。如果您还想了解更多内容,请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章,希望大家以后多多支持TOY模板网!

本文来自互联网用户投稿,该文观点仅代表作者本人,不代表本站立场。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如若转载,请注明出处: 如若内容造成侵权/违法违规/事实不符,请点击违法举报进行投诉反馈,一经查实,立即删除!

领支付宝红包 赞助服务器费用

相关文章

  • 【Python机器学习】卷积神经网络卷积层、池化层、Flatten层、批标准化层的讲解(图文解释)

    卷积神经网络(convolutional neural network, CNN)在提出之初被成功应用于手写字符图像识别,2012年的AlexNet网络在图像分类任务中取得成功,此后,卷积神经网络发展迅速,现在已经被广泛应用于图形、图像、语音识别等领域。 图片的像素数往往非常大,如果用多层全连接网络来

    2024年02月08日
    浏览(43)
  • 【Python机器学习】实验11 神经网络-感知器

    1.感知机是根据输入实例的特征向量 x x x 对其进行二类分类的线性分类模型: f ( x ) = sign ⁡ ( w ⋅ x + b ) f(x)=operatorname{sign}(w cdot x+b) f ( x ) = sign ( w ⋅ x + b ) 感知机模型对应于输入空间(特征空间)中的分离超平面 w ⋅ x + b = 0 w cdot x+b=0 w ⋅ x + b = 0 。 2.感知机学习的策略

    2024年02月13日
    浏览(40)
  • 深度学习12. CNN经典网络 VGG16

    VGG(Visual Geometry Group)是一个视觉几何组在2014年提出的深度卷积神经网络架构。 VGG在2014年ImageNet图像分类竞赛亚军,定位竞赛冠军;VGG网络采用连续的小卷积核(3x3)和池化层构建深度神经网络,网络深度可以达到16层或19层,其中VGG16和VGG19最为著名。 VGG16和VGG19网络架构非

    2023年04月09日
    浏览(36)
  • 毕设项目分享 基于机器视觉opencv的手势检测 手势识别 算法 - 深度学习 卷积神经网络 opencv python

    今天学长向大家介绍一个机器视觉项目 基于机器视觉opencv的手势检测 手势识别 算法 普通机器视觉手势检测的基本流程如下: 其中轮廓的提取,多边形拟合曲线的求法,凸包集和凹陷集的求法都是采用opencv中自带的函数。手势数字的识别是利用凸包点以及凹陷点和手部中心

    2024年02月03日
    浏览(81)
  • Python基于深度学习机器学习卷积神经网络实现垃圾分类垃圾识别系统(GoogLeNet,Resnet,DenseNet,MobileNet,EfficientNet,Shufflent)

    文章目录 1 前言+ 2 卷积神经网络(CNN)详解+ 2.1 CNN架构概述+ 2.1.1 卷积层+ 2.1.2 池化层+ 2.1.3 全连接层 2.2 CNN训练过程+ 2.3 CNN在垃圾图片分类中的应用 3 代码详解+ 3.1 导入必要的库+ 3.2 加载数据集+ 3.3 可视化随机样本+ 3.4 数据预处理与生成器+ 3.5 构建、编译和训练CNN模型+ 3.5.

    2024年02月04日
    浏览(54)
  • 软件工程毕设 基于机器视觉opencv的手势检测 手势识别 算法 - 深度学习 卷积神经网络 opencv python

    今天学长向大家介绍一个机器视觉项目 基于机器视觉opencv的手势检测 手势识别 算法 普通机器视觉手势检测的基本流程如下: 其中轮廓的提取,多边形拟合曲线的求法,凸包集和凹陷集的求法都是采用opencv中自带的函数。手势数字的识别是利用凸包点以及凹陷点和手部中心

    2024年01月17日
    浏览(79)
  • 计算机竞赛 题目:基于机器视觉opencv的手势检测 手势识别 算法 - 深度学习 卷积神经网络 opencv python

    🔥 优质竞赛项目系列,今天要分享的是 基于机器视觉opencv的手势检测 手势识别 算法 该项目较为新颖,适合作为竞赛课题方向,学长非常推荐! 🧿 更多资料, 项目分享: https://gitee.com/dancheng-senior/postgraduate 普通机器视觉手势检测的基本流程如下: 其中轮廓的提取,多边形

    2024年02月07日
    浏览(81)
  • 深度学习(16)--基于经典网络架构resnet训练图像分类模型

    目录 一.项目介绍 二.项目流程详解 2.1.引入所需的工具包 2.2.数据读取和预处理 2.3.加载resnet152模型 2.4.初始化模型 2.5.设置需要更新的参数 2.6.训练模块设置 2.7.再次训练所有层 2.8.测试网络效果 三.完整代码 使用PyTorch工具包调用经典网络架构resnet训练图像分类模型,用于分辨

    2024年02月20日
    浏览(42)
  • 深度学习实验3 - 卷积神经网络

    手写二维卷积的实现,并在至少一个数据集上进行实验,从训练时间、预测精度、Loss变化等角度分析实验结果(最好使用图表展示) 使用torch.nn实现二维卷积,并在至少一个数据集上进行实验,从训练时间、预测精度、Loss变化等角度分析实验结果(最好使用图表展示) 不同

    2024年02月14日
    浏览(49)
  • 机器学习 | 卷积神经网络

    采用任意一种课程中介绍过的或者其它卷积神经网络模型(例如LeNet-5、AlexNet等)用于解决某种媒体类型的模式识别问题。 卷积神经网络可以基于现有框架如TensorFlow、Pytorch或者Mindspore等构建,也可以自行设计实现。 数据集可以使用手写体数字图像标准数据集,也可以自行构

    2024年01月17日
    浏览(46)

觉得文章有用就打赏一下文章作者

支付宝扫一扫打赏

博客赞助

微信扫一扫打赏

请作者喝杯咖啡吧~博客赞助

支付宝扫一扫领取红包,优惠每天领

二维码1

领取红包

二维码2

领红包