zoo/senet/se_resnet_c.py
def example():
    ''' 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()
 Example for constructing/training a SE-ResNet model on CIFAR-10
    
Example for constructing/training a SE-ResNet model on CIFAR-10
Example for constructing / training a SE - ResNet model on CIFAR - 10
def example():
    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()
def example ( ) : 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 ( )
Example for constructing/training a SE-ResNet model on CIFAR-10
Example for constructing / training a SE - ResNet model on CIFAR - 10
''' Example for constructing/training a SE-ResNet model on CIFAR-10
    ''' # Example of constructing a mini-ResNet