`mlair.model_modules.fully_connected_networks`¶

Module Contents¶

Classes¶

`FCN`	A customisable fully connected network (64, 32, 16, window_lead_time), where the last layer is the output layer depending
`FCN_64_32_16`	A customised model 4 Dense layers (64, 32, 16, window_lead_time), where the last layer is the output layer depending

Attributes¶

`__author__`
`__date__`

mlair.model_modules.fully_connected_networks.__author__ = Lukas Leufen¶

mlair.model_modules.fully_connected_networks.__date__ = 2021-02-18¶

class mlair.model_modules.fully_connected_networks.FCN(input_shape: list, output_shape: list, activation='relu', activation_output='linear', optimizer='adam', n_layer=1, n_hidden=10, regularizer=None, dropout=None, layer_configuration=None, batch_normalization=False, **kwargs)¶

Bases: mlair.model_modules.AbstractModelClass

A customisable fully connected network (64, 32, 16, window_lead_time), where the last layer is the output layer depending on the window_lead_time parameter.

_activation¶

_initializer¶

_optimizer¶

_regularizer¶

_requirements = ['lr', 'beta_1', 'beta_2', 'epsilon', 'decay', 'amsgrad', 'momentum', 'nesterov', 'l1', 'l2']¶

_dropout¶

_set_activation(self, activation)¶

_set_optimizer(self, optimizer, **kwargs)¶

_set_regularizer(self, regularizer, **kwargs)¶

_set_dropout(self, activation, dropout_rate)¶

_update_model_name(self)¶

set_model(self)¶: Build the model.

set_compile_options(self)¶

This method only has to be defined in child class, when additional compile options should be used () (other options than optimizer and loss) Has to be set as dictionary: {‘optimizer’: None,

‘loss’: None, ‘metrics’: None, ‘loss_weights’: None, ‘sample_weight_mode’: None, ‘weighted_metrics’: None, ‘target_tensors’: None }

Returns

class mlair.model_modules.fully_connected_networks.FCN_64_32_16(input_shape: list, output_shape: list, **kwargs)¶

Bases: FCN

A customised model 4 Dense layers (64, 32, 16, window_lead_time), where the last layer is the output layer depending on the window_lead_time parameter.

_requirements = ['lr', 'beta_1', 'beta_2', 'epsilon', 'decay', 'amsgrad']¶