五、项目实战---识别人和马

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：
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 model


tuner = 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: 0.87109375 Best val_acc So Far: 0.890625 Total elapsed time: 00h 14m 22s Search: Running Trial #14 Value |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 """

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 _________________________________________________________________ """