Tensorflow permute dimensions update#

Tensorflow permute dimensions update#

weight regularization losses).Īdd update op(s), potentially dependent on layer inputs. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. This is the case for activity regularization losses, for instance. inputs: If anything other than None is passed, it signals the losses are conditional on some of the layer's inputs, and thus they should only be run where these inputs are available.losses: Loss tensor, or list/tuple of tensors.Activity regularization is not supported directly (but such losses may be returned from Layer.call()). Instead, variable regularizers may be added through add_variable. Note that add_loss is not supported when executing eagerly. The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs. This method automatically keeps track of dependencies. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. TypeError: If positional arguments are passed and this Layer is not a subclassed Model.Īdd loss tensor(s), potentially dependent on layer inputs.ValueError: in case the layer is missing shape information for its build call.**kwargs: Additional keyword arguments to be passed to call().In other cases, Layer inputs must be passed using the inputs argument and non-inputs must be keyword arguments.

Only allowed in subclassed Models with custom call() signatures. *args: Additional positional arguments to be passed to call().inputs: Can be a tensor or list/tuple of tensors.This is done as part of _add_inbound_node(). We update the _keras_history of the output tensor(s) with the current layer. If necessary, we build the layer to match the shape of the input(s). If a Keras tensor is passed: - We call self._add_inbound_node(). Wrapper around self.call(), for handling internal references. See help(type(self)) for accurate signature. Returns the list of all layer variables/weights. Scope_name trainable_variables trainable_weights updates variables AttributeError: if the layer has no defined output shape.Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor). Only applicable if the layer has one output, or if all outputs have the same shape. Retrieves the output shape(s) of a layer. Output mask tensor (potentially None) or list of output mask tensors. Retrieves the output mask tensor(s) of a layer. if it is connected to one incoming layer. Only applicable if the layer has exactly one output, i.e. Retrieves the output tensor(s) of a layer. name non_trainable_variables non_trainable_weights output When using graph execution, variable regularization ops have already been created and are simply returned here. Note that when executing eagerly, getting this property evaluates regularizers. Losses which are associated with this Layer. AttributeError: if the layer has no defined input_shape.Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor). if it is connected to one incoming layer, or if all inputs have the same shape. Only applicable if the layer has exactly one input, i.e. Input mask tensor (potentially None) or list of input mask tensors. Only applicable if the layer has exactly one inbound node, i.e.

Retrieves the input mask tensor(s) of a layer.

AttributeError: If no inbound nodes are found.

AttributeError: if the layer is connected to more than one incoming layers.

Retrieves the input tensor(s) of a layer.

Optional regularizer function for the output of this layer. Output shape: Same as the input shape, but with the dimensions re-ordered according to the specified pattern. Use the keyword argument input_shape (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. For instance, (2, 1) permutes the first and second dimension of the input. Permutation pattern, does not include the samples dimension. # now: model.output_shape = (None, 64, 10) Permutes the dimensions of the input according to a given pattern. Defined in tensorflow/python/keras/_impl/keras/layers/core.py.