senet

SE-Net

se_resnet.py - academic (idiomatic)
se_resnext.py - academic (idiomatic)
se_resnet_c.py - production (composable)
se_resnext_c.py - production (composable)

Paper

Macro-Architecture

Micro-Architecture

Residual Block and Identity Shortcut w/SE Link

Residual Block and Projection Shortcut w/SE Link

Squeeze-Excitation Block

Composable

Example: Instantiate a stock SE-ResNet model

from se_resnet_c import SEResNet

# SE-ResNet50 from research paper
senet = SEResNet(50)

# ResNet50 custom input shape/classes
senet = SEResNet(50, input_shape=(128, 128, 3), n_classes=50)

# getter for the tf.keras model
model = senet.model

Example: Compose and Train a SE-ResNet

    ''' Example for constructing/training a SE-ResNet model on CIFAR-10
    '''
    # Example of constructing a mini-ResNet
    groups = [ { 'n_filters' : 64, 'n_blocks': 1 },
               { 'n_filters': 128, 'n_blocks': 2 },
               { 'n_filters': 256, 'n_blocks': 2 } ]
    senet = SEResNet(groups, input_shape=(32, 32, 3), n_classes=10)
    senet.model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['acc'])
    senet.model.summary()
    senet.cifar10()