1 # 2 # Copyright (C) 2017 The Android Open Source Project 3 # 4 # Licensed under the Apache License, Version 2.0 (the "License"); 5 # you may not use this file except in compliance with the License. 6 # You may obtain a copy of the License at 7 # 8 # http://www.apache.org/licenses/LICENSE-2.0 9 # 10 # Unless required by applicable law or agreed to in writing, software 11 # distributed under the License is distributed on an "AS IS" BASIS, 12 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 # See the License for the specific language governing permissions and 14 # limitations under the License. 15 # 16 17 batches = 2 18 units = 16 19 input_size = 8 20 21 model = Model() 22 23 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size)) 24 weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size)) 25 recurrent_weights = Input("recurrent_weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, units)) 26 bias = Input("bias", "TENSOR_FLOAT32", "{%d}" % (units)) 27 hidden_state_in = Input("hidden_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units)) 28 29 activation_param = Int32Scalar("activation_param", 1) # Relu 30 31 hidden_state_out = IgnoredOutput("hidden_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units)) 32 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units)) 33 34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in, 35 activation_param).To([hidden_state_out, output]) 36 37 input0 = { 38 weights: [ 39 0.461459, 0.153381, 0.529743, -0.00371218, 0.676267, -0.211346, 40 0.317493, 0.969689, -0.343251, 0.186423, 0.398151, 0.152399, 41 0.448504, 0.317662, 0.523556, -0.323514, 0.480877, 0.333113, 42 -0.757714, -0.674487, -0.643585, 0.217766, -0.0251462, 0.79512, 43 -0.595574, -0.422444, 0.371572, -0.452178, -0.556069, -0.482188, 44 -0.685456, -0.727851, 0.841829, 0.551535, -0.232336, 0.729158, 45 -0.00294906, -0.69754, 0.766073, -0.178424, 0.369513, -0.423241, 46 0.548547, -0.0152023, -0.757482, -0.85491, 0.251331, -0.989183, 47 0.306261, -0.340716, 0.886103, -0.0726757, -0.723523, -0.784303, 48 0.0354295, 0.566564, -0.485469, -0.620498, 0.832546, 0.697884, 49 -0.279115, 0.294415, -0.584313, 0.548772, 0.0648819, 0.968726, 50 0.723834, -0.0080452, -0.350386, -0.272803, 0.115121, -0.412644, 51 -0.824713, -0.992843, -0.592904, -0.417893, 0.863791, -0.423461, 52 -0.147601, -0.770664, -0.479006, 0.654782, 0.587314, -0.639158, 53 0.816969, -0.337228, 0.659878, 0.73107, 0.754768, -0.337042, 54 0.0960841, 0.368357, 0.244191, -0.817703, -0.211223, 0.442012, 55 0.37225, -0.623598, -0.405423, 0.455101, 0.673656, -0.145345, 56 -0.511346, -0.901675, -0.81252, -0.127006, 0.809865, -0.721884, 57 0.636255, 0.868989, -0.347973, -0.10179, -0.777449, 0.917274, 58 0.819286, 0.206218, -0.00785118, 0.167141, 0.45872, 0.972934, 59 -0.276798, 0.837861, 0.747958, -0.0151566, -0.330057, -0.469077, 60 0.277308, 0.415818 61 ], 62 recurrent_weights: [ 63 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 64 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 65 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 66 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 67 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 68 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 69 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 70 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 71 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 72 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 73 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 74 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 75 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 76 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 77 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 78 0.1 79 ], 80 bias: [ 81 0.065691948, -0.69055247, 0.1107955, -0.97084129, -0.23957068, 82 -0.23566568, -0.389184, 0.47481549, -0.4791103, 0.29931796, 83 0.10463274, 0.83918178, 0.37197268, 0.61957061, 0.3956964, 84 -0.37609905 85 ], 86 } 87 88 input0[input] = [ 89 -0.69424844, -0.93421471, -0.87287879, 0.37144363, 90 -0.62476718, 0.23791671, 0.40060222, 0.1356622, 91 -0.69424844, -0.93421471, -0.87287879, 0.37144363, 92 -0.62476718, 0.23791671, 0.40060222, 0.1356622, 93 ] 94 input0[hidden_state_in] = [ 95 0.496726, 0, 0.965996, 0, 96 0.0584256, 0, 0, 0.12315, 97 0, 0, 0.612267, 0.456601, 98 0, 0.52286, 1.16099, 0.0291233, 99 0.496726, 0, 0.965996, 0, 100 0.0584256, 0, 0, 0.12315, 101 0, 0, 0.612267, 0.456601, 102 0, 0.52286, 1.16099, 0.0291233, 103 ] 104 output0 = { 105 hidden_state_out : [ 106 0, 0, 0.524902, 0, 107 0, 0, 0, 1.02116, 108 0, 1.35762, 0, 0.356909, 109 0.436415, 0.0355731, 0, 0, 110 0, 0, 0.524902, 0, 111 0, 0, 0, 1.02116, 112 0, 1.35762, 0, 0.356909, 113 0.436415, 0.0355731, 0, 0, 114 ] 115 } 116 output0[output] = [ 117 0, 0, 0.524901, 0, 0, 0, 118 0, 1.02116, 0, 1.35762, 0, 0.356909, 119 0.436415, 0.0355727, 0, 0, 120 121 0, 0, 0.524901, 0, 0, 0, 122 0, 1.02116, 0, 1.35762, 0, 0.356909, 123 0.436415, 0.0355727, 0, 0, 124 ] 125 126 Example((input0, output0)) 127
