teras.layers.LayerList

class teras.layers.LayerList(layers, sequential=True, **kwargs)

LayerList is a list of layers, but is also a layer itself. If you know what that means, great. if not, well it’s alright. you’ll get there, one day, sooner or later. just keep pushing. keep learning. never give up. you got this, king/queen!

Parameters:

• layers (list) – list, list of Keras layers

• sequential (bool) – bool, whether to build layers sequentially. Set it to True ONLY when each layer in the list is applied one after the other in a sequential manner. Otherwise, set it to False. If sequential, each layer is built using the output shape of the previous layer, with first layer being built with the input_shape argument to the build method. Otherwise, each layer is built using the input_shape argument received.

__init__(layers, sequential=True, **kwargs)

Methods

__init__(layers[, sequential])
add_loss(loss)	Can be called inside of the call() method to add a scalar loss.
add_metric()
add_variable(shape, initializer[, dtype, ...])	Add a weight variable to the layer.
add_weight([shape, initializer, dtype, ...])	Add a weight variable to the layer.
build(input_shape)	Build.
build_from_config(config)	Builds the layer's states with the supplied config dict.
call(inputs)
compute_mask(inputs, previous_mask)
compute_output_shape(input_shape)
compute_output_spec(*args, **kwargs)
count_params()	Count the total number of scalars composing the weights.
from_config(config)	Creates an operation from its config.
get_build_config()	Returns a dictionary with the layer's input shape.
get_config()	Returns the config of the object.
get_weights()	Return the values of layer.weights as a list of NumPy arrays.
load_own_variables(store)	Loads the state of the layer.
quantize(mode)
quantized_call(*args, **kwargs)
save_own_variables(store)	Saves the state of the layer.
set_weights(weights)	Sets the values of layer.weights from a list of NumPy arrays.
stateless_call(trainable_variables, ...[, ...])	Call the layer without any side effects.
symbolic_call(*args, **kwargs)

Attributes

compute_dtype	The dtype of the computations performed by the layer.
dtype	Alias of layer.variable_dtype.
dtype_policy
input	Retrieves the input tensor(s) of a symbolic operation.
input_dtype	The dtype layer inputs should be converted to.
input_spec
losses	List of scalar losses from add_loss, regularizers and sublayers.
metrics	List of all metrics.
metrics_variables	List of all metric variables.
non_trainable_variables	List of all non-trainable layer state.
non_trainable_weights	List of all non-trainable weight variables of the layer.
output	Retrieves the output tensor(s) of a layer.
path	The path of the layer.
quantization_mode	The quantization mode of this layer, None if not quantized.
supports_masking	Whether this layer supports computing a mask using compute_mask.
trainable	Settable boolean, whether this layer should be trainable or not.
trainable_variables	List of all trainable layer state.
trainable_weights	List of all trainable weight variables of the layer.
variable_dtype	The dtype of the state (weights) of the layer.
variables	List of all layer state, including random seeds.
weights	List of all weight variables of the layer.