Versioned name: LSTMCell-1
Category: Sequence processing
Short description: LSTMCell operation represents a single LSTM cell. It computes the output using the formula described in the original paper Long Short-Term Memory.
Detailed description
Formula:
* - matrix mult
(.) - eltwise mult
[,] - concatenation
sigm - 1/(1 + e^{-x})
tanh - (e^{2x} - 1)/(e^{2x} + 1)
f = sigm(Wf*[Hi, X] + Bf)
i = sigm(Wi*[Hi, X] + Bi)
c = tanh(Wc*[Hi, X] + Bc)
o = sigm(Wo*[Hi, X] + Bo)
Co = f (.) Ci + i (.) c
Ho = o (.) tanh(Co)
Attributes
- hidden_size
- Description: hidden_size specifies hidden state size.
- Range of values: a positive integer
- Type:
int
- Default value: None
- Required: yes
- activations
- Description: activations specifies activation functions for gates, there are three gates, so three activation functions should be specified as a value for this attributes
- Range of values: any combination of relu, sigmoid, tanh
- Type: a list of strings
- Default value: sigmoid,tanh,tanh
- Required: no
- activations_alpha, activations_beta
- Description: activations_alpha, activations_beta attributes of functions; applicability and meaning of these attributes depends on chosen activation functions
- Range of values: a list of floating-point numbers
- Type:
float[]
- Default value: None
- Required: no
- clip
- Description: clip specifies bound values [-C, C] for tensor clipping. Clipping is performed before activations.
- Range of values: a positive floating-point number
- Type:
float
- Default value: infinity that means that the clipping is not applied
- Required: no
Inputs
- 1:
X
- 2D tensor of type T [batch_size, input_size]
, input data. Required.
- 2:
initial_hidden_state
- 2D ([batch_size, hidden_size]) tensor of type T. Required.
- 3:
initial_cell_state
- 2D ([batch_size, hidden_size]) tensor of type T. Required.
- 4:
W
- 2D tensor of type T [4 * hidden_size, input_size]
, the weights for matrix multiplication, gate order: fico. Required.
- 5:
R
- 2D tensor of type T [4 * hidden_size, hidden_size]
, the recurrence weights for matrix multiplication, gate order: fico. Required.
- 6:
B
1D tensor of type T [4 * hidden_size]
, the sum of biases (weights and recurrence weights). Required.
Outputs
- 1:
Ho
- 2D tensor of type T [batch_size, hidden_size]
, output hidden state, .
- 2:
Co
- 2D tensor of type T [batch_size, hidden_size]
, output cell state.
Types
- T: any supported floating point type.
Example
<layer ... type="LSTMCell" ...>
<data hidden_size="128"/>
<input>
<port id="0">
<dim>1</dim>
<dim>16</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>128</dim>
</port>
<port id="2">
<dim>1</dim>
<dim>128</dim>
</port>
<port id="3">
<dim>512</dim>
<dim>16</dim>
</port>
<port id="4">
<dim>512</dim>
<dim>128</dim>
</port>
<port id="5">
<dim>512</dim>
</port>
</input>
<output>
<port id="6">
<dim>1</dim>
<dim>128</dim>
</port>
<port id="7">
<dim>1</dim>
<dim>128</dim>
</port>
</output>
</layer>