源码如下:
import os,shutil
original_dataset_dir = r'C:\Users\15790\Deep_Learning _with_Python\conv\kaggle_original_data'
base_dir = r'C:\Users\15790\Deep_Learning _with_Python\conv\cats_and_dogs_small'
os.mkdir(base_dir)
train_dir = os.path.join(base_dir, 'train')
os.mkdir(train_dir)
validation_dir = os.path.join(base_dir, 'validation')
os.mkdir(validation_dir)
test_dir = os.path.join(base_dir,'test')
os.mkdir(test_dir)
train_cats_dir = os.path.join(train_dir,'cats') #猫的训练图像目录
os.mkdir(train_cats_dir)
train_dogs_dir = os.path.join(train_dir,'dogs') #狗的训练图像目录
os.mkdir(train_dogs_dir)
validation_cats_dir = os.path.join(validation_dir,'cats') #猫的验证图像目录
os.mkdir(validation_cats_dir)
validation_dogs_dir = os.path.join(validation_dir,'dogs') #狗的验证图像目录
os.mkdir(validation_dogs_dir)
test_cats_dir = os.path.join(test_dir,'cats') #猫的测试图像目录
os.mkdir(test_cats_dir)
test_dogs_dir = os.path.join(test_dir,'dogs') #狗的测试图像目录
os.mkdir(test_dogs_dir)
from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32,(3,3),activation='relu',input_shape=(150,150,3)))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64,(3,3),activation='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(128,(3,3),activation='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(128,(3,3),activation='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Flatten())
model.add(layers.Dense(512,activation='relu'))
model.add(layers.Dense(1,activation='sigmoid'))
from tensorflow.keras import optimizers
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=1e-4),
metrics=['acc'])
from keras.preprocessing.image import ImageDataGenerator
# All images will be rescaled by 1./255
train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
# This is the target directory
train_dir,
# All images will be resized to 150x150
target_size=(150, 150),
batch_size=20,
# Since we use binary_crossentropy loss, we need binary labels
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size=(150, 150),
batch_size=20,
class_mode='binary')
``
for data_batch, labels_batch in train_generator:
print('data batch shape:', data_batch.shape)
print('labels batch shape:', labels_batch.shape)
break
````
history = model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=30,
validation_data=validation_generator,
validation_steps=50)