Keras documentation: Keras Applications (original) (raw)

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/.

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 data format convention "Height-Width-Depth".

Available models

The top-1 and top-5 accuracy refers to the model's performance on the ImageNet validation dataset.

Depth refers to the topological depth of the network. This includes activation layers, batch normalization layers etc.

Time per inference step is the average of 30 batches and 10 repetitions.

• CPU: AMD EPYC Processor (with IBPB) (92 core)
• RAM: 1.7T
• GPU: Tesla A100
• Batch size: 32

Depth counts the number of layers with parameters.

Usage examples for image classification models

Classify ImageNet classes with ResNet50

`import keras from keras.applications.resnet50 import ResNet50 from keras.applications.resnet50 import preprocess_input, decode_predictions import numpy as np

model = ResNet50(weights='imagenet')

img_path = 'elephant.jpg' img = keras.utils.load_img(img_path, target_size=(224, 224)) x = keras.utils.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)]

`

`import keras from keras.applications.vgg16 import VGG16 from keras.applications.vgg16 import preprocess_input import numpy as np

model = VGG16(weights='imagenet', include_top=False)

img_path = 'elephant.jpg' img = keras.utils.load_img(img_path, target_size=(224, 224)) x = keras.utils.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x)

features = model.predict(x) `

`from keras.applications.vgg19 import VGG19 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 = keras.utils.load_img(img_path, target_size=(224, 224)) x = keras.utils.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

`from keras.applications.inception_v3 import InceptionV3 from keras.models import Model from keras.layers import Dense, GlobalAveragePooling2D

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(...)

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(...) `

Build InceptionV3 over a custom input tensor

`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))

model = InceptionV3(input_tensor=input_tensor, weights='imagenet', include_top=True) `