blob: 326daa1d9f3a94bafa3a318d3fcf7d81ef251a77 [file] [log] [blame]
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# to you under the Apache License, Version 2.0 (the
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# pylint:skip-file
import sys
from mxnet.symbol_doc import SymbolDoc
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_fc = mx.sym.FullyConnected(data=hidden_concat, num_hidden=11)
pred_ctc = mx.sym.Reshape(data=pred_fc, shape=(-4, seq_len, -1, 0))
loss = mx.sym.contrib.ctc_loss(data=pred_ctc, label=label)
ctc_loss = mx.sym.MakeLoss(loss)
softmax_class = mx.symbol.SoftmaxActivation(data=pred_fc)
softmax_loss = mx.sym.MakeLoss(softmax_class)
softmax_loss = mx.sym.BlockGrad(softmax_loss)
return mx.sym.Group([softmax_loss, ctc_loss])