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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
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+++ b/runtimes/tests/neural_networks_test/specs/V1_0/lstm2_state.mod.py
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+#
+# 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))