| /frameworks/ml/nn/runtime/test/specs/V1_2/ |
| D | quantized_lstm.mod.py | 23 n_output = n_cell variable
32 [n_output, n_input], weights_scale, weights_zero_point)
39 [n_output, n_output], weights_scale, weights_zero_point)
45 BiasType = ("TENSOR_INT32", [n_output], weights_scale / 128., 0)
52 OutputType = ("TENSOR_QUANT8_ASYMM", (n_batch, n_output), 1 / 128, 128)
110 n_output = n_cell variable
119 [n_output, n_input], weights_scale, weights_zero_point)
130 [n_output, n_output], weights_scale, weights_zero_point)
140 BiasType = ("TENSOR_INT32", [n_output], weights_scale / 128., 0)
151 OutputType = ("TENSOR_QUANT8_ASYMM", (n_batch, n_output), 1 / 128, 128)
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| D | layer_norm_lstm.mod.py | 25 n_output = 3 variable
40 "{%d, %d}" % (n_cell, n_output))
43 "{%d, %d}" % (n_cell, n_output))
45 "{%d, %d}" % (n_cell, n_output))
48 "{%d, %d}" % (n_cell, n_output))
65 "{%d,%d}" % (n_output, n_cell))
69 "{%d, %d}" % (n_batch, n_output))
89 "{%d, %d}" % (n_batch, n_output))
92 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
197 n_output = 3 variable
[all …]
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| D | lstm3_float16.mod.py | 25 n_output = 16 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
48 projection_weights = Input("projection_weights", "TENSOR_FLOAT16", "{%d,%d}" % (n_output, n_cell))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = Output("output_state_out", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
621 input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
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| D | unidirectional_sequence_lstm_batch_major_peephole_projection_bias.mod.py | 28 n_output = 16 variable
43 "{%d, %d}" % (n_cell, n_output))
46 "{%d, %d}" % (n_cell, n_output))
48 "{%d, %d}" % (n_cell, n_output))
51 "{%d, %d}" % (n_cell, n_output))
68 "{%d,%d}" % (n_output, n_cell))
69 projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{%d}" % n_output)
72 "{%d, %d}" % (n_batch, n_output))
86 output = Output("output", "TENSOR_FLOAT32", "{%d, %d, %d}" % (n_batch, max_time, n_output))
702 input0[output_state_in] = [0 for _ in range(n_batch * n_output)]
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| D | lstm3_state3_float16.mod.py | 25 n_output = 16 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
48 projection_weights = Input("projection_weights", "TENSOR_FLOAT16", "{%d,%d}" % (n_output, n_cell))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
59 …t_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
645 output_state_out: [ 0 for x in range(n_batch * n_output) ],
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| D | lstm2_state2_float16.mod.py | 25 n_output = 4 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
59 …t_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
134 output_state_out: [ 0 for x in range(n_batch * n_output) ],
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| D | lstm2_float16.mod.py | 25 n_output = 4 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = Output("output_state_out", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
138 input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
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| D | lstm3_state2_float16.mod.py | 25 n_output = 16 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
48 projection_weights = Input("projection_weights", "TENSOR_FLOAT16", "{%d,%d}" % (n_output, n_cell))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = Output("output_state_out", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
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| D | lstm_state2_float16.mod.py | 25 n_output = 4 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT16", "{%d, %d}" % (n_cell, n_output))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
59 …t_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT16", "{%d, %d}" % (n_batch, n_output))
142 output_state_out: [ 0 for x in range(n_batch * n_output) ],
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| /frameworks/ml/nn/runtime/test/specs/V1_0/ |
| D | lstm3.mod.py | 25 n_output = 16 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
48 projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
621 input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
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| D | lstm3_state3.mod.py | 25 n_output = 16 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
48 projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 …t_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
645 output_state_out: [ 0 for x in range(n_batch * n_output) ],
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| D | lstm_state2.mod.py | 25 n_output = 4 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 …t_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
142 output_state_out: [ 0 for x in range(n_batch * n_output) ],
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| D | lstm3_state.mod.py | 25 n_output = 16 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
48 projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
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| D | lstm2.mod.py | 25 n_output = 4 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
138 input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
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| D | lstm2_state2.mod.py | 25 n_output = 4 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 …t_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
134 output_state_out: [ 0 for x in range(n_batch * n_output) ],
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| D | lstm3_state2.mod.py | 25 n_output = 16 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
48 projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
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| /frameworks/ml/nn/runtime/test/specs/V1_3/ |
| D | bidirectional_sequence_lstm.mod.py | 26 n_output, argument
104 "{{{}, {}}}".format(n_cell, n_output))
107 "{{{}, {}}}".format(n_cell, n_output))
110 "{{{}, {}}}".format(n_cell, n_output))
113 "{{{}, {}}}".format(n_cell, n_output))
149 "{{{}, {}}}".format(n_cell, n_output))
152 "{{{}, {}}}".format(n_cell, n_output))
155 "{{{}, {}}}".format(n_cell, n_output))
158 "{{{}, {}}}".format(n_cell, n_output))
181 "{{{}, {}}}".format(n_batch, n_output))
[all …]
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| D | unidirectional_sequence_lstm_layer_norm_cifg_peephole_state_output.mod.py | 28 n_output = 3 variable
46 "{%d, %d}" % (n_cell, n_output))
48 "{%d, %d}" % (n_cell, n_output))
51 "{%d, %d}" % (n_cell, n_output))
67 "{%d,%d}" % (n_output, n_cell))
71 "{%d, %d}" % (n_batch, n_output))
90 "{%d, %d, %d}" % (max_time, n_batch, n_output))
92 "{%d, %d}" % (n_batch, n_output))
181 golden_output[(max_time - 1) * (n_batch * n_output):],
189 input0[output_state_in] = [0 for _ in range(n_batch * n_output)]
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| D | bidirectional_sequence_lstm_state_output.mod.py | 23 n_output = 4 variable
38 "fw_recurrent_to_input_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_output))
40 "fw_recurrent_to_forget_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_output))
42 "fw_recurrent_to_cell_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_output))
44 "fw_recurrent_to_output_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_output))
63 "fw_projection_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_output, n_cell))
65 "fw_projection_bias", "TENSOR_FLOAT32", "{{{}}}".format(n_output))
77 "bw_recurrent_to_input_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_output))
79 "bw_recurrent_to_forget_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_output))
81 "bw_recurrent_to_cell_weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(n_cell, n_output))
[all …]
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| /frameworks/ml/nn/runtime/test/specs/V1_1/ |
| D | lstm3_state3_relaxed.mod.py | 25 n_output = 16 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
48 projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 …t_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
646 output_state_out: [ 0 for x in range(n_batch * n_output) ],
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| D | lstm3_relaxed.mod.py | 25 n_output = 16 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
48 projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
622 input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
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| D | lstm2_state2_relaxed.mod.py | 25 n_output = 4 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 …t_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
135 output_state_out: [ 0 for x in range(n_batch * n_output) ],
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| D | lstm_state2_relaxed.mod.py | 25 n_output = 4 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 …t_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
143 output_state_out: [ 0 for x in range(n_batch * n_output) ],
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| D | lstm2_relaxed.mod.py | 25 n_output = 4 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
139 input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
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| D | lstm3_state_relaxed.mod.py | 25 n_output = 16 variable
34 …t_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 …t_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 …ell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 …t_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
48 projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
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