# Applications
Keras Applications are deep learning models that are made available alongside pre-trained weights.
These models can be used for prediction, feature extraction, and fine-tuning.
Weights are downloaded automatically when instantiating a model. They are stored at `~/.keras/models/`.
## Available models
### Models for image classification with weights trained on ImageNet:
– [Xception](#xception)
– [VGG16](#vgg16)
– [VGG19](#vgg19)
– [ResNet50](#resnet50)
– [InceptionV3](#inceptionv3)
– [InceptionResNetV2](#inceptionresnetv2)
– [MobileNet](#mobilenet)
– [DenseNet](#densenet)
– [NASNet](#nasnet)
All of these architectures (except Xception and MobileNet) are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image data format set in your Keras configuration file at `~/.keras/keras.json`. For instance, if you have set `image_data_format=channels_last`, then any model loaded from this repository will get built according to the TensorFlow data format convention, “Height-Width-Depth”.
The Xception model is only available for TensorFlow, due to its reliance on `SeparableConvolution` layers.
The MobileNet model is only available for TensorFlow, due to its reliance on `DepthwiseConvolution` layers.
—–
## Usage examples for image classification models
### Classify ImageNet classes with ResNet50
“`python
from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np
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)
# decode the results into a list of tuples (class, description, probability)
# (one such list for each sample in the batch)
print(‘Predicted:’, decode_predictions(preds, top=3)[0])
# Predicted: [(u’n02504013′, u’Indian_elephant’, 0.82658225), (u’n01871265′, u’tusker’, 0.1122357), (u’n02504458′, u’African_elephant’, 0.061040461)]
“`
### Extract features with VGG16
“`python
from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
import numpy as np
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)
“`
### Extract features from an arbitrary intermediate layer with VGG19
“`python
from keras.applications.vgg19 import VGG19
from keras.preprocessing import image
from keras.applications.vgg19 import preprocess_input
from keras.models import Model
import numpy as np
base_model = VGG19(weights=’imagenet’)
model = Model(inputs=base_model.input, outputs=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)
“`
### Fine-tune InceptionV3 on a new set of classes
“`python
from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K
# create the base pre-trained model
base_model = InceptionV3(weights=’imagenet’, include_top=False)
# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let’s add a fully-connected layer
x = Dense(1024, activation=’relu’)(x)
# and a logistic layer — let’s say we have 200 classes
predictions = Dense(200, activation=’softmax’)(x)
# this is the model we will train
model = Model(inputs=base_model.input, outputs=predictions)
# first: train only the top layers (which were randomly initialized)
# i.e. freeze all convolutional InceptionV3 layers
for layer in base_model.layers:
layer.trainable = False
# compile the model (should be done *after* setting layers to non-trainable)
model.compile(optimizer=’rmsprop’, loss=’categorical_crossentropy’)
# train the model on the new data for a few epochs
model.fit_generator(…)
# at this point, the top layers are well trained and we can start fine-tuning
# convolutional layers from inception V3. We will freeze the bottom N layers
# and train the remaining top layers.
# let’s visualize layer names and layer indices to see how many layers
# we should freeze:
for i, layer in enumerate(base_model.layers):
print(i, layer.name)
# we chose to train the top 2 inception blocks, i.e. we will freeze
# the first 249 layers and unfreeze the rest:
for layer in model.layers[:249]:
layer.trainable = False
for layer in model.layers[249:]:
layer.trainable = True
# we need to recompile the model for these modifications to take effect
# we use SGD with a low learning rate
from keras.optimizers import SGD
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss=’categorical_crossentropy’)
# we train our model again (this time fine-tuning the top 2 inception blocks
# alongside the top Dense layers
model.fit_generator(…)
“`
### Build InceptionV3 over a custom input tensor
“`python
from keras.applications.inception_v3 import InceptionV3
from keras.layers import Input
# this could also be the output a different Keras model or layer
input_tensor = Input(shape=(224, 224, 3)) # this assumes K.image_data_format() == ‘channels_last’
model = InceptionV3(input_tensor=input_tensor, weights=’imagenet’, include_top=True)
“`
—–
# Documentation for individual models
| Model | Size | Top-1 Accuracy | Top-5 Accuracy | Parameters | Depth |
| —– | —-: | ————–: | ————–: | ———-: | —–: |
| [Xception](#xception) | 88 MB | 0.790 | 0.945| 22,910,480 | 126 |
| [VGG16](#vgg16) | 528 MB| 0.715 | 0.901 | 138,357,544 | 23
| [VGG19](#vgg19) | 549 MB | 0.727 | 0.910 | 143,667,240 | 26
| [ResNet50](#resnet50) | 99 MB | 0.759 | 0.929 | 25,636,712 | 168
| [InceptionV3](#inceptionv3) | 92 MB | 0.788 | 0.944 | 23,851,784 | 159 |
| [InceptionResNetV2](#inceptionresnetv2) | 215 MB | 0.804 | 0.953 | 55,873,736 | 572 |
| [MobileNet](#mobilenet) | 17 MB | 0.665 | 0.871 | 4,253,864 | 88
| [DenseNet121](#densenet) | 33 MB | 0.745 | 0.918 | 8,062,504 | 121
| [DenseNet169](#densenet) | 57 MB | 0.759 | 0.928 | 14,307,880 | 169
| [DenseNet201](#densenet) | 80 MB | 0.770 | 0.933 | 20,242,984 | 201
The top-1 and top-5 accuracy refers to the model’s performance on the ImageNet validation dataset.
—–
## Xception
“`python
keras.applications.xception.Xception(include_top=True, weights=’imagenet’, input_tensor=None, input_shape=None, pooling=None, classes=1000)
“`
Xception V1 model, with weights pre-trained on ImageNet.
On ImageNet, this model gets to a top-1 validation accuracy of 0.790
and a top-5 validation accuracy of 0.945.
Note that this model is only available for the TensorFlow backend,
due to its reliance on `SeparableConvolution` layers. Additionally it only supports
the data format `’channels_last’` (height, width, channels).
The default input size for this model is 299×299.
### Arguments
– include_top: whether to include the fully-connected layer at the top of the network.
– weights: one of `None` (random initialization) or `’imagenet’` (pre-training on ImageNet).
– input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model.
– input_shape: optional shape tuple, only to be specified
if `include_top` is `False` (otherwise the input shape
has to be `(299, 299, 3)`.
It should have exactly 3 inputs channels,
and width and height should be no smaller than 71.
E.g. `(150, 150, 3)` would be one valid value.
– pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
– `None` means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
– `’avg’` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
– `’max’` means that global max pooling will
be applied.
– classes: optional number of classes to classify images
into, only to be specified if `include_top` is `True`, and
if no `weights` argument is specified.
### Returns
A Keras `Model` instance.
### References
– [Xception: Deep Learning with Depthwise Separable Convolutions](https://arxiv.org/abs/1610.02357)
### License
These weights are trained by ourselves and are released under the MIT license.
—–
## VGG16
“`python
keras.applications.vgg16.VGG16(include_top=True, weights=’imagenet’, input_tensor=None, input_shape=None, pooling=None, classes=1000)
“`
VGG16 model, with weights pre-trained on ImageNet.
This model is available for both the Theano and TensorFlow backend, and can be built both
with `’channels_first’` data format (channels, height, width) or `’channels_last’` data format (height, width, channels).
The default input size for this model is 224×224.
### Arguments
– include_top: whether to include the 3 fully-connected layers at the top of the network.
– weights: one of `None` (random initialization) or `’imagenet’` (pre-training on ImageNet).
– input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model.
– input_shape: optional shape tuple, only to be specified
if `include_top` is `False` (otherwise the input shape
has to be `(224, 224, 3)` (with `’channels_last’` data format)
or `(3, 224, 224)` (with `’channels_first’` data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 48.
E.g. `(200, 200, 3)` would be one valid value.
– pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
– `None` means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
– `’avg’` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
– `’max’` means that global max pooling will
be applied.
– classes: optional number of classes to classify images
into, only to be specified if `include_top` is `True`, and
if no `weights` argument is specified.
### Returns
A Keras `Model` instance.
### References
– [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556): please cite this paper if you use the VGG models in your work.
### License
These weights are ported from the ones [released by VGG at Oxford](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) under the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/).
—–
## VGG19
“`python
keras.applications.vgg19.VGG19(include_top=True, weights=’imagenet’, input_tensor=None, input_shape=None, pooling=None, classes=1000)
“`
VGG19 model, with weights pre-trained on ImageNet.
This model is available for both the Theano and TensorFlow backend, and can be built both
with `’channels_first’` data format (channels, height, width) or `’channels_last’` data format (height, width, channels).
The default input size for this model is 224×224.
### Arguments
– include_top: whether to include the 3 fully-connected layers at the top of the network.
– weights: one of `None` (random initialization) or `’imagenet’` (pre-training on ImageNet).
– input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model.
– input_shape: optional shape tuple, only to be specified
if `include_top` is `False` (otherwise the input shape
has to be `(224, 224, 3)` (with `’channels_last’` data format)
or `(3, 224, 224)` (with `’channels_first’` data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 48.
E.g. `(200, 200, 3)` would be one valid value.
– pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
– `None` means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
– `’avg’` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
– `’max’` means that global max pooling will
be applied.
– classes: optional number of classes to classify images
into, only to be specified if `include_top` is `True`, and
if no `weights` argument is specified.
### Returns
A Keras `Model` instance.
### References
– [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556)
### License
These weights are ported from the ones [released by VGG at Oxford](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) under the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/).
—–
## ResNet50
“`python
keras.applications.resnet50.ResNet50(include_top=True, weights=’imagenet’, input_tensor=None, input_shape=None, pooling=None, classes=1000)
“`
ResNet50 model, with weights pre-trained on ImageNet.
This model is available for both the Theano and TensorFlow backend, and can be built both
with `’channels_first’` data format (channels, height, width) or `’channels_last’` data format (height, width, channels).
The default input size for this model is 224×224.
### Arguments
– include_top: whether to include the fully-connected layer at the top of the network.
– weights: one of `None` (random initialization) or `’imagenet’` (pre-training on ImageNet).
– input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model.
– input_shape: optional shape tuple, only to be specified
if `include_top` is `False` (otherwise the input shape
has to be `(224, 224, 3)` (with `’channels_last’` data format)
or `(3, 224, 224)` (with `’channels_first’` data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 197.
E.g. `(200, 200, 3)` would be one valid value.
– pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
– `None` means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
– `’avg’` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
– `’max’` means that global max pooling will
be applied.
– classes: optional number of classes to classify images
into, only to be specified if `include_top` is `True`, and
if no `weights` argument is specified.
### Returns
A Keras `Model` instance.
### References
– [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)
### License
These weights are ported from the ones [released by Kaiming He](https://github.com/KaimingHe/deep-residual-networks) under the [MIT license](https://github.com/KaimingHe/deep-residual-networks/blob/master/LICENSE).
—–
## InceptionV3
“`python
keras.applications.inception_v3.InceptionV3(include_top=True, weights=’imagenet’, input_tensor=None, input_shape=None, pooling=None, classes=1000)
“`
Inception V3 model, with weights pre-trained on ImageNet.
This model is available for both the Theano and TensorFlow backend, and can be built both
with `’channels_first’` data format (channels, height, width) or `’channels_last’` data format (height, width, channels).
The default input size for this model is 299×299.
### Arguments
– include_top: whether to include the fully-connected layer at the top of the network.
– weights: one of `None` (random initialization) or `’imagenet’` (pre-training on ImageNet).
– input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model.
– input_shape: optional shape tuple, only to be specified
if `include_top` is `False` (otherwise the input shape
has to be `(299, 299, 3)` (with `’channels_last’` data format)
or `(3, 299, 299)` (with `’channels_first’` data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 139.
E.g. `(150, 150, 3)` would be one valid value.
– pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
– `None` means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
– `’avg’` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
– `’max’` means that global max pooling will
be applied.
– classes: optional number of classes to classify images
into, only to be specified if `include_top` is `True`, and
if no `weights` argument is specified.
### Returns
A Keras `Model` instance.
### References
– [Rethinking the Inception Architecture for Computer Vision](http://arxiv.org/abs/1512.00567)
### License
These weights are released under [the Apache License](https://github.com/tensorflow/models/blob/master/LICENSE).
—–
## InceptionResNetV2
“`python
keras.applications.inception_resnet_v2.InceptionResNetV2(include_top=True, weights=’imagenet’, input_tensor=None, input_shape=None, pooling=None, classes=1000)
“`
Inception-ResNet V2 model, with weights pre-trained on ImageNet.
This model is available for Theano, TensorFlow and CNTK backends, and can be built both
with `’channels_first’` data format (channels, height, width) or `’channels_last’` data format (height, width, channels).
The default input size for this model is 299×299.
### Arguments
– include_top: whether to include the fully-connected layer at the top of the network.
– weights: one of `None` (random initialization) or `’imagenet’` (pre-training on ImageNet).
– input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model.
– input_shape: optional shape tuple, only to be specified
if `include_top` is `False` (otherwise the input shape
has to be `(299, 299, 3)` (with `’channels_last’` data format)
or `(3, 299, 299)` (with `’channels_first’` data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 139.
E.g. `(150, 150, 3)` would be one valid value.
– pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
– `None` means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
– `’avg’` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
– `’max’` means that global max pooling will
be applied.
– classes: optional number of classes to classify images
into, only to be specified if `include_top` is `True`, and
if no `weights` argument is specified.
### Returns
A Keras `Model` instance.
### References
– [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261)
### License
These weights are released under [the Apache License](https://github.com/tensorflow/models/blob/master/LICENSE).
—–
## MobileNet
“`python
keras.applications.mobilenet.MobileNet(input_shape=None, alpha=1.0, depth_multiplier=1, dropout=1e-3, include_top=True, weights=’imagenet’, input_tensor=None, pooling=None, classes=1000)
“`
MobileNet model, with weights pre-trained on ImageNet.
Note that only TensorFlow is supported for now,
therefore it only works with the data format
`image_data_format=’channels_last’` in your Keras config at `~/.keras/keras.json`.
To load a MobileNet model via `load_model`, import the custom objects `relu6` and `DepthwiseConv2D` and pass them to the `custom_objects` parameter.
E.g.
“`python
model = load_model(‘mobilenet.h5’, custom_objects={
‘relu6’: mobilenet.relu6,
‘DepthwiseConv2D’: mobilenet.DepthwiseConv2D})
“`
The default input size for this model is 224×224.
### Arguments
– input_shape: optional shape tuple, only to be specified
if `include_top` is `False` (otherwise the input shape
has to be `(224, 224, 3)` (with `’channels_last’` data format)
or `(3, 224, 224)` (with `’channels_first’` data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 32.
E.g. `(200, 200, 3)` would be one valid value.
– alpha: controls the width of the network.
– If `alpha` < 1.0, proportionally decreases the number
of filters in each layer.
- If `alpha` > 1.0, proportionally increases the number
of filters in each layer.
– If `alpha` = 1, default number of filters from the paper
are used at each layer.
– depth_multiplier: depth multiplier for depthwise convolution
(also called the resolution multiplier)
– dropout: dropout rate
– include_top: whether to include the fully-connected
layer at the top of the network.
– weights: `None` (random initialization) or
`’imagenet’` (ImageNet weights)
– input_tensor: optional Keras tensor (i.e. output of
`layers.Input()`)
to use as image input for the model.
– pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
– `None` means that the output of the model
will be the 4D tensor output of the
last convolutional layer.
– `’avg’` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a
2D tensor.
– `’max’` means that global max pooling will
be applied.
– classes: optional number of classes to classify images
into, only to be specified if `include_top` is `True`, and
if no `weights` argument is specified.
### Returns
A Keras `Model` instance.
### References
– [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/pdf/1704.04861.pdf)
### License
These weights are released under [the Apache License](https://github.com/tensorflow/models/blob/master/LICENSE).
—–
## DenseNet
“`python
keras.applications.densenet.DenseNet121(include_top=True, weights=’imagenet’, input_tensor=None, input_shape=None, pooling=None, classes=1000)
keras.applications.densenet.DenseNet169(include_top=True, weights=’imagenet’, input_tensor=None, input_shape=None, pooling=None, classes=1000)
keras.applications.densenet.DenseNet201(include_top=True, weights=’imagenet’, input_tensor=None, input_shape=None, pooling=None, classes=1000)
“`
Optionally loads weights pre-trained
on ImageNet. Note that when using TensorFlow,
for best performance you should set
`image_data_format=’channels_last’` in your Keras config
at ~/.keras/keras.json.
The model and the weights are compatible with
TensorFlow, Theano, and CNTK. The data format
convention used by the model is the one
specified in your Keras config file.
### Arguments
– blocks: numbers of building blocks for the four dense layers.
– include_top: whether to include the fully-connected
layer at the top of the network.
– weights: one of `None` (random initialization),
‘imagenet’ (pre-training on ImageNet),
or the path to the weights file to be loaded.
– input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
to use as image input for the model.
– input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(224, 224, 3)` (with `channels_last` data format)
or `(3, 224, 224)` (with `channels_first` data format).
It should have exactly 3 inputs channels.
– pooling: optional pooling mode for feature extraction
when `include_top` is `False`.
– `None` means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
– `avg` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
– `max` means that global max pooling will
be applied.
– classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
### Returns
A Keras model instance.
### References
– [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993) (CVPR 2017 Best Paper Award)
### License
These weights are released under [the BSD 3-clause License](https://github.com/liuzhuang13/DenseNet/blob/master/LICENSE).
—–
## NASNet
“`python
keras.applications.nasnet.NASNetLarge(input_shape=None, include_top=True, weights=’imagenet’, input_tensor=None, pooling=None, classes=1000)
keras.applications.nasnet.NASNetMobile(input_shape=None, include_top=True, weights=’imagenet’, input_tensor=None, pooling=None, classes=1000)
“`
Neural Architecture Search Network (NASNet) model, with weights pre-trained on ImageNet.
Note that only TensorFlow is supported for now,
therefore it only works with the data format
`image_data_format=’channels_last’` in your Keras config at `~/.keras/keras.json`.
The default input size for the NASNetLarge model is 331×331 and for the
NASNetMobile model is 224×224.
### Arguments
– input_shape: optional shape tuple, only to be specified
if `include_top` is `False` (otherwise the input shape
has to be `(224, 224, 3)` (with `’channels_last’` data format)
or `(3, 224, 224)` (with `’channels_first’` data format)
for NASNetMobile or `(331, 331, 3)` (with `’channels_last’`
data format) or `(3, 331, 331)` (with `’channels_first’`
data format) for NASNetLarge.
It should have exactly 3 inputs channels,
and width and height should be no smaller than 32.
E.g. `(200, 200, 3)` would be one valid value.
– include_top: whether to include the fully-connected
layer at the top of the network.
– weights: `None` (random initialization) or
`’imagenet’` (ImageNet weights)
– input_tensor: optional Keras tensor (i.e. output of
`layers.Input()`)
to use as image input for the model.
– pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
– `None` means that the output of the model
will be the 4D tensor output of the
last convolutional layer.
– `’avg’` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a
2D tensor.
– `’max’` means that global max pooling will
be applied.
– classes: optional number of classes to classify images
into, only to be specified if `include_top` is `True`, and
if no `weights` argument is specified.
### Returns
A Keras `Model` instance.
### References
– [Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/abs/1707.07012)
### License
These weights are released under [the Apache License](https://github.com/tensorflow/models/blob/master/LICENSE).