| # |
| # 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: No Cifg, No Peephole, No Projection, and 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", "{0}") |
| cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{0}") |
| |
| 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}") |
| |
| activation_param = Input("activation_param", "TENSOR_INT32", "{1}") |
| cell_clip_param = Input("cell_clip_param", "TENSOR_FLOAT32", "{1}") |
| proj_clip_param = Input("proj_clip_param", "TENSOR_FLOAT32", "{1}") |
| |
| scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d, %d}" % (n_batch, n_cell, 4)) |
| output_state = IgnoredOutput("output_state", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) |
| cell_state = IgnoredOutput("cell_state", "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, |
| |
| activation_param, |
| cell_clip_param, |
| proj_clip_param |
| ).To([scratch_buffer, output_state, cell_state, output]) |
| |
| # Example 1. Input in operand 0, |
| input0 = {input_to_input_weights: [-0.45018822, -0.02338299, -0.0870589, -0.34550029, 0.04266912, -0.15680569, -0.34856534, 0.43890524], |
| input_to_forget_weights: [0.09701663, 0.20334584, -0.50592935, -0.31343272, -0.40032279, 0.44781327, 0.01387155, -0.35593212], |
| input_to_cell_weights: [-0.50013041, 0.1370284, 0.11810488, 0.2013163, -0.20583314, 0.44344562, 0.22077113, -0.29909778], |
| input_to_output_weights: [-0.25065863, -0.28290087, 0.04613829, 0.40525138, 0.44272184, 0.03897077, -0.1556896, 0.19487578], |
| |
| input_gate_bias: [0.,0.,0.,0.], |
| 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: [ |
| -0.0063535, -0.2042388, 0.31454784, -0.35746509, 0.28902304, 0.08183324, |
| -0.16555229, 0.02286911, -0.13566875, 0.03034258, 0.48091322, |
| -0.12528998, 0.24077177, -0.51332325, -0.33502164, 0.10629296], |
| |
| recurrent_to_cell_weights: [ |
| -0.3407414, 0.24443203, -0.2078532, 0.26320225, 0.05695659, -0.00123841, |
| -0.4744786, -0.35869038, -0.06418842, -0.13502428, -0.501764, 0.22830659, |
| -0.46367589, 0.26016325, -0.03894562, -0.16368064], |
| |
| recurrent_to_forget_weights: [ |
| -0.48684245, -0.06655136, 0.42224967, 0.2112639, 0.27654213, 0.20864892, |
| -0.07646349, 0.45877004, 0.00141793, -0.14609534, 0.36447752, 0.09196436, |
| 0.28053468, 0.01560611, -0.20127171, -0.01140004], |
| |
| recurrent_to_output_weights: [ |
| 0.43385774, -0.17194885, 0.2718237, 0.09215671, 0.24107647, -0.39835793, |
| 0.18212086, 0.01301402, 0.48572797, -0.50656658, 0.20047462, -0.20607421, |
| -0.51818722, -0.15390486, 0.0468148, 0.39922136], |
| |
| cell_to_input_weights: [], |
| cell_to_forget_weights: [], |
| cell_to_output_weights: [], |
| |
| projection_weights: [], |
| projection_bias: [], |
| |
| activation_param: [4], # Tanh |
| cell_clip_param: [0.], |
| proj_clip_param: [0.], |
| } |
| |
| # Instantiate examples |
| # TODO: Add more examples after fixing the reference issue |
| test_inputs = [ |
| [2., 3.], |
| # [3., 4.],[1., 1.] |
| ] |
| golden_outputs = [ |
| [-0.02973187, 0.1229473, 0.20885126, -0.15358765,], |
| # [-0.03716109, 0.12507336, 0.41193449, -0.20860538], |
| # [-0.15053082, 0.09120187, 0.24278517, -0.12222792] |
| ] |
| |
| for (input_tensor, output_tensor) in zip(test_inputs, golden_outputs): |
| output0 = { |
| scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ], |
| cell_state: [ 0 for x in range(n_batch * n_cell) ], |
| output_state: [ 0 for x in range(n_batch * n_output) ], |
| output: output_tensor |
| } |
| input0[input] = input_tensor |
| Example((input0, output0)) |