# # 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 Peephole, With Projection, No Clipping model = Model() n_batch = 2 n_input = 5 # n_cell and n_output have the same size when there is no projection. n_cell = 20 n_output = 16 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", "{%d}" % (n_cell)) 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", "{%d,%d}" % (n_output, n_cell)) 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 * 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)) # TODO: need support for more than one 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: [ 0.021393683, 0.06124551, 0.046905167, -0.014657677, 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-0.13393489, 0.06534304, 0.003620307, 0.04490757, 0.05970546, 0.05197996, 0.02839995, 0.10434969, -0.013699693, -0.028353551, -0.07260381, 0.047201227, -0.024575593, -0.036445823, 0.07155557, 0.009672501, -0.02328883, 0.009533515, -0.03606021, -0.07421458, -0.028082801, -0.2678904, -0.13221288, 0.18419984, -0.13012612, -0.014588381, -0.035059117, -0.04824723, 0.07830115, -0.056184657, 0.03277091, 0.025466874, 0.14494097, -0.12522776, -0.098633975, -0.10766018, -0.08317623, 0.08594209, 0.07749552, 0.039474737, 0.1776665, -0.07409566, -0.0477268, 0.29323658, 0.10801441, 0.1154011, 0.013952499, 0.10739139, 0.10708251, -0.051456142, 0.0074137426, -0.10430189, 0.10034707, 0.045594677, 0.0635285, -0.0715442, -0.089667566, -0.10811871, 0.00026344223, 0.08298446, -0.009525053, 0.006585689, -0.24567553, -0.09450807, 0.09648481, 0.026996298, -0.06419476, -0.04752702, -0.11063944, -0.23441927, -0.17608605, -0.052156363, 0.067035615, 0.19271925, -0.0032889997, -0.043264326, 0.09663576, -0.057112187, -0.10100678, 0.0628376, 0.04447668, 0.017961001, -0.10094388, -0.10190601, 0.18335468, 0.10494553, -0.052095775, -0.0026118709, 0.10539724, -0.04383912, -0.042349473, 0.08438151, -0.1947263, 0.02251204, 0.11216432, -0.10307853, 0.17351969, -0.039091777, 0.08066188, -0.00561982, 0.12633002, 0.11335965, -0.0088127935, -0.019777594, 0.06864014, -0.059751723, 0.016233567, -0.06894641, -0.28651384, -0.004228674, 0.019708522, -0.16305895, -0.07468996, -0.0855457, 0.099339016, -0.07580735, -0.13775392, 0.08434318, 0.08330512, -0.12131499, 0.031935584, 0.09180414, -0.08876437, -0.08049874, 0.008753825, 0.03498998, 0.030215185, 0.03907079, 0.089751154, 0.029194152, -0.03337423, -0.019092513, 0.04331237, 0.04299654, -0.036394123, -0.12915532, 0.09793732, 0.07512415, -0.11319543, -0.032502122, 0.15661901, 0.07671967, -0.005491124, -0.19379048, -0.218606, 0.21448623, 0.017840758, 0.1416943, -0.07051762, 0.19488361, 0.02664691, -0.18104725, -0.09334311, 0.15026465, -0.15493552, -0.057762887, -0.11604192, -0.262013, -0.01391798, 0.012185008, 0.11156489, -0.07483202, 0.06693364, -0.26151478, 0.046425626, 0.036540434, -0.16435726, 0.17338543, -0.21401681, -0.11385144, -0.08283257, -0.069031075, 0.030635102, 0.010969227, 0.11109743, 0.010919218, 0.027526086, 0.13519906, 0.01891392, -0.046839405, -0.040167913, 0.017953383, -0.09700955, 0.0061885654, -0.07000971, 0.026893595, -0.038844477, 0.14543656], projection_bias: [], } # Batch0: 4 (input_sequence_size) * 5 (n_input) input0[input] = [0.073204, 0.296072, 0.743333, 0.069199, 0.045348] # Batch1: 4 (input_sequence_size) * 5 (n_input) input0[input].extend( [0.640394, 0.930399, 0.050782, 0.432485, 0.988078] ) input0[output_state_in] = [ -0.0166936, 0.0381209, 0.000889684, 0.0143363, -0.0328911, -0.0234288, 0.0333051, -0.012229, 0.0110322, -0.0457725, -0.000832209, -0.0202817, 0.0327257, 0.0121309, 0.0155969, 0.0312091, -0.0141913, 0.0322082, 0.00227024, 0.0260507, -0.0188721, -0.0296489, 0.0399134, -0.0160509, 0.011604, -0.0447318, -0.0150515, -0.0277406, 0.0316596, 0.0118233, 0.0214762, 0.0293641, ] input0[cell_state_in] = [ -0.154022, -0.124934, 0.0478463, 0.0607819, -0.218727, -0.111053, -0.103885, -0.00447221, 0.0554757, -0.0207068, 0.0595767, -0.116297, -0.249466, -0.0723206, 0.0794942, -0.0377107, 0.124532, 0.249952, 0.188641, 0.411865, -0.11012, -0.0694494, 0.103501, 0.0428427, -0.167345, -0.106061, -0.0775679, 0.00936161, 0.0105526, -0.0314523, 0.0243475, -0.132179, -0.258763, -0.0307266, 0.107047, -0.0115197, 0.0995485, 0.220027, 0.158355, 0.436369, ] output0 = { scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ], cell_state_out: [ -0.126572, -0.121882, 0.121569, 0.0489971, -0.240177, -0.124685, -0.122565, 0.0162748, 0.0317536, -0.0270355, 0.0418199, -0.179755, -0.327279, -0.0342741, 0.133831, -0.0238279, 0.122148, 0.269115, 0.185989, 0.525976, -0.167208, -0.109612, 0.0531226, 0.0695387, -0.248335, -0.134123, -0.108246, 0.00628498, 0.0492984, -0.0264919, 0.0698144, -0.0635602, -0.295363, -0.0760078, 0.102725, -0.0351708, 0.149804, 0.259131, 0.202573, 0.500664, ], output_state_out: [ -0.0213783, 0.0350169, 0.000324787, 0.0276012, -0.0263374, -0.0371449, 0.0446149, -0.0205474, 0.0103729, -0.0576349, -0.0150052, -0.0292043, 0.0376827, 0.0136115, 0.0243435, 0.0354492, -0.0204549, 0.0450315, -0.00117379, 0.0167673, -0.0375007, -0.0238314, 0.038784, -0.0174034, 0.0131743, -0.0506589, -0.00484469, -0.0240239, 0.0325789, 0.00790064, 0.0220157, 0.0333314, ], } # Batch0: 4 (input_sequence_size) * 16 (n_output) output0[output] = [ -0.0213783, 0.0350169, 0.000324794, 0.0276012, -0.0263374, -0.0371449, 0.0446149, -0.0205474, 0.0103729, -0.0576349, -0.0150052, -0.0292043, 0.0376827, 0.0136115, 0.0243435, 0.0354492] # Batch1: 4 (input_sequence_size) * 16 (n_output) output0[output].extend( [-0.0204549, 0.0450315, -0.00117378, 0.0167673, -0.0375007, -0.0238314, 0.038784, -0.0174034, 0.0131743, -0.0506589, -0.0048447, -0.0240239, 0.0325789, 0.00790065, 0.0220157, 0.0333314], ) Example((input0, output0))