diff options
Diffstat (limited to 'runtimes/tests/neural_networks_test/specs/V1_0/svdf.mod.py')
-rw-r--r-- | runtimes/tests/neural_networks_test/specs/V1_0/svdf.mod.py | 138 |
1 files changed, 0 insertions, 138 deletions
diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/svdf.mod.py b/runtimes/tests/neural_networks_test/specs/V1_0/svdf.mod.py deleted file mode 100644 index 4f3e42c20..000000000 --- a/runtimes/tests/neural_networks_test/specs/V1_0/svdf.mod.py +++ /dev/null @@ -1,138 +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. -# - -batches = 2 -features = 4 -rank = 1 -units = int(features / rank) -input_size = 3 -memory_size = 10 - -model = Model() - -input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size)) -weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size)) -weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size)) -bias = Input("bias", "TENSOR_FLOAT32", "{%d}" % (units)) -state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features)) -rank_param = Int32Scalar("rank_param", rank) -activation_param = Int32Scalar("activation_param", 0) -state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features)) -output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units)) - -model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in, - rank_param, activation_param).To([state_out, output]) - -input0 = { - input: [], - weights_feature: [ - -0.31930989, -0.36118156, 0.0079667, 0.37613347, - 0.22197971, 0.12416199, 0.27901134, 0.27557442, - 0.3905206, -0.36137494, -0.06634006, -0.10640851 - ], - weights_time: [ - -0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156, - 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199, - - 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518, - -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296, - - -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236, - 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846, - - -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166, - -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657 - ], - bias: [], - state_in: [0 for _ in range(batches * memory_size * features)], -} - -test_inputs = [ - 0.12609188, -0.46347019, -0.89598465, - 0.12609188, -0.46347019, -0.89598465, - - 0.14278367, -1.64410412, -0.75222826, - 0.14278367, -1.64410412, -0.75222826, - - 0.49837467, 0.19278903, 0.26584083, - 0.49837467, 0.19278903, 0.26584083, - - -0.11186574, 0.13164264, -0.05349274, - -0.11186574, 0.13164264, -0.05349274, - - -0.68892461, 0.37783599, 0.18263303, - -0.68892461, 0.37783599, 0.18263303, - - -0.81299269, -0.86831826, 1.43940818, - -0.81299269, -0.86831826, 1.43940818, - - -1.45006323, -0.82251364, -1.69082689, - -1.45006323, -0.82251364, -1.69082689, - - 0.03966608, -0.24936394, -0.77526885, - 0.03966608, -0.24936394, -0.77526885, - - 0.11771342, -0.23761693, -0.65898693, - 0.11771342, -0.23761693, -0.65898693, - - -0.89477462, 1.67204106, -0.53235275, - -0.89477462, 1.67204106, -0.53235275 -] - -golden_outputs = [ - 0.014899, -0.0517661, -0.143725, -0.00271883, - 0.014899, -0.0517661, -0.143725, -0.00271883, - - 0.068281, -0.162217, -0.152268, 0.00323521, - 0.068281, -0.162217, -0.152268, 0.00323521, - - -0.0317821, -0.0333089, 0.0609602, 0.0333759, - -0.0317821, -0.0333089, 0.0609602, 0.0333759, - - -0.00623099, -0.077701, -0.391193, -0.0136691, - -0.00623099, -0.077701, -0.391193, -0.0136691, - - 0.201551, -0.164607, -0.179462, -0.0592739, - 0.201551, -0.164607, -0.179462, -0.0592739, - - 0.0886511, -0.0875401, -0.269283, 0.0281379, - 0.0886511, -0.0875401, -0.269283, 0.0281379, - - -0.201174, -0.586145, -0.628624, -0.0330412, - -0.201174, -0.586145, -0.628624, -0.0330412, - - -0.0839096, -0.299329, 0.108746, 0.109808, - -0.0839096, -0.299329, 0.108746, 0.109808, - - 0.419114, -0.237824, -0.422627, 0.175115, - 0.419114, -0.237824, -0.422627, 0.175115, - - 0.36726, -0.522303, -0.456502, -0.175475, - 0.36726, -0.522303, -0.456502, -0.175475 -] - -output0 = {state_out: [0 for _ in range(batches * memory_size * features)], - output: []} - -# TODO: enable more data points after fixing the reference issue -for i in range(1): - batch_start = i * input_size * batches - batch_end = batch_start + input_size * batches - input0[input] = test_inputs[batch_start:batch_end] - golden_start = i * units * batches - golden_end = golden_start + units * batches - output0[output] = golden_outputs[golden_start:golden_end] - Example((input0, output0)) |