THIS REPOSITORY IS DEPRECATED. USE THE MODULE keras.applications
INSTEAD.
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_predictions
model = 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_input
model = 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 Model
base_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.