Since ResNet was proposed in 2015, it won the first place in the classification task of the ImageNet competition, because it is "simple and practical", and many methods have been completed on the basis of ResNet50 or ResNet101, detection, segmentation, recognition and other fields They all use ResNet one after another, and Alpha zero also uses ResNet, so it can be seen that ResNet is really easy to use.
Schematic diagram of Resnets50 network structure
Comparison of ResNet50 and ResNet101
Image classification problem using ResNets50
# -*- coding: UTF-8 -*-
import os, sys
import numpy as np
import scipy
from scipy import ndimage
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing import image
import keras
import pickle
import PIL.Image
import random
# 准备用来训练网络和测试的数据集
def DataSet():
# 训练数据集目录
train_path_classier1 = './src/train/a/'
train_path_classier2 = './src/train/b/'
# 测试数据集目录
test_path_classier1 = './src/test/a/'
test_path_classier2 = './src/test/b/'
# 获取文件列表
imglist_train_classier1 = os.listdir(train_path_classier1)
imglist_train_classier2 = os.listdir(train_path_classier2)
imglist_test_face = os.listdir(test_path_classier1)
imglist_test_dog = os.listdir(test_path_classier2)
# 准备训练数据集，虽然ResNet50的缺省输入为224x224，但也接受256x256的输入，最小192x192
X_train = np.empty((len(imglist_train_classier1) + len(imglist_train_classier2), 256, 256, 3))
Y_train = np.empty((len(imglist_train_classier1) + len(imglist_train_classier2), 2))
count = 0
for img_name in imglist_train_classier1:
img_path = train_path_classier1 + img_name
img = image.load_img(img_path, target_size=(256, 256))
img = image.img_to_array(img) / 255.0
# 训练的输入为图像，输出为分类，（1，0）是第一类，（0，1）是第二类
X_train[count] = img
Y_train[count] = np.array((1, 0))
count += 1
for img_name in imglist_train_classier2:
img_path = train_path_classier2 + img_name
img = image.load_img(img_path, target_size=(256, 256))
img = image.img_to_array(img) / 255.0
X_train[count] = img
Y_train[count] = np.array((0, 1))
count += 1
# 准备测试数据集，虽然ResNet50的缺省输入为224x224，但也接受256x256的输入，最小192x192
X_test = np.empty((len(imglist_test_face) + len(imglist_test_dog), 256, 256, 3))
Y_test = np.empty((len(imglist_test_face) + len(imglist_test_dog), 2))
count = 0
for img_name in imglist_test_face:
img_path = test_path_classier1 + img_name
img = image.load_img(img_path, target_size=(256, 256))
img = image.img_to_array(img) / 255.0
X_test[count] = img
Y_test[count] = np.array((1, 0))
count += 1
for img_name in imglist_test_dog:
img_path = test_path_classier2 + img_name
img = image.load_img(img_path, target_size=(256, 256))
img = image.img_to_array(img) / 255.0
X_test[count] = img
Y_test[count] = np.array((0, 1))
count += 1
# 打乱顺序
index = [i for i in range(len(X_train))]
random.shuffle(index)
X_train = X_train[index]
Y_train = Y_train[index]
index = [i for i in range(len(X_test))]
random.shuffle(index)
X_test = X_test[index]
Y_test = Y_test[index]
return X_train, Y_train, X_test, Y_test
# 主程序
def main():
# 输入图片为256x256，2个分类
shape, classes = (256, 256, 3), 2
# 调用keras的ResNet50模型
model = keras.applications.resnet50.ResNet50(input_shape = shape, weights=None, classes=classes)
model.summary()
model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
# 给出训练和测试数据
X_train, Y_train, X_test, Y_test = DataSet()
print('X_train shape : ', X_train.shape)
print('Y_train shape : ', Y_train.shape)
print('X_test shape : ', X_test.shape)
print('Y_test shape : ', Y_test.shape)
# 训练模型
training = model.fit(X_train, Y_train, epochs=30, batch_size=1)
return
# 画图看一下训练的效果
plt.plot(training.history['acc'])
plt.plot(training.history['loss'])
plt.title('model accuracy and loss')
plt.xlabel('epoch')
plt.ylabel('accuracy')
plt.legend(['acc', 'loss'], loc='upper left')
plt.show()
# 评估模型
model.evaluate(X_test, Y_test, batch_size=32)
# 把训练好的模型保存到文件
model.save('resnet_model_2_classier.h5')
if __name__ == "__main__":
main()
print network structure
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 256, 256, 3) 0
__________________________________________________________________________________________________
conv1 (Conv2D) (None, 128, 128, 64) 9472 input_1[0][0]
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization) (None, 128, 128, 64) 256 conv1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 128, 128, 64) 0 bn_conv1[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 63, 63, 64) 0 activation_1[0][0]
__________________________________________________________________________________________________
res2a_branch2a (Conv2D) (None, 63, 63, 64) 4160 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
bn2a_branch2a (BatchNormalizati (None, 63, 63, 64) 256 res2a_branch2a[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, 63, 63, 64) 0 bn2a_branch2a[0][0]
__________________________________________________________________________________________________
res2a_branch2b (Conv2D) (None, 63, 63, 64) 36928 activation_2[0][0]
__________________________________________________________________________________________________
bn2a_branch2b (BatchNormalizati (None, 63, 63, 64) 256 res2a_branch2b[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, 63, 63, 64) 0 bn2a_branch2b[0][0]
__________________________________________________________________________________________________
res2a_branch2c (Conv2D) (None, 63, 63, 256) 16640 activation_3[0][0]
__________________________________________________________________________________________________
res2a_branch1 (Conv2D) (None, 63, 63, 256) 16640 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
bn2a_branch2c (BatchNormalizati (None, 63, 63, 256) 1024 res2a_branch2c[0][0]
__________________________________________________________________________________________________
bn2a_branch1 (BatchNormalizatio (None, 63, 63, 256) 1024 res2a_branch1[0][0]
__________________________________________________________________________________________________
add_1 (Add) (None, 63, 63, 256) 0 bn2a_branch2c[0][0]
bn2a_branch1[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, 63, 63, 256) 0 add_1[0][0]
__________________________________________________________________________________________________
res2b_branch2a (Conv2D) (None, 63, 63, 64) 16448 activation_4[0][0]
__________________________________________________________________________________________________
bn2b_branch2a (BatchNormalizati (None, 63, 63, 64) 256 res2b_branch2a[0][0]
__________________________________________________________________________________________________
activation_5 (Activation) (None, 63, 63, 64) 0 bn2b_branch2a[0][0]
__________________________________________________________________________________________________
res2b_branch2b (Conv2D) (None, 63, 63, 64) 36928 activation_5[0][0]
__________________________________________________________________________________________________
bn2b_branch2b (BatchNormalizati (None, 63, 63, 64) 256 res2b_branch2b[0][0]
__________________________________________________________________________________________________
activation_6 (Activation) (None, 63, 63, 64) 0 bn2b_branch2b[0][0]
__________________________________________________________________________________________________
res2b_branch2c (Conv2D) (None, 63, 63, 256) 16640 activation_6[0][0]
__________________________________________________________________________________________________
bn2b_branch2c (BatchNormalizati (None, 63, 63, 256) 1024 res2b_branch2c[0][0]
__________________________________________________________________________________________________
add_2 (Add) (None, 63, 63, 256) 0 bn2b_branch2c[0][0]
activation_4[0][0]
__________________________________________________________________________________________________
activation_7 (Activation) (None, 63, 63, 256) 0 add_2[0][0]
__________________________________________________________________________________________________
res2c_branch2a (Conv2D) (None, 63, 63, 64) 16448 activation_7[0][0]
__________________________________________________________________________________________________
bn2c_branch2a (BatchNormalizati (None, 63, 63, 64) 256 res2c_branch2a[0][0]
__________________________________________________________________________________________________
activation_8 (Activation) (None, 63, 63, 64) 0 bn2c_branch2a[0][0]
__________________________________________________________________________________________________
res2c_branch2b (Conv2D) (None, 63, 63, 64) 36928 activation_8[0][0]
__________________________________________________________________________________________________
bn2c_branch2b (BatchNormalizati (None, 63, 63, 64) 256 res2c_branch2b[0][0]
__________________________________________________________________________________________________
activation_9 (Activation) (None, 63, 63, 64) 0 bn2c_branch2b[0][0]
__________________________________________________________________________________________________
res2c_branch2c (Conv2D) (None, 63, 63, 256) 16640 activation_9[0][0]
__________________________________________________________________________________________________
bn2c_branch2c (BatchNormalizati (None, 63, 63, 256) 1024 res2c_branch2c[0][0]
__________________________________________________________________________________________________
add_3 (Add) (None, 63, 63, 256) 0 bn2c_branch2c[0][0]
activation_7[0][0]
__________________________________________________________________________________________________
activation_10 (Activation) (None, 63, 63, 256) 0 add_3[0][0]
__________________________________________________________________________________________________
res3a_branch2a (Conv2D) (None, 32, 32, 128) 32896 activation_10[0][0]
__________________________________________________________________________________________________
bn3a_branch2a (BatchNormalizati (None, 32, 32, 128) 512 res3a_branch2a[0][0]
__________________________________________________________________________________________________
activation_11 (Activation) (None, 32, 32, 128) 0 bn3a_branch2a[0][0]
__________________________________________________________________________________________________
res3a_branch2b (Conv2D) (None, 32, 32, 128) 147584 activation_11[0][0]
__________________________________________________________________________________________________
bn3a_branch2b (BatchNormalizati (None, 32, 32, 128) 512 res3a_branch2b[0][0]
__________________________________________________________________________________________________
activation_12 (Activation) (None, 32, 32, 128) 0 bn3a_branch2b[0][0]
__________________________________________________________________________________________________
res3a_branch2c (Conv2D) (None, 32, 32, 512) 66048 activation_12[0][0]
__________________________________________________________________________________________________
res3a_branch1 (Conv2D) (None, 32, 32, 512) 131584 activation_10[0][0]
__________________________________________________________________________________________________
bn3a_branch2c (BatchNormalizati (None, 32, 32, 512) 2048 res3a_branch2c[0][0]
__________________________________________________________________________________________________
bn3a_branch1 (BatchNormalizatio (None, 32, 32, 512) 2048 res3a_branch1[0][0]
__________________________________________________________________________________________________
add_4 (Add) (None, 32, 32, 512) 0 bn3a_branch2c[0][0]
bn3a_branch1[0][0]
__________________________________________________________________________________________________
activation_13 (Activation) (None, 32, 32, 512) 0 add_4[0][0]
__________________________________________________________________________________________________
res3b_branch2a (Conv2D) (None, 32, 32, 128) 65664 activation_13[0][0]
__________________________________________________________________________________________________
bn3b_branch2a (BatchNormalizati (None, 32, 32, 128) 512 res3b_branch2a[0][0]
__________________________________________________________________________________________________
activation_14 (Activation) (None, 32, 32, 128) 0 bn3b_branch2a[0][0]
__________________________________________________________________________________________________
res3b_branch2b (Conv2D) (None, 32, 32, 128) 147584 activation_14[0][0]
__________________________________________________________________________________________________
bn3b_branch2b (BatchNormalizati (None, 32, 32, 128) 512 res3b_branch2b[0][0]
__________________________________________________________________________________________________
activation_15 (Activation) (None, 32, 32, 128) 0 bn3b_branch2b[0][0]
__________________________________________________________________________________________________
res3b_branch2c (Conv2D) (None, 32, 32, 512) 66048 activation_15[0][0]
__________________________________________________________________________________________________
bn3b_branch2c (BatchNormalizati (None, 32, 32, 512) 2048 res3b_branch2c[0][0]
__________________________________________________________________________________________________
add_5 (Add) (None, 32, 32, 512) 0 bn3b_branch2c[0][0]
activation_13[0][0]
__________________________________________________________________________________________________
activation_16 (Activation) (None, 32, 32, 512) 0 add_5[0][0]
__________________________________________________________________________________________________
res3c_branch2a (Conv2D) (None, 32, 32, 128) 65664 activation_16[0][0]
__________________________________________________________________________________________________
bn3c_branch2a (BatchNormalizati (None, 32, 32, 128) 512 res3c_branch2a[0][0]
__________________________________________________________________________________________________
activation_17 (Activation) (None, 32, 32, 128) 0 bn3c_branch2a[0][0]
__________________________________________________________________________________________________
res3c_branch2b (Conv2D) (None, 32, 32, 128) 147584 activation_17[0][0]
__________________________________________________________________________________________________
bn3c_branch2b (BatchNormalizati (None, 32, 32, 128) 512 res3c_branch2b[0][0]
__________________________________________________________________________________________________
activation_18 (Activation) (None, 32, 32, 128) 0 bn3c_branch2b[0][0]
__________________________________________________________________________________________________
res3c_branch2c (Conv2D) (None, 32, 32, 512) 66048 activation_18[0][0]
__________________________________________________________________________________________________
bn3c_branch2c (BatchNormalizati (None, 32, 32, 512) 2048 res3c_branch2c[0][0]
__________________________________________________________________________________________________
add_6 (Add) (None, 32, 32, 512) 0 bn3c_branch2c[0][0]
activation_16[0][0]
__________________________________________________________________________________________________
activation_19 (Activation) (None, 32, 32, 512) 0 add_6[0][0]
__________________________________________________________________________________________________
res3d_branch2a (Conv2D) (None, 32, 32, 128) 65664 activation_19[0][0]
__________________________________________________________________________________________________
bn3d_branch2a (BatchNormalizati (None, 32, 32, 128) 512 res3d_branch2a[0][0]
__________________________________________________________________________________________________
activation_20 (Activation) (None, 32, 32, 128) 0 bn3d_branch2a[0][0]
__________________________________________________________________________________________________
res3d_branch2b (Conv2D) (None, 32, 32, 128) 147584 activation_20[0][0]
__________________________________________________________________________________________________
bn3d_branch2b (BatchNormalizati (None, 32, 32, 128) 512 res3d_branch2b[0][0]
__________________________________________________________________________________________________
activation_21 (Activation) (None, 32, 32, 128) 0 bn3d_branch2b[0][0]
__________________________________________________________________________________________________
res3d_branch2c (Conv2D) (None, 32, 32, 512) 66048 activation_21[0][0]
__________________________________________________________________________________________________
bn3d_branch2c (BatchNormalizati (None, 32, 32, 512) 2048 res3d_branch2c[0][0]
__________________________________________________________________________________________________
add_7 (Add) (None, 32, 32, 512) 0 bn3d_branch2c[0][0]
activation_19[0][0]
__________________________________________________________________________________________________
activation_22 (Activation) (None, 32, 32, 512) 0 add_7[0][0]
__________________________________________________________________________________________________
res4a_branch2a (Conv2D) (None, 16, 16, 256) 131328 activation_22[0][0]
__________________________________________________________________________________________________
bn4a_branch2a (BatchNormalizati (None, 16, 16, 256) 1024 res4a_branch2a[0][0]
__________________________________________________________________________________________________
activation_23 (Activation) (None, 16, 16, 256) 0 bn4a_branch2a[0][0]
__________________________________________________________________________________________________
res4a_branch2b (Conv2D) (None, 16, 16, 256) 590080 activation_23[0][0]
__________________________________________________________________________________________________
bn4a_branch2b (BatchNormalizati (None, 16, 16, 256) 1024 res4a_branch2b[0][0]
__________________________________________________________________________________________________
activation_24 (Activation) (None, 16, 16, 256) 0 bn4a_branch2b[0][0]
__________________________________________________________________________________________________
res4a_branch2c (Conv2D) (None, 16, 16, 1024) 263168 activation_24[0][0]
__________________________________________________________________________________________________
res4a_branch1 (Conv2D) (None, 16, 16, 1024) 525312 activation_22[0][0]
__________________________________________________________________________________________________
bn4a_branch2c (BatchNormalizati (None, 16, 16, 1024) 4096 res4a_branch2c[0][0]
__________________________________________________________________________________________________
bn4a_branch1 (BatchNormalizatio (None, 16, 16, 1024) 4096 res4a_branch1[0][0]
__________________________________________________________________________________________________
add_8 (Add) (None, 16, 16, 1024) 0 bn4a_branch2c[0][0]
bn4a_branch1[0][0]
__________________________________________________________________________________________________
activation_25 (Activation) (None, 16, 16, 1024) 0 add_8[0][0]
__________________________________________________________________________________________________
res4b_branch2a (Conv2D) (None, 16, 16, 256) 262400 activation_25[0][0]
__________________________________________________________________________________________________
bn4b_branch2a (BatchNormalizati (None, 16, 16, 256) 1024 res4b_branch2a[0][0]
__________________________________________________________________________________________________
activation_26 (Activation) (None, 16, 16, 256) 0 bn4b_branch2a[0][0]
__________________________________________________________________________________________________
res4b_branch2b (Conv2D) (None, 16, 16, 256) 590080 activation_26[0][0]
__________________________________________________________________________________________________
bn4b_branch2b (BatchNormalizati (None, 16, 16, 256) 1024 res4b_branch2b[0][0]
__________________________________________________________________________________________________
activation_27 (Activation) (None, 16, 16, 256) 0 bn4b_branch2b[0][0]
__________________________________________________________________________________________________
res4b_branch2c (Conv2D) (None, 16, 16, 1024) 263168 activation_27[0][0]
__________________________________________________________________________________________________
bn4b_branch2c (BatchNormalizati (None, 16, 16, 1024) 4096 res4b_branch2c[0][0]
__________________________________________________________________________________________________
add_9 (Add) (None, 16, 16, 1024) 0 bn4b_branch2c[0][0]
activation_25[0][0]
__________________________________________________________________________________________________
activation_28 (Activation) (None, 16, 16, 1024) 0 add_9[0][0]
__________________________________________________________________________________________________
res4c_branch2a (Conv2D) (None, 16, 16, 256) 262400 activation_28[0][0]
__________________________________________________________________________________________________
bn4c_branch2a (BatchNormalizati (None, 16, 16, 256) 1024 res4c_branch2a[0][0]
__________________________________________________________________________________________________
activation_29 (Activation) (None, 16, 16, 256) 0 bn4c_branch2a[0][0]
__________________________________________________________________________________________________
res4c_branch2b (Conv2D) (None, 16, 16, 256) 590080 activation_29[0][0]
__________________________________________________________________________________________________
bn4c_branch2b (BatchNormalizati (None, 16, 16, 256) 1024 res4c_branch2b[0][0]
__________________________________________________________________________________________________
activation_30 (Activation) (None, 16, 16, 256) 0 bn4c_branch2b[0][0]
__________________________________________________________________________________________________
res4c_branch2c (Conv2D) (None, 16, 16, 1024) 263168 activation_30[0][0]
__________________________________________________________________________________________________
bn4c_branch2c (BatchNormalizati (None, 16, 16, 1024) 4096 res4c_branch2c[0][0]
__________________________________________________________________________________________________
add_10 (Add) (None, 16, 16, 1024) 0 bn4c_branch2c[0][0]
activation_28[0][0]
__________________________________________________________________________________________________
activation_31 (Activation) (None, 16, 16, 1024) 0 add_10[0][0]
__________________________________________________________________________________________________
res4d_branch2a (Conv2D) (None, 16, 16, 256) 262400 activation_31[0][0]
__________________________________________________________________________________________________
bn4d_branch2a (BatchNormalizati (None, 16, 16, 256) 1024 res4d_branch2a[0][0]
__________________________________________________________________________________________________
activation_32 (Activation) (None, 16, 16, 256) 0 bn4d_branch2a[0][0]
__________________________________________________________________________________________________
res4d_branch2b (Conv2D) (None, 16, 16, 256) 590080 activation_32[0][0]
__________________________________________________________________________________________________
bn4d_branch2b (BatchNormalizati (None, 16, 16, 256) 1024 res4d_branch2b[0][0]
__________________________________________________________________________________________________
activation_33 (Activation) (None, 16, 16, 256) 0 bn4d_branch2b[0][0]
__________________________________________________________________________________________________
res4d_branch2c (Conv2D) (None, 16, 16, 1024) 263168 activation_33[0][0]
__________________________________________________________________________________________________
bn4d_branch2c (BatchNormalizati (None, 16, 16, 1024) 4096 res4d_branch2c[0][0]
__________________________________________________________________________________________________
add_11 (Add) (None, 16, 16, 1024) 0 bn4d_branch2c[0][0]
activation_31[0][0]
__________________________________________________________________________________________________
activation_34 (Activation) (None, 16, 16, 1024) 0 add_11[0][0]
__________________________________________________________________________________________________
res4e_branch2a (Conv2D) (None, 16, 16, 256) 262400 activation_34[0][0]
__________________________________________________________________________________________________
bn4e_branch2a (BatchNormalizati (None, 16, 16, 256) 1024 res4e_branch2a[0][0]
__________________________________________________________________________________________________
activation_35 (Activation) (None, 16, 16, 256) 0 bn4e_branch2a[0][0]
__________________________________________________________________________________________________
res4e_branch2b (Conv2D) (None, 16, 16, 256) 590080 activation_35[0][0]
__________________________________________________________________________________________________
bn4e_branch2b (BatchNormalizati (None, 16, 16, 256) 1024 res4e_branch2b[0][0]
__________________________________________________________________________________________________
activation_36 (Activation) (None, 16, 16, 256) 0 bn4e_branch2b[0][0]
__________________________________________________________________________________________________
res4e_branch2c (Conv2D) (None, 16, 16, 1024) 263168 activation_36[0][0]
__________________________________________________________________________________________________
bn4e_branch2c (BatchNormalizati (None, 16, 16, 1024) 4096 res4e_branch2c[0][0]
__________________________________________________________________________________________________
add_12 (Add) (None, 16, 16, 1024) 0 bn4e_branch2c[0][0]
activation_34[0][0]
__________________________________________________________________________________________________
activation_37 (Activation) (None, 16, 16, 1024) 0 add_12[0][0]
__________________________________________________________________________________________________
res4f_branch2a (Conv2D) (None, 16, 16, 256) 262400 activation_37[0][0]
__________________________________________________________________________________________________
bn4f_branch2a (BatchNormalizati (None, 16, 16, 256) 1024 res4f_branch2a[0][0]
__________________________________________________________________________________________________
activation_38 (Activation) (None, 16, 16, 256) 0 bn4f_branch2a[0][0]
__________________________________________________________________________________________________
res4f_branch2b (Conv2D) (None, 16, 16, 256) 590080 activation_38[0][0]
__________________________________________________________________________________________________
bn4f_branch2b (BatchNormalizati (None, 16, 16, 256) 1024 res4f_branch2b[0][0]
__________________________________________________________________________________________________
activation_39 (Activation) (None, 16, 16, 256) 0 bn4f_branch2b[0][0]
__________________________________________________________________________________________________
res4f_branch2c (Conv2D) (None, 16, 16, 1024) 263168 activation_39[0][0]
__________________________________________________________________________________________________
bn4f_branch2c (BatchNormalizati (None, 16, 16, 1024) 4096 res4f_branch2c[0][0]
__________________________________________________________________________________________________
add_13 (Add) (None, 16, 16, 1024) 0 bn4f_branch2c[0][0]
activation_37[0][0]
__________________________________________________________________________________________________
activation_40 (Activation) (None, 16, 16, 1024) 0 add_13[0][0]
__________________________________________________________________________________________________
res5a_branch2a (Conv2D) (None, 8, 8, 512) 524800 activation_40[0][0]
__________________________________________________________________________________________________
bn5a_branch2a (BatchNormalizati (None, 8, 8, 512) 2048 res5a_branch2a[0][0]
__________________________________________________________________________________________________
activation_41 (Activation) (None, 8, 8, 512) 0 bn5a_branch2a[0][0]
__________________________________________________________________________________________________
res5a_branch2b (Conv2D) (None, 8, 8, 512) 2359808 activation_41[0][0]
__________________________________________________________________________________________________
bn5a_branch2b (BatchNormalizati (None, 8, 8, 512) 2048 res5a_branch2b[0][0]
__________________________________________________________________________________________________
activation_42 (Activation) (None, 8, 8, 512) 0 bn5a_branch2b[0][0]
__________________________________________________________________________________________________
res5a_branch2c (Conv2D) (None, 8, 8, 2048) 1050624 activation_42[0][0]
__________________________________________________________________________________________________
res5a_branch1 (Conv2D) (None, 8, 8, 2048) 2099200 activation_40[0][0]
__________________________________________________________________________________________________
bn5a_branch2c (BatchNormalizati (None, 8, 8, 2048) 8192 res5a_branch2c[0][0]
__________________________________________________________________________________________________
bn5a_branch1 (BatchNormalizatio (None, 8, 8, 2048) 8192 res5a_branch1[0][0]
__________________________________________________________________________________________________
add_14 (Add) (None, 8, 8, 2048) 0 bn5a_branch2c[0][0]
bn5a_branch1[0][0]
__________________________________________________________________________________________________
activation_43 (Activation) (None, 8, 8, 2048) 0 add_14[0][0]
__________________________________________________________________________________________________
res5b_branch2a (Conv2D) (None, 8, 8, 512) 1049088 activation_43[0][0]
__________________________________________________________________________________________________
bn5b_branch2a (BatchNormalizati (None, 8, 8, 512) 2048 res5b_branch2a[0][0]
__________________________________________________________________________________________________
activation_44 (Activation) (None, 8, 8, 512) 0 bn5b_branch2a[0][0]
__________________________________________________________________________________________________
res5b_branch2b (Conv2D) (None, 8, 8, 512) 2359808 activation_44[0][0]
__________________________________________________________________________________________________
bn5b_branch2b (BatchNormalizati (None, 8, 8, 512) 2048 res5b_branch2b[0][0]
__________________________________________________________________________________________________
activation_45 (Activation) (None, 8, 8, 512) 0 bn5b_branch2b[0][0]
__________________________________________________________________________________________________
res5b_branch2c (Conv2D) (None, 8, 8, 2048) 1050624 activation_45[0][0]
__________________________________________________________________________________________________
bn5b_branch2c (BatchNormalizati (None, 8, 8, 2048) 8192 res5b_branch2c[0][0]
__________________________________________________________________________________________________
add_15 (Add) (None, 8, 8, 2048) 0 bn5b_branch2c[0][0]
activation_43[0][0]
__________________________________________________________________________________________________
activation_46 (Activation) (None, 8, 8, 2048) 0 add_15[0][0]
__________________________________________________________________________________________________
res5c_branch2a (Conv2D) (None, 8, 8, 512) 1049088 activation_46[0][0]
__________________________________________________________________________________________________
bn5c_branch2a (BatchNormalizati (None, 8, 8, 512) 2048 res5c_branch2a[0][0]
__________________________________________________________________________________________________
activation_47 (Activation) (None, 8, 8, 512) 0 bn5c_branch2a[0][0]
__________________________________________________________________________________________________
res5c_branch2b (Conv2D) (None, 8, 8, 512) 2359808 activation_47[0][0]
__________________________________________________________________________________________________
bn5c_branch2b (BatchNormalizati (None, 8, 8, 512) 2048 res5c_branch2b[0][0]
__________________________________________________________________________________________________
activation_48 (Activation) (None, 8, 8, 512) 0 bn5c_branch2b[0][0]
__________________________________________________________________________________________________
res5c_branch2c (Conv2D) (None, 8, 8, 2048) 1050624 activation_48[0][0]
__________________________________________________________________________________________________
bn5c_branch2c (BatchNormalizati (None, 8, 8, 2048) 8192 res5c_branch2c[0][0]
__________________________________________________________________________________________________
add_16 (Add) (None, 8, 8, 2048) 0 bn5c_branch2c[0][0]
activation_46[0][0]
__________________________________________________________________________________________________
activation_49 (Activation) (None, 8, 8, 2048) 0 add_16[0][0]
__________________________________________________________________________________________________
avg_pool (AveragePooling2D) (None, 1, 1, 2048) 0 activation_49[0][0]
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 2048) 0 avg_pool[0][0]
__________________________________________________________________________________________________
fc1000 (Dense) (None, 2) 4098 flatten_1[0][0]
==================================================================================================
Predict classification results
# -*- coding: UTF-8 -*-
import os, sys
import numpy as np
import scipy
from scipy import ndimage
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing import image
import keras
import pickle
import PIL.Image
import random
Dict_classier = {
0:"classier1",
1:"classier2"
}
model = keras.models.load_model('resnet_model_2_classier.h5')
# 准备用于分类的图片
# img_path = "你的测试图片路径"
img_path = "./src/train/a/1_anime.png"
img = image.load_img(img_path, target_size=(256, 256))
plt.imshow(img)
img = image.img_to_array(img) / 255.0
img = np.expand_dims(img, axis=0) # 为batch添加第四维
# 测试分类的结果
print(model.predict(img))
print('classier2 is 1, classier1 is 0.')
print('The predicted type of img is: ', Dict_classier[np.argmax(model.predict(img))])