keras分类之二分类实例(Cat and dog)

# 存储数据集的目录
base_dir = 'E:/python learn/dog_and_cat/data/'
 
# 训练、验证数据集的目录
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
test_dir = os.path.join(base_dir, 'test')
 
# 猫训练图片所在目录
train_cats_dir = os.path.join(train_dir, 'cats')
 
# 狗训练图片所在目录
train_dogs_dir = os.path.join(train_dir, 'dogs')
 
# 猫验证图片所在目录
validation_cats_dir = os.path.join(validation_dir, 'cats')
 
# 狗验证数据集所在目录
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
 
print('total training cat images:', len(os.listdir(train_cats_dir))) 
print('total training dog images:', len(os.listdir(train_dogs_dir))) 
print('total validation cat images:', len(os.listdir(validation_cats_dir))) 
print('total validation dog images:', len(os.listdir(validation_dogs_dir)))

# 搭建模型
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu',
         input_shape=(150, 150, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
 
print(model.summary())
 
model.compile(loss='binary_crossentropy',
       optimizer=RMSprop(lr=1e-4),
       metrics=['acc'])

train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)
 
train_generator = train_datagen.flow_from_directory(
  train_dir, # target directory
  target_size=(150, 150), # resize图片
  batch_size=20,
  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
 
hist = model.fit_generator(
  train_generator,
  steps_per_epoch=100,
  epochs=10,
  validation_data=validation_generator,
  validation_steps=50
)
 
model.save('cats_and_dogs_small_1.h5')

acc = hist.history['acc']
val_acc = hist.history['val_acc']<i>本文来源gaodai$ma#com搞$代*码网2</i>
loss = hist.history['loss']
val_loss = hist.history['val_loss']
 
epochs = range(len(acc))
 
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
 
plt.legend()
plt.figure()
 
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.legend()
plt.show()

imagename = 'E:/python learn/dog_and_cat/data/validation/dogs/dog.2026.jpg'
test_image = image.load_img(imagename, target_size = (150, 150))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis=0)
result = model.predict(test_image)
 
if result[0][0] == 1:
  prediction ='dog'
else:
  prediction ='cat'
  
print(prediction)