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		Accelerator
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		Constant
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		Generator
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LSTM Cell
Description
Define the cell lstm layer according to its parameters. To be used for the RNN layer. Type : polymorphic.

Input parameters
 Β Parameters :Β layer parameters.
Β Parameters :Β layer parameters.
 Β units :Β integer, dimensionality of the output space.
Β units :Β integer, dimensionality of the output space. Β ActivationΒ :Β cluster,Β applied to the candidate cell input. This function transforms the new information considered for updating the cell state.
Β ActivationΒ :Β cluster,Β applied to the candidate cell input. This function transforms the new information considered for updating the cell state. Β Output ActivationΒ :Β cluster,Β applied to the updated cell state before producing the visible hidden output of the LSTM at each time step.
Β Output ActivationΒ :Β cluster,Β applied to the updated cell state before producing the visible hidden output of the LSTM at each time step. Β Recurrent ActivationΒ :Β cluster,Β applied to the input, forget, and output gates. It controls which parts of the past information are allowed to pass or be blocked.
Β Recurrent ActivationΒ :Β cluster,Β applied to the input, forget, and output gates. It controls which parts of the past information are allowed to pass or be blocked. Β use bias? :Β boolean, whether the layer uses a bias vector.
Β use bias? :Β boolean, whether the layer uses a bias vector.
Default value βTrueβ. Β Input Weight InitializerΒ :Β cluster,Β initializer for theΒ
Β Input Weight InitializerΒ :Β cluster,Β initializer for theΒ kernelΒ weights matrix, used for the linear transformation of the inputs. Β Hidden Weight InitializerΒ :Β cluster,Β initializer for theΒ
Β Hidden Weight InitializerΒ :Β cluster,Β initializer for theΒ recurrent_kernelΒ weights matrix, used for the linear transformation of the recurrent state. Β Bias InitializerΒ :Β cluster,Β initializer for the bias vector.
Β Bias InitializerΒ :Β cluster,Β initializer for the bias vector. Β unit forget bias? :Β boolean, if True, add 1 to the bias of the forget gate at initialization.
Β unit forget bias? :Β boolean, if True, add 1 to the bias of the forget gate at initialization.
Default value βTrueβ. Β dropoutΒ :Β float, fraction of the units to drop for the linear transformation of the inputs.
Β dropoutΒ :Β float, fraction of the units to drop for the linear transformation of the inputs.
Default value β0.0β. Β recurrent dropout :Β float, fraction of the units to drop for the linear transformation of the recurrent state.
Β recurrent dropout :Β float, fraction of the units to drop for the linear transformation of the recurrent state.
Default value β0.0β. Β Input Weight RegularizerΒ :Β cluster,Β regularizer function applied to theΒ
Β Input Weight RegularizerΒ :Β cluster,Β regularizer function applied to theΒ kernelΒ weights matrix. Β Hidden Weight RegularizerΒ :Β cluster,Β regularizer function applied to theΒ
Β Hidden Weight RegularizerΒ :Β cluster,Β regularizer function applied to theΒ recurrent_kernelΒ weights matrix. Β Bias RegularizerΒ :Β cluster,Β regularizer function applied to the bias vector.
Β Bias RegularizerΒ :Β cluster,Β regularizer function applied to the bias vector. Β training?Β :Β boolean, whether the layer is in training mode (can store data for backward).
Β training?Β :Β boolean, whether the layer is in training mode (can store data for backward).
Default value βTrueβ. Β store?Β :Β boolean, whether the layer stores the last iteration gradient (accessible via the βget_gradientsβ function).
Β store?Β :Β boolean, whether the layer stores the last iteration gradient (accessible via the βget_gradientsβ function).
Default value βFalseβ. Β update?Β :Β boolean, whether the layerβs variables should be updated during backward. Equivalent to freeze the layer.
Β update?Β :Β boolean, whether the layerβs variables should be updated during backward. Equivalent to freeze the layer.
Default value βTrueβ.
Output parameters
 CellΒ : cluster, this cluster defines the recurrent cell type used in a recurrent layer.
 CellΒ : cluster, this cluster defines the recurrent cell type used in a recurrent layer. 
 enum :Β enum, an enumeration indicating the cell type (e.g., SimpleRNN, LSTM, GRU, etc.). If
 enum :Β enum, an enumeration indicating the cell type (e.g., SimpleRNN, LSTM, GRU, etc.). If enum is set to CustomCell, the class on the right will be used. Otherwise, the selected cell type will be instantiated with default parameters. Β Class :Β object, a custom RNN cell class instance.
Β Class :Β object, a custom RNN cell class instance.
Example
All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).
LSTM cell inside RNN layer
 
			

