用到的数据增强再上一章已经详细的介绍了。

一、导包

import warnings
warnings.filterwarnings('ignore')
import tensorflow as tf
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator

二、网络架构

可以重新给一下path，代码如下：

train_dir='D:/暑假/data/cats_and_dogs_small/train'
validation_dir='D:/暑假/data/cats_and_dogs_small/validation'

model1=tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32,(3,3),activation='relu',input_shape=(64,64,3)),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(64,(3,3),activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(128,(3,3),activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(128,(3,3),activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(512,activation='relu'),
    tf.keras.layers.Dense(1,activation='sigmoid')
])

三、配置训练器

model1.compile(loss='binary_crossentropy',
              optimizer=Adam(lr=1e-4),
              metrics=['acc'])

四、数据增强 （我们只需要给训练数据进行增强）

train_datagen=ImageDataGenerator(
    rescale=1./255,
    rotation_range=40,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest'
)
test_datagen=ImageDataGenerator(rescale=1./225)
train_generator=train_datagen.flow_from_directory(
    train_dir,
    target_size=(64,64),
    batch_size=80,
    class_mode='binary')
validation_generator=test_datagen.flow_from_directory(
    validation_dir,
    target_size=(64,64),
    batch_size=50,
    class_mode='binary')
history2=model1.fit_generator(
    train_generator,
    steps_per_epoch=100,
    epochs=100,
    validation_data=validation_generator,
    validation_steps=50)

结果如下：（我觉得还挺行）

 五、绘制训练过程中损失曲线和精度曲线。

import matplotlib.pyplot as plt
acc=history2.history['acc']
val_acc=history2.history['val_acc']
loss=history2.history['loss']
val_loss=history2.history['val_loss']
epochs=range(len(acc))
plt.plot(epochs,acc,'bo',label='Training accuracy')
plt.plot(epochs,val_acc,'r',label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.figure()
plt.plot(epochs,loss,'bo',label='Training Loss')
plt.plot(epochs,val_loss,'r',label='Validation Loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()

结果如下：

 效果还是比较明显滴！