CNNInterface

class ketos.neural_networks.cnn.CNNInterface(convolutional_layers=[{'n_filters': 32, 'filter_shape': (8, 8), 'strides': 4, 'padding': 'valid', 'activation': 'relu', 'max_pool': {'pool_size': (3, 3), 'strides': (2, 2)}, 'batch_normalization': True}, {'n_filters': 64, 'filter_shape': (3, 3), 'strides': 1, 'padding': 'valid', 'activation': 'relu', 'max_pool': {'pool_size': (3, 3), 'strides': (2, 2)}, 'batch_normalization': True}], dense_layers=[{'n_hidden': 512, 'activation': 'relu', 'batch_normalization': True, 'dropout': 0.5}, {'n_hidden': 128, 'activation': 'relu', 'batch_normalization': True, 'dropout': 0.5}], n_classes=2, optimizer=Adam ketos recipe, loss_function=BinaryCrossentropy ketos recipe, metrics=[BinaryAccuracy ketos recipe])[source]

Creates a CNN model with the standardized Ketos interface.

Args:

convolutional_layers: list

A list of dictionaries containing the detailed specification for the convolutional layers. Each layer is specified as a dictionary with the following format:

>>> {'n_filters':96, "filter_shape":(11,11), 'strides':4, 'padding':'valid', activation':'relu', 'max_pool': {'pool_size':(3,3) , 'strides':(2,2)}, 'batch_normalization':True} 

dense_layers: list

A list of dictionaries containing the detailed specification for the fully connected layers. Each layer is specified as a dictionary with the following format:

>>> {'n_hidden':4096, 'activation':'relu', 'batch_normalization':True, 'dropout':0.5} 

n_classes:int

The number of classes the network will be used to classify. The output will be this number of values representing the scores for each class. Scores sum to 1.0.

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.

Examples:

>>> # Most users will create a model based on a Ketos recipe 
>>> # The one below, specifies a CNN with 3 convolutional layers and 2 dense layers
>>>
>>> recipe = {'conv_set':[[64, False], [128, True], [256, True]], 
...   'dense_set': [512, ],
...   'n_classes':2,
...   'optimizer': {'recipe_name':'Adam', 'parameters': {'learning_rate':0.005}},
...   'loss_function': {'recipe_name':'FScoreLoss', 'parameters':{}},  
...   'metrics': [{'recipe_name':'CategoricalAccuracy', 'parameters':{}}]
... }
>>> # To create the CNN, simply  use the  'build_from_recipe' method:
>>> cnn = CNNInterface._build_from_recipe(recipe, recipe_compat=False) 

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`