LSTM - 7 vs 14

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1. LSTM7 → LSTM14 +0 -11

LSTM7 → LSTM14 RENAMED Viewed

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1 1	Computes an one-layer LSTM. This operator is usually supported via some
2 2	custom implementation such as CuDNN.
3 3	Notations:
4 4	X - input tensor
5 5	i - input gate
6 6	o - output gate
7 7	f - forget gate
8 8	c - cell gate
9 9	t - time step (t-1 means previous time step)
10 10	W[iofc] - W parameter weight matrix for input, output, forget, and cell gates
11 11	R[iofc] - R recurrence weight matrix for input, output, forget, and cell gates
12 12	Wb[iofc] - W bias vectors for input, output, forget, and cell gates
13 13	Rb[iofc] - R bias vectors for input, output, forget, and cell gates
14 14	P[iof] - P peephole weight vector for input, output, and forget gates
15 15	WB[iofc] - W parameter weight matrix for backward input, output, forget, and cell gates
16 16	RB[iofc] - R recurrence weight matrix for backward input, output, forget, and cell gates
17 17	WBb[iofc] - W bias vectors for backward input, output, forget, and cell gates
18 18	RBb[iofc] - R bias vectors for backward input, output, forget, and cell gates
19 19	PB[iof] - P peephole weight vector for backward input, output, and forget gates
20 20	H - Hidden state
21 21	num_directions - 2 if direction == bidirectional else 1
22 22	Activation functions:
23 23	Relu(x) - max(0, x)
24 24	Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
25 25	Sigmoid(x) - 1/(1 + e^{-x})
26 26	(NOTE: Below are optional)
27 27	Affine(x) - alpha*x + beta
28 28	LeakyRelu(x) - x if x >= 0 else alpha * x
29 29	ThresholdedRelu(x) - x if x >= alpha else 0
30 30	ScaledTanh(x) - alpha*Tanh(beta*x)
31 31	HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
32 32	Elu(x) - x if x >= 0 else alpha*(e^x - 1)
33 33	Softsign(x) - x/(1 + |x|)
34 34	Softplus(x) - log(1 + e^x)
35 35	Equations (Default: f=Sigmoid, g=Tanh, h=Tanh):
36 36	- it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi)
37 37	- ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf)
38 38	- ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
39 39	- Ct = ft (.) Ct-1 + it (.) ct
40 40	- ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo)
41 41	- Ht = ot (.) h(Ct)
42 42	This operator has **optional** inputs/outputs. See ONNX <https://github.com/onnx/onnx/blob/master/docs/IR.md>_ for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
43 43	**Attributes**
44 44	* **activation_alpha**:
45 45	Optional scaling values used by some activation functions. The
46 46	values are consumed in the order of activation functions, for
47 47	example (f, g, h) in LSTM. Default values are the same as of
48 48	corresponding ONNX operators.For example with LeakyRelu, the default
49 49	alpha is 0.01.
50 50	* **activation_beta**:
51 51	Optional scaling values used by some activation functions. The
52 52	values are consumed in the order of activation functions, for
53 53	example (f, g, h) in LSTM. Default values are the same as of
54 54	corresponding ONNX operators.
55 55	* **activations**:
56 56	A list of 3 (or 6 if bidirectional) activation functions for input,
57 57	output, forget, cell, and hidden. The activation functions must be
58 58	one of the activation functions specified above. Optional: See the
59 59	equations for default if not specified.
60 60	* **clip**:
61 61	Cell clip threshold. Clipping bounds the elements of a tensor in the
62 62	range of [-threshold, +threshold] and is applied to the input of
63 63	activations. No clip if not specified.
64 64	* **direction**:
65 65	Specify if the RNN is forward, reverse, or bidirectional. Must be
66 66	one of forward (default), reverse, or bidirectional.
67 67	* **hidden_size**:
68 68	Number of neurons in the hidden layer
69 69	* **input_forget**:
70 70	Couple the input and forget gates if 1.
71	- * **layout**:
72	- The shape format of inputs X, initial_h, initial_c and outputs Y,
73	- Y_h, Y_c. If 0, the following shapes are expected: X.shape =
74	- [seq_length, batch_size, input_size], Y.shape = [seq_length,
75	- num_directions, batch_size, hidden_size], initial_h.shape =
76	- Y_h.shape = initial_c.shape = Y_c.shape = [num_directions,
77	- batch_size, hidden_size]. If 1, the following shapes are expected:
78	- X.shape = [batch_size, seq_length, input_size], Y.shape =
79	- [batch_size, seq_length, num_directions, hidden_size],
80	- initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape =
81	- [batch_size, num_directions, hidden_size].
82 71	**Inputs**
83 72	Between 3 and 8 inputs.
84 73	* **X** (heterogeneous) - **T**:
85 74	The input sequences packed (and potentially padded) into one 3-D
86 75	tensor with the shape of [seq_length, batch_size, input_size].
87 76	* **W** (heterogeneous) - **T**:
88 77	The weight tensor for the gates. Concatenation of W[iofc] and
89 78	WB[iofc] (if bidirectional) along dimension 0. The tensor has
90 79	shape [num_directions, 4*hidden_size, input_size].
91 80	* **R** (heterogeneous) - **T**:
92 81	The recurrence weight tensor. Concatenation of R[iofc] and
93 82	RB[iofc] (if bidirectional) along dimension 0. This tensor has
94 83	shape [num_directions, 4*hidden_size, hidden_size].
95 84	* **B** (optional, heterogeneous) - **T**:
96 85	The bias tensor for input gate. Concatenation of [Wb[iofc],
97 86	Rb[iofc]], and [WBb[iofc], RBb[iofc]] (if bidirectional) along
98 87	dimension 0. This tensor has shape [num_directions,
99 88	8*hidden_size]. Optional: If not specified - assumed to be 0.
100 89	* **sequence_lens** (optional, heterogeneous) - **T1**:
101 90	Optional tensor specifying lengths of the sequences in a batch. If
102 91	not specified - assumed all sequences in the batch to have length
103 92	seq_length. It has shape [batch_size].
104 93	* **initial_h** (optional, heterogeneous) - **T**:
105 94	Optional initial value of the hidden. If not specified - assumed to
106 95	be 0. It has shape [num_directions, batch_size, hidden_size].
107 96	* **initial_c** (optional, heterogeneous) - **T**:
108 97	Optional initial value of the cell. If not specified - assumed to be
109 98	0. It has shape [num_directions, batch_size, hidden_size].
110 99	* **P** (optional, heterogeneous) - **T**:
111 100	The weight tensor for peepholes. Concatenation of P[iof] and
112 101	PB[iof] (if bidirectional) along dimension 0. It has shape
113 102	[num_directions, 3*hidde_size]. Optional: If not specified -
114 103	assumed to be 0.
115 104	**Outputs**
116 105	Between 0 and 3 outputs.
117 106	* **Y** (optional, heterogeneous) - **T**:
118 107	A tensor that concats all the intermediate output values of the
119 108	hidden. It has shape [seq_length, num_directions, batch_size,
120 109	hidden_size].
121 110	* **Y_h** (optional, heterogeneous) - **T**:
122 111	The last output value of the hidden. It has shape [num_directions,
123 112	batch_size, hidden_size].
124 113	* **Y_c** (optional, heterogeneous) - **T**:
125 114	The last output value of the cell. It has shape [num_directions,
126 115	batch_size, hidden_size].
127 116	**Type Constraints**
128 117	* **T** in (
129 118	tensor(double),
130 119	tensor(float),
131 120	tensor(float16)
132 121	):
133 122	Constrain input and output types to float tensors.
134 123	* **T1** in (
135 124	tensor(int32)
136 125	):
137 126	Constrain seq_lens to integer tensor.