Keras code and weights files for popular deep learning models.
THIS REPOSITORY IS DEPRECATED. USE THE MODULE
keras.applicationsINSTEAD.
Pull requests will not be reviewed nor merged. Direct any PRs to
keras.applications. Issues are not monitored either.
This repository contains code for the following Keras models:
All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at
~/.keras/keras.json. For instance, if you have set
image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth".
Pre-trained weights can be automatically loaded upon instantiation (
weights='imagenet'argument in model constructor for all image models,
weights='msd'for the music tagging model). Weights are automatically downloaded if necessary, and cached locally in
~/.keras/models/.
from resnet50 import ResNet50 from keras.preprocessing import image from imagenet_utils import preprocess_input, decode_predictionsmodel = ResNet50(weights='imagenet')
img_path = 'elephant.jpg' img = image.load_img(img_path, target_size=(224, 224)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x)
preds = model.predict(x) print('Predicted:', decode_predictions(preds))
print: [[u'n02504458', u'African_elephant']]
from vgg16 import VGG16 from keras.preprocessing import image from imagenet_utils import preprocess_inputmodel = VGG16(weights='imagenet', include_top=False)
img_path = 'elephant.jpg' img = image.load_img(img_path, target_size=(224, 224)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x)
features = model.predict(x)
from vgg19 import VGG19 from keras.preprocessing import image from imagenet_utils import preprocess_input from keras.models import Modelbase_model = VGG19(weights='imagenet') model = Model(input=base_model.input, output=base_model.get_layer('block4_pool').output)
img_path = 'elephant.jpg' img = image.load_img(img_path, target_size=(224, 224)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x)
block4_pool_features = model.predict(x)
Additionally, don't forget to cite Keras if you use these models.