from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
# dimensions of our images.
img_width, img_height = 150, 150
train_data_dir = 'flowers/train'
validation_data_dir = 'flowers/validation'
nb_train_samples = 2500
nb_validation_samples = 1000
epochs = 20
batch_size = 50
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(5))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='categorical')
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)
model.save_weights('first_flowers_try.h5')
我们训练了这个模型以分类5个图像类别。我们使用了500张图像来训练模型,并使用每个类别的200张图像来验证模型。我们在tensorflow后端中使用keras进行训练。可以在以下网址下载数据: https://www.kaggle.com/alxmamaev/flowers-recognition 在我们的设置中,我们:
- 创建了一个data/文件夹
- 在data/文件夹内创建了train/和validation/子文件夹
- 在train/和validation/子文件夹中创建了daisy/、dandelion/、rose/、sunflower/和tulip/子文件夹
- 将500张图像放置于每个data/train/daisy、dandelion、rose、sunflower和tulip子文件夹中
- 将每个data/validation/daisy、dandelion、rose、sunflower和tulip文件夹中的200张图像放置于其中,以使每个类别有500个训练示例和200个验证示例。