一、准备训练数据

下载数据集

validation验证集
train训练集

数据集结构如下：在这里插入图片描述
将数据集解压到自己选择的目录下就行
最后的结构效果如下：

二、构建模型

ImageDataGenerator

真实数据中，往往图片尺寸大小不一，需要裁剪成一样大小，一般为正方形
数据量比较大，不能一下子全部装入内存中
经常需要进行修改参数，比如输出的尺寸，增补图像拉伸等

from tensorflow import keras
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator

#创建两个数据生成器，指定scaling范围为0-1
train_datagen = ImageDataGenerator(rescale=1/255)
validation_datagen = ImageDataGenerator(rescale=1/255)#将train_datagen数据生成器指向数据集所在文件夹
train_generator = train_datagen.flow_from_directory(r'.\images\training',#训练集所在文件夹target_size=(300,300),#指定输出尺寸batch_size=32,class_mode='binary'#指定二分类
)
#Found 1027 images belonging to 2 classes.#将validation_datagen数据生成器指向数据集所在文件夹
validation_generator = validation_datagen.flow_from_directory(r'.\images\validation',#验证集所在文件夹target_size=(300,300),#指定输出尺寸batch_size=32,class_mode='binary'#指定二分类
)
#Found 256 images belonging to 2 classes.

三、训练模型

model = tf.keras.models.Sequential()model.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu',input_shape=(300,300,3)))
model.add(tf.keras.layers.MaxPooling2D(2,2))
model.add(tf.keras.layers.Conv2D(32,(3,3),activation='relu',input_shape=(28,28,1)))
model.add(tf.keras.layers.MaxPooling2D(2,2))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128,activation='relu'))
model.add(tf.keras.layers.Dense(1,activation='sigmoid'))model.compile(optimizer=RMSprop(lr=0.001),loss="binary_crossentropy",metrics=['accuracy'])model.fit(train_generator,epochs=20,validation_data = validation_generator)"""
Epoch 1/20
8/8 [==============================] - 8s 996ms/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 136s 4s/step - loss: 7.4488 - acc: 0.5151 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 2/20
8/8 [==============================] - 8s 968ms/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 136s 4s/step - loss: 7.8380 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 3/20
8/8 [==============================] - 8s 1s/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 141s 4s/step - loss: 7.6964 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 4/20
8/8 [==============================] - 8s 1s/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 143s 4s/step - loss: 7.8380 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 5/20
8/8 [==============================] - 8s 1s/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 145s 4s/step - loss: 7.5547 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 6/20
8/8 [==============================] - 8s 1s/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 144s 4s/step - loss: 7.8380 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 7/20
8/8 [==============================] - 8s 1s/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 146s 4s/step - loss: 7.5547 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 8/20
8/8 [==============================] - 8s 1s/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 144s 4s/step - loss: 7.8380 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 9/20
8/8 [==============================] - 8s 1s/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 144s 4s/step - loss: 7.6964 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 10/20
8/8 [==============================] - 8s 1s/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 144s 4s/step - loss: 7.8380 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 11/20
8/8 [==============================] - 8s 1s/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 143s 4s/step - loss: 7.6964 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 12/20
8/8 [==============================] - 8s 982ms/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 138s 4s/step - loss: 7.6964 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 13/20
8/8 [==============================] - 8s 968ms/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 135s 4s/step - loss: 7.6964 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 14/20
8/8 [==============================] - 8s 974ms/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 135s 4s/step - loss: 7.8380 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 15/20
8/8 [==============================] - 8s 971ms/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 135s 4s/step - loss: 7.6964 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 16/20
8/8 [==============================] - 8s 972ms/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 135s 4s/step - loss: 7.8380 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 17/20
8/8 [==============================] - 8s 983ms/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 135s 4s/step - loss: 7.8380 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 18/20
8/8 [==============================] - 8s 976ms/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 136s 4s/step - loss: 7.8380 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 19/20
8/8 [==============================] - 8s 969ms/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 134s 4s/step - loss: 7.8380 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
Epoch 20/20
8/8 [==============================] - 9s 1s/step - loss: 7.9712 - acc: 0.5000
33/33 [==============================] - 137s 4s/step - loss: 7.5547 - acc: 0.5131 - val_loss: 7.9712 - val_acc: 0.5000
"""

四、优化参数

import os
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import RMSprop
from kerastuner.tuners import Hyperband
from kerastuner.engine.hyperparameters import HyperParameters#创建两个数据生成器，指定scaling范围为0-1
train_datagen = ImageDataGenerator(rescale=1/255)
validation_datagen = ImageDataGenerator(rescale=1/255)#将train_datagen数据生成器指向数据集所在文件夹
train_generator = train_datagen.flow_from_directory(r'.\images\training',#训练集所在文件夹target_size=(150,150),#指定输出尺寸batch_size=32,class_mode='binary'#指定二分类
)#将validation_datagen数据生成器指向数据集所在文件夹
validation_generator = validation_datagen.flow_from_directory(r'.\images\validation',#验证集所在文件夹target_size=(150,150),#指定输出尺寸batch_size=32,class_mode='binary'#指定二分类
)hp = HyperParameters()def build_model(hp):model = tf.keras.models.Sequential()model.add(tf.keras.layers.Conv2D(hp.Choice('num_filters_layer0',values=[16,64],default=16),(3,3),activation='relu',input_shape=(150,150,3))),model.add(tf.keras.layers.MaxPooling2D(2,2)),for i in range(hp.Int("num_conv_layers",1,3)):model.add(tf.keras.layers.Conv2D(hp.Choice(f'num_filters_layer{i}',values=[16,64],default=16),(3,3),activation='relu')),model.add(tf.keras.layers.MaxPooling2D(2,2)),model.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu')),model.add(tf.keras.layers.MaxPooling2D(2,2)),model.add(tf.keras.layers.Flatten()),model.add(tf.keras.layers.Dense(hp.Int("hidden_units",128,512,step=32),activation='relu')),model.add(tf.keras.layers.Dense(1,activation='sigmoid'))#是否，一个神经元就行model.compile(optimizer=RMSprop(lr=0.001),loss="binary_crossentropy",metrics=['accuracy'])return modeltuner = Hyperband(#将训练好的参数存放起来build_model,objective='val_acc',max_epochs=15,directory='horse_human_params',hyperparameters=hp,project_name='my_horse_human_project'
)tuner.search(train_generator,epochs=10,validation_data=validation_generator)
"""
Trial 13 Complete [00h 02m 13s]
val_acc: 0.87109375Best val_acc So Far: 0.890625
Total elapsed time: 00h 14m 22sSearch: Running Trial #14Value             |Best Value So Far |Hyperparameter
16                |64                |num_filters_layer0
5                 |2                 |tuner/epochs
2                 |0                 |tuner/initial_epoch
2                 |2                 |tuner/bracket
1                 |0                 |tuner/round
0000              |None              |tuner/trial_id
"""

在这里插入图片描述

best_hps = tuner.get_best_hyperparameters(1)[0]#根据最优，把模型构建出来
print(best_hps.values)
"""
{'num_filters_layer0': 64, 'num_conv_layers': 2, 'hidden_units': 256, 'num_filters_layer1': 16, 'num_filters_layer2': 64, 'tuner/epochs': 2, 'tuner/initial_epoch': 0, 'tuner/bracket': 2, 'tuner/round': 0}
"""

model = tuner.hypermodel.build(best_hps)
model.summary()
"""
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 148, 148, 64)      1792      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 74, 74, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 72, 72, 64)        36928     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 36, 36, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 34, 34, 16)        9232      
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 17, 17, 16)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 15, 15, 64)        9280      
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 7, 7, 64)          0         
_________________________________________________________________
flatten (Flatten)            (None, 3136)              0         
_________________________________________________________________
dense (Dense)                (None, 256)               803072    
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 257       
=================================================================
Total params: 860,561
Trainable params: 860,561
Non-trainable params: 0
_________________________________________________________________
"""