J1 ResNet-50算法实战与解析
- 🍨 本文為🔗365天深度學習訓練營 中的學習紀錄博客
- 🍖 原作者：K同学啊 | 接輔導、項目定制
一、理论知识储备
1. 残差网络的由来
ResNet主要解决了CNN在深度加深时的退化问题（梯度消失与梯度爆炸）。 虽然BN可以在一定程度上保持梯度的大小稳定，但当层级数加大时不但不容易收敛，还容易出现准确率饱和并迅速下降，这一下降由网络过于复杂导致。
ResNet有一个额外的分支把输入直接连在输出上，使输出为分支输出+卷积输出，通过人为制造恒等映射使整个网络朝恒等映射的方向去收敛。
复杂网络通用规则：如果一个网络通过简单的手工设置参数值就可以达到想要的结果，那这种结构很容易通过训练来收敛到该结果
较浅的ResNet网络（左）：两层残差单元包含两个相同输出通道数的3x3卷积。
较深的ResNet网络（右）：先用1x1卷积进行降维，然后3x3卷积，最后用1x1升维恢复原有维度，又称bottleneck结构。
2. ResNet50
包含两个基本块：Conv Block和Identity Block
二、前期准备
1. 导入数据
import matplotlib.pyplot as plt
# set the font to SimHei to display Chinese characters
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False import os, PIL, pathlib
import numpy as npfrom tensorflow import keras
from tensorflow.keras import layers,modelsdata_dir = 'C:/Self_Learning/Deep_Learning/K_Codes/data/8_data/bird_photos/'
data_dir = pathlib.Path(data_dir)2. 查看数据
# Check the data
image_count = len(list(data_dir.glob('*/*.jpg')))
print('Total images:', image_count)
三、数据预处理
1. 加载数据
# Load the data
batch_size = 8
img_height = 224
img_width = 224train_ds = keras.preprocessing.image_dataset_from_directory(data_dir,validation_split=0.2,subset='training',seed=123,image_size=(img_height, img_width),batch_size=batch_size)
val_ds = keras.preprocessing.image_dataset_from_directory(data_dir,validation_split=0.2,subset='validation',seed=123,image_size=(img_height, img_width),batch_size=batch_size)
# Check the class names
class_names = train_ds.class_names
print(class_names)
2. 数据可视化
# Visualize the data
plt.figure(figsize=(10, 5))for images, labels in train_ds.take(1):for i in range(8):ax = plt.subplot(2, 4, i + 1)image = images[i].numpy().astype("uint8")plt.imshow(image)plt.title(class_names[labels[i]])plt.axis("off")plt.show()
3. 检查数据
# Check the data shape
for image_batch, labels_batch in train_ds:print(image_batch.shape)print(labels_batch.shape)break
4. 配置数据集
AUTOTUNE = tf.data.AUTOTUNE
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)四、训练模型
1. 构建ResNet-50模型
# Define the ResNet50 modeldef identity_block(input_tensor, kernel_size, filters, stage, block):filters1, filters2, filters3 = filtersname_base = str(stage) + block + '_identity_block_'x = Conv2D(filters1, (1, 1), name=name_base + 'conv1')(input_tensor)x = BatchNormalization(name=name_base + 'bn1')(x)x = Activation('relu', name=name_base + 'relu1')(x)x = Conv2D(filters2, kernel_size, padding='same', name=name_base + 'conv2')(x)x = BatchNormalization(name=name_base + 'bn2')(x)x = Activation('relu', name=name_base + 'relu2')(x)x = Conv2D(filters3, (1, 1), name=name_base + 'conv3')(x)x = BatchNormalization(name=name_base + 'bn3')(x)x = layers.add([x, input_tensor], name=name_base + 'add')x = Activation('relu', name=name_base + 'relu4')(x)return xdef conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):filters1, filters2, filters3 = filtersres_name_base = str(stage) + block + '_conv_block_res_'name_base = str(stage) + block + '_conv_block_'x = Conv2D(filters1, (1, 1), strides=strides, name=name_base + 'conv1')(input_tensor)x = BatchNormalization(name=name_base + 'bn1')(x)x = Activation('relu', name=name_base + 'relu1')(x)x = Conv2D(filters2, kernel_size, padding='same', name=name_base + 'conv2')(x)x = BatchNormalization(name=name_base + 'bn2')(x)x = Activation('relu', name=name_base + 'relu2')(x)x = Conv2D(filters3, (1, 1), name=name_base + 'conv3')(x)x = BatchNormalization(name=name_base + 'bn3')(x)shortcut = Conv2D(filters3, (1, 1), strides=strides, name=res_name_base + 'conv')(input_tensor)shortcut = BatchNormalization(name=res_name_base + 'bn')(shortcut)x = layers.add([x, shortcut], name=name_base + 'add')x = Activation('relu', name=name_base + 'relu4')(x)return xdef ResNet50(input_shape=(224, 224, 3), classes=1000):img_input = Input(shape=input_shape)x = ZeroPadding2D((3, 3))(img_input)x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)x = BatchNormalization(name='bn_conv1')(x)x = Activation('relu')(x)x = MaxPooling2D((3, 3), strides=(2, 2))(x)x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')x = AveragePooling2D((7, 7), name='avg_pool')(x)x = Flatten()(x)x = Dense(classes, activation='softmax', name='fc1000')(x)model = Model(img_input, x, name='ResNet50')model.load_weights('C:/Self_Learning/Deep_Learning/K_Codes/data/8_data/ResNet50_weights_tf_dim_ordering_tf_kernels.h5', by_name=True, skip_mismatch=True)return modelmodel = ResNet50()
model.summary()Model: "ResNet50"
__________________________________________________________________________________________________Layer (type) Output Shape Param # Connected to
==================================================================================================input_3 (InputLayer) [(None, 224, 224, 3)] 0 [] zero_padding2d_2 (ZeroPadd (None, 230, 230, 3) 0 ['input_3[0][0]'] ing2D) conv1 (Conv2D) (None, 112, 112, 64) 9472 ['zero_padding2d_2[0][0]'] bn_conv1 (BatchNormalizati (None, 112, 112, 64) 256 ['conv1[0][0]'] on) activation_4 (Activation) (None, 112, 112, 64) 0 ['bn_conv1[0][0]'] max_pooling2d_2 (MaxPoolin (None, 55, 55, 64) 0 ['activation_4[0][0]'] g2D) 2a_conv_block_conv1 (Conv2 (None, 55, 55, 64) 4160 ['max_pooling2d_2[0][0]'] D) 2a_conv_block_bn1 (BatchNo (None, 55, 55, 64) 256 ['2a_conv_block_conv1[0][0]'] rmalization) 2a_conv_block_relu1 (Activ (None, 55, 55, 64) 0 ['2a_conv_block_bn1[0][0]'] ation) 2a_conv_block_conv2 (Conv2 (None, 55, 55, 64) 36928 ['2a_conv_block_relu1[0][0]'] D) 2a_conv_block_bn2 (BatchNo (None, 55, 55, 64) 256 ['2a_conv_block_conv2[0][0]'] rmalization) 2a_conv_block_relu2 (Activ (None, 55, 55, 64) 0 ['2a_conv_block_bn2[0][0]'] ation) 2a_conv_block_conv3 (Conv2 (None, 55, 55, 256) 16640 ['2a_conv_block_relu2[0][0]'] D) 2a_conv_block_res_conv (Co (None, 55, 55, 256) 16640 ['max_pooling2d_2[0][0]'] nv2D) 2a_conv_block_bn3 (BatchNo (None, 55, 55, 256) 1024 ['2a_conv_block_conv3[0][0]'] rmalization) 2a_conv_block_res_bn (Batc (None, 55, 55, 256) 1024 ['2a_conv_block_res_conv[0][0]hNormalization) '] 2a_conv_block_add (Add) (None, 55, 55, 256) 0 ['2a_conv_block_bn3[0][0]', '2a_conv_block_res_bn[0][0]']2a_conv_block_relu4 (Activ (None, 55, 55, 256) 0 ['2a_conv_block_add[0][0]'] ation) 2b_identity_block_conv1 (C (None, 55, 55, 64) 16448 ['2a_conv_block_relu4[0][0]'] onv2D) 2b_identity_block_bn1 (Bat (None, 55, 55, 64) 256 ['2b_identity_block_conv1[0][0chNormalization) ]'] 2b_identity_block_relu1 (A (None, 55, 55, 64) 0 ['2b_identity_block_bn1[0][0]'ctivation) ] 2b_identity_block_conv2 (C (None, 55, 55, 64) 36928 ['2b_identity_block_relu1[0][0onv2D) ]'] 2b_identity_block_bn2 (Bat (None, 55, 55, 64) 256 ['2b_identity_block_conv2[0][0chNormalization) ]'] 2b_identity_block_relu2 (A (None, 55, 55, 64) 0 ['2b_identity_block_bn2[0][0]'ctivation) ] 2b_identity_block_conv3 (C (None, 55, 55, 256) 16640 ['2b_identity_block_relu2[0][0onv2D) ]'] 2b_identity_block_bn3 (Bat (None, 55, 55, 256) 1024 ['2b_identity_block_conv3[0][0chNormalization) ]'] 2b_identity_block_add (Add (None, 55, 55, 256) 0 ['2b_identity_block_bn3[0][0]') , '2a_conv_block_relu4[0][0]']2b_identity_block_relu4 (A (None, 55, 55, 256) 0 ['2b_identity_block_add[0][0]'ctivation) ] 2c_identity_block_conv1 (C (None, 55, 55, 64) 16448 ['2b_identity_block_relu4[0][0onv2D) ]'] 2c_identity_block_bn1 (Bat (None, 55, 55, 64) 256 ['2c_identity_block_conv1[0][0chNormalization) ]'] 2c_identity_block_relu1 (A (None, 55, 55, 64) 0 ['2c_identity_block_bn1[0][0]'ctivation) ] 2c_identity_block_conv2 (C (None, 55, 55, 64) 36928 ['2c_identity_block_relu1[0][0onv2D) ]'] 2c_identity_block_bn2 (Bat (None, 55, 55, 64) 256 ['2c_identity_block_conv2[0][0chNormalization) ]'] 2c_identity_block_relu2 (A (None, 55, 55, 64) 0 ['2c_identity_block_bn2[0][0]'ctivation) ] 2c_identity_block_conv3 (C (None, 55, 55, 256) 16640 ['2c_identity_block_relu2[0][0onv2D) ]'] 2c_identity_block_bn3 (Bat (None, 55, 55, 256) 1024 ['2c_identity_block_conv3[0][0chNormalization) ]'] 2c_identity_block_add (Add (None, 55, 55, 256) 0 ['2c_identity_block_bn3[0][0]') , '2b_identity_block_relu4[0][0]'] 2c_identity_block_relu4 (A (None, 55, 55, 256) 0 ['2c_identity_block_add[0][0]'ctivation) ] 3a_conv_block_conv1 (Conv2 (None, 28, 28, 128) 32896 ['2c_identity_block_relu4[0][0D) ]'] 3a_conv_block_bn1 (BatchNo (None, 28, 28, 128) 512 ['3a_conv_block_conv1[0][0]'] rmalization) 3a_conv_block_relu1 (Activ (None, 28, 28, 128) 0 ['3a_conv_block_bn1[0][0]'] ation) 3a_conv_block_conv2 (Conv2 (None, 28, 28, 128) 147584 ['3a_conv_block_relu1[0][0]'] D) 3a_conv_block_bn2 (BatchNo (None, 28, 28, 128) 512 ['3a_conv_block_conv2[0][0]'] rmalization) 3a_conv_block_relu2 (Activ (None, 28, 28, 128) 0 ['3a_conv_block_bn2[0][0]'] ation) 3a_conv_block_conv3 (Conv2 (None, 28, 28, 512) 66048 ['3a_conv_block_relu2[0][0]'] D) 3a_conv_block_res_conv (Co (None, 28, 28, 512) 131584 ['2c_identity_block_relu4[0][0nv2D) ]'] 3a_conv_block_bn3 (BatchNo (None, 28, 28, 512) 2048 ['3a_conv_block_conv3[0][0]'] rmalization) 3a_conv_block_res_bn (Batc (None, 28, 28, 512) 2048 ['3a_conv_block_res_conv[0][0]hNormalization) '] 3a_conv_block_add (Add) (None, 28, 28, 512) 0 ['3a_conv_block_bn3[0][0]', '3a_conv_block_res_bn[0][0]']3a_conv_block_relu4 (Activ (None, 28, 28, 512) 0 ['3a_conv_block_add[0][0]'] ation) 3b_identity_block_conv1 (C (None, 28, 28, 128) 65664 ['3a_conv_block_relu4[0][0]'] onv2D) 3b_identity_block_bn1 (Bat (None, 28, 28, 128) 512 ['3b_identity_block_conv1[0][0chNormalization) ]'] 3b_identity_block_relu1 (A (None, 28, 28, 128) 0 ['3b_identity_block_bn1[0][0]'ctivation) ] 3b_identity_block_conv2 (C (None, 28, 28, 128) 147584 ['3b_identity_block_relu1[0][0onv2D) ]'] 3b_identity_block_bn2 (Bat (None, 28, 28, 128) 512 ['3b_identity_block_conv2[0][0chNormalization) ]'] 3b_identity_block_relu2 (A (None, 28, 28, 128) 0 ['3b_identity_block_bn2[0][0]'ctivation) ] 3b_identity_block_conv3 (C (None, 28, 28, 512) 66048 ['3b_identity_block_relu2[0][0onv2D) ]'] 3b_identity_block_bn3 (Bat (None, 28, 28, 512) 2048 ['3b_identity_block_conv3[0][0chNormalization) ]'] 3b_identity_block_add (Add (None, 28, 28, 512) 0 ['3b_identity_block_bn3[0][0]') , '3a_conv_block_relu4[0][0]']3b_identity_block_relu4 (A (None, 28, 28, 512) 0 ['3b_identity_block_add[0][0]'ctivation) ] 3c_identity_block_conv1 (C (None, 28, 28, 128) 65664 ['3b_identity_block_relu4[0][0onv2D) ]'] 3c_identity_block_bn1 (Bat (None, 28, 28, 128) 512 ['3c_identity_block_conv1[0][0chNormalization) ]'] 3c_identity_block_relu1 (A (None, 28, 28, 128) 0 ['3c_identity_block_bn1[0][0]'ctivation) ] 3c_identity_block_conv2 (C (None, 28, 28, 128) 147584 ['3c_identity_block_relu1[0][0onv2D) ]'] 3c_identity_block_bn2 (Bat (None, 28, 28, 128) 512 ['3c_identity_block_conv2[0][0chNormalization) ]'] 3c_identity_block_relu2 (A (None, 28, 28, 128) 0 ['3c_identity_block_bn2[0][0]'ctivation) ] 3c_identity_block_conv3 (C (None, 28, 28, 512) 66048 ['3c_identity_block_relu2[0][0onv2D) ]'] 3c_identity_block_bn3 (Bat (None, 28, 28, 512) 2048 ['3c_identity_block_conv3[0][0chNormalization) ]'] 3c_identity_block_add (Add (None, 28, 28, 512) 0 ['3c_identity_block_bn3[0][0]') , '3b_identity_block_relu4[0][0]'] 3c_identity_block_relu4 (A (None, 28, 28, 512) 0 ['3c_identity_block_add[0][0]'ctivation) ] 3d_identity_block_conv1 (C (None, 28, 28, 128) 65664 ['3c_identity_block_relu4[0][0onv2D) ]'] 3d_identity_block_bn1 (Bat (None, 28, 28, 128) 512 ['3d_identity_block_conv1[0][0chNormalization) ]'] 3d_identity_block_relu1 (A (None, 28, 28, 128) 0 ['3d_identity_block_bn1[0][0]'ctivation) ] 3d_identity_block_conv2 (C (None, 28, 28, 128) 147584 ['3d_identity_block_relu1[0][0onv2D) ]'] 3d_identity_block_bn2 (Bat (None, 28, 28, 128) 512 ['3d_identity_block_conv2[0][0chNormalization) ]'] 3d_identity_block_relu2 (A (None, 28, 28, 128) 0 ['3d_identity_block_bn2[0][0]'ctivation) ] 3d_identity_block_conv3 (C (None, 28, 28, 512) 66048 ['3d_identity_block_relu2[0][0onv2D) ]'] 3d_identity_block_bn3 (Bat (None, 28, 28, 512) 2048 ['3d_identity_block_conv3[0][0chNormalization) ]'] 3d_identity_block_add (Add (None, 28, 28, 512) 0 ['3d_identity_block_bn3[0][0]') , '3c_identity_block_relu4[0][0]'] 3d_identity_block_relu4 (A (None, 28, 28, 512) 0 ['3d_identity_block_add[0][0]'ctivation) ] 4a_conv_block_conv1 (Conv2 (None, 14, 14, 256) 131328 ['3d_identity_block_relu4[0][0D) ]'] 4a_conv_block_bn1 (BatchNo (None, 14, 14, 256) 1024 ['4a_conv_block_conv1[0][0]'] rmalization) 4a_conv_block_relu1 (Activ (None, 14, 14, 256) 0 ['4a_conv_block_bn1[0][0]'] ation) 4a_conv_block_conv2 (Conv2 (None, 14, 14, 256) 590080 ['4a_conv_block_relu1[0][0]'] D) 4a_conv_block_bn2 (BatchNo (None, 14, 14, 256) 1024 ['4a_conv_block_conv2[0][0]'] rmalization) 4a_conv_block_relu2 (Activ (None, 14, 14, 256) 0 ['4a_conv_block_bn2[0][0]'] ation) 4a_conv_block_conv3 (Conv2 (None, 14, 14, 1024) 263168 ['4a_conv_block_relu2[0][0]'] D) 4a_conv_block_res_conv (Co (None, 14, 14, 1024) 525312 ['3d_identity_block_relu4[0][0nv2D) ]'] 4a_conv_block_bn3 (BatchNo (None, 14, 14, 1024) 4096 ['4a_conv_block_conv3[0][0]'] rmalization) 4a_conv_block_res_bn (Batc (None, 14, 14, 1024) 4096 ['4a_conv_block_res_conv[0][0]hNormalization) '] 4a_conv_block_add (Add) (None, 14, 14, 1024) 0 ['4a_conv_block_bn3[0][0]', '4a_conv_block_res_bn[0][0]']4a_conv_block_relu4 (Activ (None, 14, 14, 1024) 0 ['4a_conv_block_add[0][0]'] ation) 4b_identity_block_conv1 (C (None, 14, 14, 256) 262400 ['4a_conv_block_relu4[0][0]'] onv2D) 4b_identity_block_bn1 (Bat (None, 14, 14, 256) 1024 ['4b_identity_block_conv1[0][0chNormalization) ]'] 4b_identity_block_relu1 (A (None, 14, 14, 256) 0 ['4b_identity_block_bn1[0][0]'ctivation) ] 4b_identity_block_conv2 (C (None, 14, 14, 256) 590080 ['4b_identity_block_relu1[0][0onv2D) ]'] 4b_identity_block_bn2 (Bat (None, 14, 14, 256) 1024 ['4b_identity_block_conv2[0][0chNormalization) ]'] 4b_identity_block_relu2 (A (None, 14, 14, 256) 0 ['4b_identity_block_bn2[0][0]'ctivation) ] 4b_identity_block_conv3 (C (None, 14, 14, 1024) 263168 ['4b_identity_block_relu2[0][0onv2D) ]'] 4b_identity_block_bn3 (Bat (None, 14, 14, 1024) 4096 ['4b_identity_block_conv3[0][0chNormalization) ]'] 4b_identity_block_add (Add (None, 14, 14, 1024) 0 ['4b_identity_block_bn3[0][0]') , '4a_conv_block_relu4[0][0]']4b_identity_block_relu4 (A (None, 14, 14, 1024) 0 ['4b_identity_block_add[0][0]'ctivation) ] 4c_identity_block_conv1 (C (None, 14, 14, 256) 262400 ['4b_identity_block_relu4[0][0onv2D) ]'] 4c_identity_block_bn1 (Bat (None, 14, 14, 256) 1024 ['4c_identity_block_conv1[0][0chNormalization) ]'] 4c_identity_block_relu1 (A (None, 14, 14, 256) 0 ['4c_identity_block_bn1[0][0]'ctivation) ] 4c_identity_block_conv2 (C (None, 14, 14, 256) 590080 ['4c_identity_block_relu1[0][0onv2D) ]'] 4c_identity_block_bn2 (Bat (None, 14, 14, 256) 1024 ['4c_identity_block_conv2[0][0chNormalization) ]'] 4c_identity_block_relu2 (A (None, 14, 14, 256) 0 ['4c_identity_block_bn2[0][0]'ctivation) ] 4c_identity_block_conv3 (C (None, 14, 14, 1024) 263168 ['4c_identity_block_relu2[0][0onv2D) ]'] 4c_identity_block_bn3 (Bat (None, 14, 14, 1024) 4096 ['4c_identity_block_conv3[0][0chNormalization) ]'] 4c_identity_block_add (Add (None, 14, 14, 1024) 0 ['4c_identity_block_bn3[0][0]') , '4b_identity_block_relu4[0][0]'] 4c_identity_block_relu4 (A (None, 14, 14, 1024) 0 ['4c_identity_block_add[0][0]'ctivation) ] 4d_identity_block_conv1 (C (None, 14, 14, 256) 262400 ['4c_identity_block_relu4[0][0onv2D) ]'] 4d_identity_block_bn1 (Bat (None, 14, 14, 256) 1024 ['4d_identity_block_conv1[0][0chNormalization) ]'] 4d_identity_block_relu1 (A (None, 14, 14, 256) 0 ['4d_identity_block_bn1[0][0]'ctivation) ] 4d_identity_block_conv2 (C (None, 14, 14, 256) 590080 ['4d_identity_block_relu1[0][0onv2D) ]'] 4d_identity_block_bn2 (Bat (None, 14, 14, 256) 1024 ['4d_identity_block_conv2[0][0chNormalization) ]'] 4d_identity_block_relu2 (A (None, 14, 14, 256) 0 ['4d_identity_block_bn2[0][0]'ctivation) ] 4d_identity_block_conv3 (C (None, 14, 14, 1024) 263168 ['4d_identity_block_relu2[0][0onv2D) ]'] 4d_identity_block_bn3 (Bat (None, 14, 14, 1024) 4096 ['4d_identity_block_conv3[0][0chNormalization) ]'] 4d_identity_block_add (Add (None, 14, 14, 1024) 0 ['4d_identity_block_bn3[0][0]') , '4c_identity_block_relu4[0][0]'] 4d_identity_block_relu4 (A (None, 14, 14, 1024) 0 ['4d_identity_block_add[0][0]'ctivation) ] 4e_identity_block_conv1 (C (None, 14, 14, 256) 262400 ['4d_identity_block_relu4[0][0onv2D) ]'] 4e_identity_block_bn1 (Bat (None, 14, 14, 256) 1024 ['4e_identity_block_conv1[0][0chNormalization) ]'] 4e_identity_block_relu1 (A (None, 14, 14, 256) 0 ['4e_identity_block_bn1[0][0]'ctivation) ] 4e_identity_block_conv2 (C (None, 14, 14, 256) 590080 ['4e_identity_block_relu1[0][0onv2D) ]'] 4e_identity_block_bn2 (Bat (None, 14, 14, 256) 1024 ['4e_identity_block_conv2[0][0chNormalization) ]'] 4e_identity_block_relu2 (A (None, 14, 14, 256) 0 ['4e_identity_block_bn2[0][0]'ctivation) ] 4e_identity_block_conv3 (C (None, 14, 14, 1024) 263168 ['4e_identity_block_relu2[0][0onv2D) ]'] 4e_identity_block_bn3 (Bat (None, 14, 14, 1024) 4096 ['4e_identity_block_conv3[0][0chNormalization) ]'] 4e_identity_block_add (Add (None, 14, 14, 1024) 0 ['4e_identity_block_bn3[0][0]') , '4d_identity_block_relu4[0][0]'] 4e_identity_block_relu4 (A (None, 14, 14, 1024) 0 ['4e_identity_block_add[0][0]'ctivation) ] 4f_identity_block_conv1 (C (None, 14, 14, 256) 262400 ['4e_identity_block_relu4[0][0onv2D) ]'] 4f_identity_block_bn1 (Bat (None, 14, 14, 256) 1024 ['4f_identity_block_conv1[0][0chNormalization) ]'] 4f_identity_block_relu1 (A (None, 14, 14, 256) 0 ['4f_identity_block_bn1[0][0]'ctivation) ] 4f_identity_block_conv2 (C (None, 14, 14, 256) 590080 ['4f_identity_block_relu1[0][0onv2D) ]'] 4f_identity_block_bn2 (Bat (None, 14, 14, 256) 1024 ['4f_identity_block_conv2[0][0chNormalization) ]'] 4f_identity_block_relu2 (A (None, 14, 14, 256) 0 ['4f_identity_block_bn2[0][0]'ctivation) ] 4f_identity_block_conv3 (C (None, 14, 14, 1024) 263168 ['4f_identity_block_relu2[0][0onv2D) ]'] 4f_identity_block_bn3 (Bat (None, 14, 14, 1024) 4096 ['4f_identity_block_conv3[0][0chNormalization) ]'] 4f_identity_block_add (Add (None, 14, 14, 1024) 0 ['4f_identity_block_bn3[0][0]') , '4e_identity_block_relu4[0][0]'] 4f_identity_block_relu4 (A (None, 14, 14, 1024) 0 ['4f_identity_block_add[0][0]'ctivation) ] 5a_conv_block_conv1 (Conv2 (None, 7, 7, 512) 524800 ['4f_identity_block_relu4[0][0D) ]'] 5a_conv_block_bn1 (BatchNo (None, 7, 7, 512) 2048 ['5a_conv_block_conv1[0][0]'] rmalization) 5a_conv_block_relu1 (Activ (None, 7, 7, 512) 0 ['5a_conv_block_bn1[0][0]'] ation) 5a_conv_block_conv2 (Conv2 (None, 7, 7, 512) 2359808 ['5a_conv_block_relu1[0][0]'] D) 5a_conv_block_bn2 (BatchNo (None, 7, 7, 512) 2048 ['5a_conv_block_conv2[0][0]'] rmalization) 5a_conv_block_relu2 (Activ (None, 7, 7, 512) 0 ['5a_conv_block_bn2[0][0]'] ation) 5a_conv_block_conv3 (Conv2 (None, 7, 7, 2048) 1050624 ['5a_conv_block_relu2[0][0]'] D) 5a_conv_block_res_conv (Co (None, 7, 7, 2048) 2099200 ['4f_identity_block_relu4[0][0nv2D) ]'] 5a_conv_block_bn3 (BatchNo (None, 7, 7, 2048) 8192 ['5a_conv_block_conv3[0][0]'] rmalization) 5a_conv_block_res_bn (Batc (None, 7, 7, 2048) 8192 ['5a_conv_block_res_conv[0][0]hNormalization) '] 5a_conv_block_add (Add) (None, 7, 7, 2048) 0 ['5a_conv_block_bn3[0][0]', '5a_conv_block_res_bn[0][0]']5a_conv_block_relu4 (Activ (None, 7, 7, 2048) 0 ['5a_conv_block_add[0][0]'] ation) 5b_identity_block_conv1 (C (None, 7, 7, 512) 1049088 ['5a_conv_block_relu4[0][0]'] onv2D) 5b_identity_block_bn1 (Bat (None, 7, 7, 512) 2048 ['5b_identity_block_conv1[0][0chNormalization) ]'] 5b_identity_block_relu1 (A (None, 7, 7, 512) 0 ['5b_identity_block_bn1[0][0]'ctivation) ] 5b_identity_block_conv2 (C (None, 7, 7, 512) 2359808 ['5b_identity_block_relu1[0][0onv2D) ]'] 5b_identity_block_bn2 (Bat (None, 7, 7, 512) 2048 ['5b_identity_block_conv2[0][0chNormalization) ]'] 5b_identity_block_relu2 (A (None, 7, 7, 512) 0 ['5b_identity_block_bn2[0][0]'ctivation) ] 5b_identity_block_conv3 (C (None, 7, 7, 2048) 1050624 ['5b_identity_block_relu2[0][0onv2D) ]'] 5b_identity_block_bn3 (Bat (None, 7, 7, 2048) 8192 ['5b_identity_block_conv3[0][0chNormalization) ]'] 5b_identity_block_add (Add (None, 7, 7, 2048) 0 ['5b_identity_block_bn3[0][0]') , '5a_conv_block_relu4[0][0]']5b_identity_block_relu4 (A (None, 7, 7, 2048) 0 ['5b_identity_block_add[0][0]'ctivation) ] 5c_identity_block_conv1 (C (None, 7, 7, 512) 1049088 ['5b_identity_block_relu4[0][0onv2D) ]'] 5c_identity_block_bn1 (Bat (None, 7, 7, 512) 2048 ['5c_identity_block_conv1[0][0chNormalization) ]'] 5c_identity_block_relu1 (A (None, 7, 7, 512) 0 ['5c_identity_block_bn1[0][0]'ctivation) ] 5c_identity_block_conv2 (C (None, 7, 7, 512) 2359808 ['5c_identity_block_relu1[0][0onv2D) ]'] 5c_identity_block_bn2 (Bat (None, 7, 7, 512) 2048 ['5c_identity_block_conv2[0][0chNormalization) ]'] 5c_identity_block_relu2 (A (None, 7, 7, 512) 0 ['5c_identity_block_bn2[0][0]'ctivation) ] 5c_identity_block_conv3 (C (None, 7, 7, 2048) 1050624 ['5c_identity_block_relu2[0][0onv2D) ]'] 5c_identity_block_bn3 (Bat (None, 7, 7, 2048) 8192 ['5c_identity_block_conv3[0][0chNormalization) ]'] 5c_identity_block_add (Add (None, 7, 7, 2048) 0 ['5c_identity_block_bn3[0][0]') , '5b_identity_block_relu4[0][0]'] 5c_identity_block_relu4 (A (None, 7, 7, 2048) 0 ['5c_identity_block_add[0][0]'ctivation) ] avg_pool (AveragePooling2D (None, 1, 1, 2048) 0 ['5c_identity_block_relu4[0][0) ]'] flatten (Flatten) (None, 2048) 0 ['avg_pool[0][0]'] fc1000 (Dense) (None, 1000) 2049000 ['flatten[0][0]'] ==================================================================================================
Total params: 25636712 (97.80 MB)
Trainable params: 25583592 (97.59 MB)
Non-trainable params: 53120 (207.50 KB)
__________________________________________________________________________________________________
2. 编译模型
# Compile the model
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])3. 训练模型
# Train the model
epochs = 10history = model.fit(train_ds,validation_data=val_ds,epochs=epochs
)Epoch 1/10
57/57 [==============================] - 476s 7s/step - loss: 2.3338 - accuracy: 0.4779 - val_loss: 95.4034 - val_accuracy: 0.2301
Epoch 2/10
57/57 [==============================] - 259s 5s/step - loss: 1.0860 - accuracy: 0.6438 - val_loss: 4.2480 - val_accuracy: 0.2920
Epoch 3/10
57/57 [==============================] - 247s 4s/step - loss: 0.7115 - accuracy: 0.7212 - val_loss: 0.9247 - val_accuracy: 0.6637
Epoch 4/10
57/57 [==============================] - 254s 4s/step - loss: 0.7610 - accuracy: 0.7456 - val_loss: 0.7742 - val_accuracy: 0.6903
Epoch 5/10
57/57 [==============================] - 276s 5s/step - loss: 0.5945 - accuracy: 0.7544 - val_loss: 0.6813 - val_accuracy: 0.7434
Epoch 6/10
57/57 [==============================] - 282s 5s/step - loss: 0.6199 - accuracy: 0.8053 - val_loss: 0.9435 - val_accuracy: 0.8053
Epoch 7/10
57/57 [==============================] - 252s 4s/step - loss: 0.5086 - accuracy: 0.8363 - val_loss: 1.8492 - val_accuracy: 0.5752
Epoch 8/10
57/57 [==============================] - 253s 4s/step - loss: 0.3915 - accuracy: 0.8827 - val_loss: 1.2361 - val_accuracy: 0.6018
Epoch 9/10
57/57 [==============================] - 247s 4s/step - loss: 0.5021 - accuracy: 0.8296 - val_loss: 2.4609 - val_accuracy: 0.5929
Epoch 10/10
57/57 [==============================] - 248s 4s/step - loss: 0.5328 - accuracy: 0.8186 - val_loss: 2.3554 - val_accuracy: 0.4779四、模型评估
1. Loss与Accuracy图
# Evaluate the model
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']loss = history.history['loss']
val_loss = history.history['val_loss']epochs_range = range(epochs)plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')plt.subplot(1, 2, 2)plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
2. 预测
# Predict on new images
plt.figure(figsize=(10, 5))for images, labels in val_ds.take(1):for i in range(8):ax = plt.subplot(2, 4, i + 1)image = images[i].numpy().astype("uint8")plt.imshow(image)img_array = tf.expand_dims(images[i], 0)predictions = model.predict(img_array)plt.title(class_names[np.argmax(predictions)])plt.axis("off")
1/1 [==============================] - 2s 2s/step
1/1 [==============================] - 0s 145ms/step
1/1 [==============================] - 0s 157ms/step
1/1 [==============================] - 0s 166ms/step
1/1 [==============================] - 0s 137ms/step
1/1 [==============================] - 0s 126ms/step
1/1 [==============================] - 0s 144ms/step
1/1 [==============================] - 0s 162ms/step
五、Pytorch版本代码
import os
import pathlib
import numpy as np
import matplotlib.pyplot as plt
from PIL import Imageimport torch
from torch import nn
from torchvision import datasets, transforms, models
from torch.utils.data import DataLoader
from torchvision.utils import make_grid# Set font for Chinese labels (SimHei)
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False# Paths
data_dir = 'C:/Self_Learning/Deep_Learning/K_Codes/data/8_data/bird_photos/'
data_dir = pathlib.Path(data_dir)# Transforms
img_height = 224
img_width = 224
batch_size = 8transform = transforms.Compose([transforms.Resize((img_height, img_width)),transforms.ToTensor()
])# Load datasets
train_ds = datasets.ImageFolder(data_dir, transform=transform)
class_names = train_ds.classes
num_classes = len(class_names)
print("Classes:", class_names)# Split into train and val
train_size = int(0.8 * len(train_ds))
val_size = len(train_ds) - train_size
train_dataset, val_dataset = torch.utils.data.random_split(train_ds, [train_size, val_size])train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)# Visualise images
def show_batch(images, labels):img_grid = make_grid(images, nrow=4)npimg = img_grid.numpy()plt.figure(figsize=(10, 5))plt.imshow(np.transpose(npimg, (1, 2, 0)))plt.title(" / ".join([class_names[label] for label in labels]))plt.axis("off")plt.show()images, labels = next(iter(train_loader))
show_batch(images, labels)# Define ResNet50 model
model = models.resnet50(pretrained=False)
model.fc = nn.Linear(model.fc.in_features, num_classes)# Load pretrained weights if needed (optional)
# model.load_state_dict(torch.load('your_resnet50_weights.pth'))device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)# Loss & Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)# Training loop
epochs = 10
train_acc_history = []
val_acc_history = []
train_loss_history = []
val_loss_history = []for epoch in range(epochs):model.train()train_loss, train_correct = 0.0, 0for inputs, labels in train_loader:inputs, labels = inputs.to(device), labels.to(device)optimizer.zero_grad()outputs = model(inputs)loss = criterion(outputs, labels)loss.backward()optimizer.step()train_loss += loss.item() * inputs.size(0)train_correct += (outputs.argmax(1) == labels).sum().item()train_loss /= len(train_loader.dataset)train_acc = train_correct / len(train_loader.dataset)# Validationmodel.eval()val_loss, val_correct = 0.0, 0with torch.no_grad():for inputs, labels in val_loader:inputs, labels = inputs.to(device), labels.to(device)outputs = model(inputs)loss = criterion(outputs, labels)val_loss += loss.item() * inputs.size(0)val_correct += (outputs.argmax(1) == labels).sum().item()val_loss /= len(val_loader.dataset)val_acc = val_correct / len(val_loader.dataset)train_loss_history.append(train_loss)val_loss_history.append(val_loss)train_acc_history.append(train_acc)val_acc_history.append(val_acc)print(f"Epoch {epoch+1}/{epochs}: "f"Train Loss {train_loss:.4f}, Acc {train_acc:.4f} | "f"Val Loss {val_loss:.4f}, Acc {val_acc:.4f}")# Plot training results
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(train_acc_history, label='Train Acc')
plt.plot(val_acc_history, label='Val Acc')
plt.legend()
plt.title('Accuracy')plt.subplot(1, 2, 2)
plt.plot(train_loss_history, label='Train Loss')
plt.plot(val_loss_history, label='Val Loss')
plt.legend()
plt.title('Loss')
plt.show()# Predict on validation batch
model.eval()
images, labels = next(iter(val_loader))
images = images.to(device)
outputs = model(images)
preds = outputs.argmax(1)# Show predictions
plt.figure(figsize=(10, 5))
for i in range(8):ax = plt.subplot(2, 4, i + 1)plt.imshow(images[i].cpu().permute(1, 2, 0).numpy())plt.title(class_names[preds[i]])plt.axis("off")
plt.show()