| # pylint:skip-file |
| import sys |
| sys.path.insert(0, "../../python") |
| import mxnet as mx |
| import numpy as np |
| from collections import namedtuple |
| import time |
| import math |
| LSTMState = namedtuple("LSTMState", ["c", "h"]) |
| LSTMParam = namedtuple("LSTMParam", ["i2h_weight", "i2h_bias", |
| "h2h_weight", "h2h_bias"]) |
| LSTMModel = namedtuple("LSTMModel", ["rnn_exec", "symbol", |
| "init_states", "last_states", |
| "seq_data", "seq_labels", "seq_outputs", |
| "param_blocks"]) |
| |
| def lstm(num_hidden, indata, prev_state, param, seqidx, layeridx): |
| """LSTM Cell symbol""" |
| i2h = mx.sym.FullyConnected(data=indata, |
| weight=param.i2h_weight, |
| bias=param.i2h_bias, |
| num_hidden=num_hidden * 4, |
| name="t%d_l%d_i2h" % (seqidx, layeridx)) |
| h2h = mx.sym.FullyConnected(data=prev_state.h, |
| weight=param.h2h_weight, |
| bias=param.h2h_bias, |
| num_hidden=num_hidden * 4, |
| name="t%d_l%d_h2h" % (seqidx, layeridx)) |
| gates = i2h + h2h |
| slice_gates = mx.sym.SliceChannel(gates, num_outputs=4, |
| name="t%d_l%d_slice" % (seqidx, layeridx)) |
| in_gate = mx.sym.Activation(slice_gates[0], act_type="sigmoid") |
| in_transform = mx.sym.Activation(slice_gates[1], act_type="tanh") |
| forget_gate = mx.sym.Activation(slice_gates[2], act_type="sigmoid") |
| out_gate = mx.sym.Activation(slice_gates[3], act_type="sigmoid") |
| next_c = (forget_gate * prev_state.c) + (in_gate * in_transform) |
| next_h = out_gate * mx.sym.Activation(next_c, act_type="tanh") |
| return LSTMState(c=next_c, h=next_h) |
| |
| |
| def lstm_unroll(num_lstm_layer, seq_len, |
| num_hidden, num_label): |
| param_cells = [] |
| last_states = [] |
| for i in range(num_lstm_layer): |
| param_cells.append(LSTMParam(i2h_weight=mx.sym.Variable("l%d_i2h_weight" % i), |
| i2h_bias=mx.sym.Variable("l%d_i2h_bias" % i), |
| h2h_weight=mx.sym.Variable("l%d_h2h_weight" % i), |
| h2h_bias=mx.sym.Variable("l%d_h2h_bias" % i))) |
| state = LSTMState(c=mx.sym.Variable("l%d_init_c" % i), |
| h=mx.sym.Variable("l%d_init_h" % i)) |
| last_states.append(state) |
| assert(len(last_states) == num_lstm_layer) |
| |
| # embeding layer |
| data = mx.sym.Variable('data') |
| label = mx.sym.Variable('label') |
| wordvec = mx.sym.SliceChannel(data=data, num_outputs=seq_len, squeeze_axis=1) |
| |
| hidden_all = [] |
| for seqidx in range(seq_len): |
| hidden = wordvec[seqidx] |
| for i in range(num_lstm_layer): |
| next_state = lstm(num_hidden, indata=hidden, |
| prev_state=last_states[i], |
| param=param_cells[i], |
| seqidx=seqidx, layeridx=i) |
| hidden = next_state.h |
| last_states[i] = next_state |
| hidden_all.append(hidden) |
| |
| hidden_concat = mx.sym.Concat(*hidden_all, dim=0) |
| pred = mx.sym.FullyConnected(data=hidden_concat, num_hidden=11) |
| |
| label = mx.sym.Reshape(data=label, shape=(-1,)) |
| label = mx.sym.Cast(data = label, dtype = 'int32') |
| sm = mx.sym.WarpCTC(data=pred, label=label, label_length = num_label, input_length = seq_len) |
| return sm |
| |
| |
| def lstm_inference_symbol(num_lstm_layer, seq_len, num_hidden, num_label): |
| param_cells = [] |
| last_states = [] |
| for i in range(num_lstm_layer): |
| param_cells.append(LSTMParam(i2h_weight=mx.sym.Variable("l%d_i2h_weight" % i), |
| i2h_bias=mx.sym.Variable("l%d_i2h_bias" % i), |
| h2h_weight=mx.sym.Variable("l%d_h2h_weight" % i), |
| h2h_bias=mx.sym.Variable("l%d_h2h_bias" % i))) |
| state = LSTMState(c=mx.sym.Variable("l%d_init_c" % i), |
| h=mx.sym.Variable("l%d_init_h" % i)) |
| last_states.append(state) |
| assert (len(last_states) == num_lstm_layer) |
| |
| # embeding layer |
| data = mx.sym.Variable('data') |
| wordvec = mx.sym.SliceChannel(data=data, num_outputs=seq_len, squeeze_axis=1) |
| |
| hidden_all = [] |
| for seqidx in range(seq_len): |
| hidden = wordvec[seqidx] |
| for i in range(num_lstm_layer): |
| next_state = lstm(num_hidden, indata=hidden, |
| prev_state=last_states[i], |
| param=param_cells[i], |
| seqidx=seqidx, layeridx=i) |
| hidden = next_state.h |
| last_states[i] = next_state |
| hidden_all.append(hidden) |
| |
| hidden_concat = mx.sym.Concat(*hidden_all, dim=0) |
| fc = mx.sym.FullyConnected(data=hidden_concat, num_hidden=11) |
| sm = mx.sym.SoftmaxOutput(data=fc, name='softmax') |
| |
| output = [sm] |
| for state in last_states: |
| output.append(state.c) |
| output.append(state.h) |
| return mx.sym.Group(output) |