# # 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 = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) cell_state_out = IgnoredOutput("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 for x in range(n_batch * n_cell) ], output_state_out: [ 0 for x in range(n_batch * n_output) ], } input0[input] = [1., 1.] input0[output_state_in] = [-0.423122, -0.0121822, 0.24201, -0.0812458] input0[cell_state_in] = [-0.978419, -0.139203, 0.338163, -0.0983904] output0[output] = [-0.358325, -0.04621704, 0.21641694, -0.06471302] Example((input0, output0))