keraflow.layers.base.Input Class Reference

The entering point of each model. More...

Inheritance diagram for keraflow.layers.base.Input:

Public Member Functions
def	__init__
Public Member Functions inherited from keraflow.layers.base.Layer
def	__init__
def	set_trainable_params
	Register the layer's trainable parameters. More...
def	input_dimension
	Return the expected dimension of input tensor. More...
def	check_input_shape
	Check if the input shape(s) are valid. More...
def	init_param
	Initializes trainable parameter(s) of the layer. More...
def	output
	Calculates the output tensor of the layer given the input tensor(s). More...
def	output_shape
	Calculates the shape of the output tensor given the shape of the input tensor(s). More...
def	support_mask
	Whether the layer supports to carry on to the output tensor the mask embedded in input tensor. More...
def	pack_init_param
	Check keraflow.utils.generic_utils.serialize.
def	get_tensor_shape
	Get the shape of a tensor. More...
def	get_weights
	Gets trainable parameter name, value (numpy arrays) pairs. More...
def	embed
	Embeds the target layer such that its trainable parameters (along with regularizers and constraints on the parameters) are treated as the host layer's parameters and are updated during traing process. More...
def	__call__
	Feed an input kensor or multiple input kensors to the layer and outputs another Kensor. More...
Public Member Functions inherited from keraflow.layers.base.Kensor
def	__init__

Additional Inherited Members
Static Public Member Functions inherited from keraflow.layers.base.Layer
def	unpack_init_param
	Check keraflow.utils.generic_utils.unserialize. More...

Detailed Description

The entering point of each model.

Constructor & Destructor Documentation

def keraflow.layers.base.Input.__init__	(		self,
			shape,
			dtype = B.floatx(),
			batch_size = None,
			mask_value = None,
			kwargs
	)

Parameters

shape	tuple. The expected shape of the input data.
dtype	str/type. the expected data type of the input data.
batch_size	int. The expected batch size. If None, accepts any number of batch. Should be same for all inputs of a model.
mask_value	int. The input value to be masked. If not None, a special mask tensor indicating which indices in the input should be skipped will be passed to the following layers (if the layers support masking). This is majorly used for recurrent layers, which may take variable length input.
kwargs	see Layer.__init__