summaryrefslogtreecommitdiff
path: root/runtimes/tests/neural_networks_test/specs/V1_0/lstm2_state2.mod.py
diff options
context:
space:
mode:
Diffstat (limited to 'runtimes/tests/neural_networks_test/specs/V1_0/lstm2_state2.mod.py')
-rw-r--r--runtimes/tests/neural_networks_test/specs/V1_0/lstm2_state2.mod.py142
1 files changed, 0 insertions, 142 deletions
diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/lstm2_state2.mod.py b/runtimes/tests/neural_networks_test/specs/V1_0/lstm2_state2.mod.py
deleted file mode 100644
index 027bedcb4..000000000
--- a/runtimes/tests/neural_networks_test/specs/V1_0/lstm2_state2.mod.py
+++ /dev/null
@@ -1,142 +0,0 @@
-#
-# Copyright (C) 2017 The Android Open Source Project
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-# LSTM Test, With Cifg, With Peephole, No Projection, No Clipping.
-
-model = Model()
-
-n_batch = 1
-n_input = 2
-# n_cell and n_output have the same size when there is no projection.
-n_cell = 4
-n_output = 4
-
-input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_input))
-
-input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
-input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
-input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
-input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
-
-recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
-recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
-recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
-recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
-
-cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32", "{0}")
-cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell))
-cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell))
-
-input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
-forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
-cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
-output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
-
-projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{0,0}")
-projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}")
-
-output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
-cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
-
-activation_param = Int32Scalar("activation_param", 4) # Tanh
-cell_clip_param = Float32Scalar("cell_clip_param", 0.)
-proj_clip_param = Float32Scalar("proj_clip_param", 0.)
-
-scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell * 3))
-output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
-cell_state_out = IgnoredOutput("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
-output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
-
-model = model.Operation("LSTM",
- input,
-
- input_to_input_weights,
- input_to_forget_weights,
- input_to_cell_weights,
- input_to_output_weights,
-
- recurrent_to_input_weights,
- recurrent_to_forget_weights,
- recurrent_to_cell_weights,
- recurrent_to_output_weights,
-
- cell_to_input_weights,
- cell_to_forget_weights,
- cell_to_output_weights,
-
- input_gate_bias,
- forget_gate_bias,
- cell_gate_bias,
- output_gate_bias,
-
- projection_weights,
- projection_bias,
-
- output_state_in,
- cell_state_in,
-
- activation_param,
- cell_clip_param,
- proj_clip_param
-).To([scratch_buffer, output_state_out, cell_state_out, output])
-
-input0 = {input_to_input_weights:[],
- input_to_cell_weights: [-0.49770179, -0.27711356, -0.09624726, 0.05100781, 0.04717243, 0.48944736, -0.38535351, -0.17212132],
- input_to_forget_weights: [-0.55291498, -0.42866567, 0.13056988, -0.3633365, -0.22755712, 0.28253698, 0.24407166, 0.33826375],
- input_to_output_weights: [0.10725588, -0.02335852, -0.55932593, -0.09426838, -0.44257352, 0.54939759, 0.01533556, 0.42751634],
-
- input_gate_bias: [],
- forget_gate_bias: [1.,1.,1.,1.],
- cell_gate_bias: [0.,0.,0.,0.],
- output_gate_bias: [0.,0.,0.,0.],
-
- recurrent_to_input_weights: [],
- recurrent_to_cell_weights: [
- 0.54066205, -0.32668582, -0.43562764, -0.56094903, 0.42957711,
- 0.01841056, -0.32764608, -0.33027974, -0.10826075, 0.20675004,
- 0.19069612, -0.03026325, -0.54532051, 0.33003211, 0.44901288,
- 0.21193194],
-
- recurrent_to_forget_weights: [
- -0.13832897, -0.0515101, -0.2359007, -0.16661474, -0.14340827,
- 0.36986142, 0.23414481, 0.55899, 0.10798943, -0.41174671, 0.17751795,
- -0.34484994, -0.35874045, -0.11352962, 0.27268326, 0.54058349],
-
- recurrent_to_output_weights: [
- 0.41613156, 0.42610586, -0.16495961, -0.5663873, 0.30579174, -0.05115908,
- -0.33941799, 0.23364776, 0.11178309, 0.09481031, -0.26424935, 0.46261835,
- 0.50248802, 0.26114327, -0.43736315, 0.33149987],
-
- cell_to_input_weights: [],
- cell_to_forget_weights: [0.47485286, -0.51955009, -0.24458408, 0.31544167],
- cell_to_output_weights: [-0.17135078, 0.82760304, 0.85573703, -0.77109635],
-
- projection_weights: [],
- projection_bias: [],
-}
-
-output0 = {
- scratch_buffer: [ 0 for x in range(n_batch * n_cell * 3) ],
- cell_state_out: [ 0 for x in range(n_batch * n_cell) ],
- output_state_out: [ 0 for x in range(n_batch * n_output) ],
-}
-
-input0[input] = [1., 1.]
-input0[output_state_in] = [-0.423122, -0.0121822, 0.24201, -0.0812458]
-input0[cell_state_in] = [-0.978419, -0.139203, 0.338163, -0.0983904]
-output0[output] = [-0.358325, -0.04621704, 0.21641694, -0.06471302]
-
-Example((input0, output0))
generated by cgit v1.2.3 (git 2.25.1) at 2024-06-04 16:35:53 +0000