ResNetInterface
- class ketos.neural_networks.resnet.ResNetInterface(block_sets=[2, 2, 2], n_classes=2, initial_filters=16, initial_strides=1, initial_kernel=[3, 3], strides=2, kernel=[3, 3], optimizer=Adam ketos recipe, loss_function=BinaryCrossentropy ketos recipe, metrics=[BinaryAccuracy ketos recipe, Precision ketos recipe, Recall ketos recipe])[source]
Creates a ResNet model with the standardized Ketos interface.
- Args:
- block_sets: list of ints
A list specifying the block sets and how many blocks each set contains. Example: [2,2,2] will create a ResNet with 3 block sets, each containing 2 ResNetBlocks (i.e.: a total of 6 residual blocks)
- n_classes:int
The number of classes. The output layer uses a Softmax activation and will contain this number of nodes, resulting in model outputs with this many values summing to 1.0.
- initial_filters:int
The number of filters used in the first ResNetBlock. Subsequent blocks will have two times more filters than their previous block.
- initial_strides: int
Strides used in the first convolutional layer
- initial_kernel: int
Kernel size used in the first convolutional layer
- strides: int
Strides used in convolutional layers within the blocks
- kernel: int
Kernel size used in convolutional layers within the blocks
- optimizer: ketos.neural_networks.RecipeCompat object
A recipe compatible optimizer (i.e.: wrapped by the ketos.neural_networksRecipeCompat class)
- loss_function: ketos.neural_networks.RecipeCompat object
A recipe compatible loss_function (i.e.: wrapped by the ketos.neural_networksRecipeCompat class)
- metrics: list of ketos.neural_networks.RecipeCompat objects
A list of recipe compatible metrics (i.e.: wrapped by the ketos.neural_networksRecipeCompat class). These metrics will be computed on each batch during training.
- secondary_metrics: list of ketos.neural_networks.RecipeCompat objects
A list of recipe compatible metrics (i.e.: wrapped by the ketos.neural_networksRecipeCompat class). These can be used as additional metrics. Computed at each batch during training but only printed or logged as the average at the end of the epoch
Methods
Attributes
checkpoint_dir
early_stopping_monitor
Sets an early stopping monitor.
log_dir
test_generator
train_generator
val_generator
valid_losses
valid_metrics
valid_optimizers