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Diffstat (limited to 'runtimes/tests/neural_networks_test/specs/V1_0/lstm2_state.mod.py')
-rw-r--r-- | runtimes/tests/neural_networks_test/specs/V1_0/lstm2_state.mod.py | 141 |
1 files changed, 141 insertions, 0 deletions
diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/lstm2_state.mod.py b/runtimes/tests/neural_networks_test/specs/V1_0/lstm2_state.mod.py new file mode 100644 index 000000000..7543e8d64 --- /dev/null +++ b/runtimes/tests/neural_networks_test/specs/V1_0/lstm2_state.mod.py @@ -0,0 +1,141 @@ +# +# 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 = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) +cell_state_out = Output("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: [ -0.978419, -0.139203, 0.338163, -0.0983904 ], + output_state_out: [ -0.423122, -0.0121822, 0.24201, -0.0812458 ], +} + +input0[input] = [3., 4.] +input0[output_state_in] = [-0.364445, -0.00352185, 0.128866, -0.0516365] +input0[cell_state_in] = [-0.760444, -0.0180416, 0.182264, -0.0649371] +output0[output] = [-0.42312205, -0.01218222, 0.24201041, -0.08124574] +Example((input0, output0)) |