143 lines
6.5 KiB
Python
143 lines
6.5 KiB
Python
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#
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# Copyright (C) 2017 The Android Open Source Project
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# LSTM Test, With Cifg, With Peephole, No Projection, No Clipping.
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model = Model()
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n_batch = 1
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n_input = 2
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# n_cell and n_output have the same size when there is no projection.
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n_cell = 4
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n_output = 4
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input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_input))
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input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
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input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
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input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
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input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
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recurrent_to_input_weights = Input("recurrent_to_input_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
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recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
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recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
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recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
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cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32", "{0}")
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cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell))
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cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell))
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input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
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forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
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cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
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output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
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projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{0,0}")
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projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}")
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output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
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cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
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activation_param = Int32Scalar("activation_param", 4) # Tanh
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cell_clip_param = Float32Scalar("cell_clip_param", 0.)
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proj_clip_param = Float32Scalar("proj_clip_param", 0.)
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scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell * 3))
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output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
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cell_state_out = Output("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
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output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
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model = model.Operation("LSTM",
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input,
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input_to_input_weights,
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input_to_forget_weights,
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input_to_cell_weights,
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input_to_output_weights,
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recurrent_to_input_weights,
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recurrent_to_forget_weights,
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recurrent_to_cell_weights,
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recurrent_to_output_weights,
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cell_to_input_weights,
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cell_to_forget_weights,
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cell_to_output_weights,
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input_gate_bias,
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forget_gate_bias,
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cell_gate_bias,
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output_gate_bias,
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projection_weights,
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projection_bias,
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output_state_in,
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cell_state_in,
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activation_param,
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cell_clip_param,
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proj_clip_param
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).To([scratch_buffer, output_state_out, cell_state_out, output])
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input0 = {input_to_input_weights:[],
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input_to_cell_weights: [-0.49770179, -0.27711356, -0.09624726, 0.05100781, 0.04717243, 0.48944736, -0.38535351, -0.17212132],
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input_to_forget_weights: [-0.55291498, -0.42866567, 0.13056988, -0.3633365, -0.22755712, 0.28253698, 0.24407166, 0.33826375],
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input_to_output_weights: [0.10725588, -0.02335852, -0.55932593, -0.09426838, -0.44257352, 0.54939759, 0.01533556, 0.42751634],
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input_gate_bias: [],
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forget_gate_bias: [1.,1.,1.,1.],
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cell_gate_bias: [0.,0.,0.,0.],
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output_gate_bias: [0.,0.,0.,0.],
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recurrent_to_input_weights: [],
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recurrent_to_cell_weights: [
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0.54066205, -0.32668582, -0.43562764, -0.56094903, 0.42957711,
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0.01841056, -0.32764608, -0.33027974, -0.10826075, 0.20675004,
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0.19069612, -0.03026325, -0.54532051, 0.33003211, 0.44901288,
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0.21193194],
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recurrent_to_forget_weights: [
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-0.13832897, -0.0515101, -0.2359007, -0.16661474, -0.14340827,
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0.36986142, 0.23414481, 0.55899, 0.10798943, -0.41174671, 0.17751795,
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-0.34484994, -0.35874045, -0.11352962, 0.27268326, 0.54058349],
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recurrent_to_output_weights: [
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0.41613156, 0.42610586, -0.16495961, -0.5663873, 0.30579174, -0.05115908,
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-0.33941799, 0.23364776, 0.11178309, 0.09481031, -0.26424935, 0.46261835,
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0.50248802, 0.26114327, -0.43736315, 0.33149987],
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cell_to_input_weights: [],
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cell_to_forget_weights: [0.47485286, -0.51955009, -0.24458408, 0.31544167],
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cell_to_output_weights: [-0.17135078, 0.82760304, 0.85573703, -0.77109635],
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projection_weights: [],
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projection_bias: [],
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}
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output0 = {
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scratch_buffer: [ 0 for x in range(n_batch * n_cell * 3) ],
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cell_state_out: [ -0.760444, -0.0180416, 0.182264, -0.0649371 ],
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output_state_out: [ -0.364445, -0.00352185, 0.128866, -0.0516365 ],
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}
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input0[input] = [2., 3.]
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input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
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input0[cell_state_in] = [ 0 for _ in range(n_batch * n_cell) ]
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output0[output] = [-0.36444446, -0.00352185, 0.12886585, -0.05163646]
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Example((input0, output0))
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