# # 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}") 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 = 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}" % (n_batch, (n_cell * 4))) 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]) # 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.], } test_input = [3., 4.] output_state = [-0.0297319, 0.122947, 0.208851, -0.153588] cell_state = [-0.145439, 0.157475, 0.293663, -0.277353,] golden_output = [-0.03716109, 0.12507336, 0.41193449, -0.20860538] output0 = { scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ], cell_state_out: [ -0.287121, 0.148115, 0.556837, -0.388276 ], output_state_out: [ -0.0371611, 0.125073, 0.411934, -0.208605 ], output: golden_output } input0[input] = test_input input0[output_state_in] = output_state input0[cell_state_in] = cell_state Example((input0, output0))