def build_graph(self, batch):
enc_data = batch.data
self.reset()
bsize, enc_length = enc_data.shape
outputs = []
# Build Fwd Encode Graph
self.feh0.value = np.zeros([bsize, self.half_dim])
self.fec0.value = np.zeros([bsize, self.half_dim])
fh = self.feh0
fc = self.fec0
for idx in range(enc_length):
in_i = self.input()
in_i.value = enc_data[:, idx] # Get value from batch
x = Embed(in_i, self.feembed)
fh, fc = self.fenc_lstm_cell(x, fh, fc)
# Build Bwd Encode Graph
self.beh0.value = np.zeros([bsize, self.half_dim])
self.bec0.value = np.zeros([bsize, self.half_dim])
bh = self.beh0
bc = self.bec0
for idx in range(enc_length):
in_i = self.input()
in_i.value = enc_data[:,
enc_length - 1 - idx] # Get value from batch
x = Embed(in_i, self.beembed)
bh, bc = self.benc_lstm_cell(x, bh, bc)
# Build Decode Graph
h = Concat(fh, bh)
c = Concat(fc, bc)
self.encoded_h = h
self.encoded_c = c
# Build Decode Graph
decode_in = self.input()
decode_in.value = np.zeros([bsize])
decode_embed = Embed(decode_in, self.dembed)
x = decode_embed
for idx in range(self.predict_len):
h, c = self.dec_lstm_cell(x, h, c)
out_i = ArgMax(SoftMax(Dot(h, self.dv2c)))
outputs.append(out_i)
x = Embed(out_i, self.dembed)
self.output(Collect(outputs))
self.expect(np.zeros([bsize, self.predict_len]))
def build_graph(self, batch):
enc_data = batch.data[0]
dec_data = batch.data[1]
self.reset()
bsize, enc_length = enc_data.shape
dec_length = dec_data.shape[1]
outputs = []
# Build Fwd Encode Graph
self.feh0.value = np.zeros([bsize, self.half_dim])
self.fec0.value = np.zeros([bsize, self.half_dim])
fh = self.feh0
fc = self.fec0
for idx in range(enc_length):
in_i = self.input()
in_i.value = enc_data[:, idx] # Get value from batch
x = Embed(in_i, self.feembed)
fh, fc = self.fenc_lstm_cell(x, fh, fc)
# Build Bwd Encode Graph
self.beh0.value = np.zeros([bsize, self.half_dim])
self.bec0.value = np.zeros([bsize, self.half_dim])
bh = self.beh0
bc = self.bec0
for idx in range(enc_length):
in_i = self.input()
in_i.value = enc_data[:,
enc_length - 1 - idx] # Get value from batch
x = Embed(in_i, self.beembed)
bh, bc = self.benc_lstm_cell(x, bh, bc)
# Build Decode Graph
h = Concat(fh, bh)
c = Concat(fc, bc)
self.encoded_h = h
self.encoded_c = c
for idx in range(dec_length - 1):
in_i = self.input()
in_i.value = dec_data[:, idx]
x = Embed(in_i, self.dembed)
h, c = self.dec_lstm_cell(x, h, c)
out_i = SoftMax(Dot(h, self.dv2c))
outputs.append(out_i)
self.output(Collect(outputs))
self.expect(dec_data[:, 1:])
def dec_lstm_cell(self, x, h, c):
concat = Concat(h, x)
# Forget Gate
f_gate = Sigmoid(Add(Dot(concat, self.dwf), self.dbf))
# Input Gate
i_gate = Sigmoid(Add(Dot(concat, self.dwi), self.dbi))
# Temp Vars
c_temp = Tanh(Add(Dot(concat, self.dwc), self.dbc))
o_temp = Sigmoid(Add(Dot(concat, self.dwo), self.dbo))
# Output
c_next = Add(Mul(f_gate, c), Mul(i_gate, c_temp))
h_next = Mul(o_temp, Tanh(c_next))
return h_next, c_next
def __init__(self, wf, bf, wi, bi, wc, bc, wo, bo, x, h, c):
self.wf = wf
self.bf = bf
self.wi = wi
self.bi = bi
self.wc = wc
self.bc = bc
self.wo = wo
self.bo = bo
self.x = x
self.h = h
self.c = c
concat = Concat(x, h)
fgate = Sigmoid(Add(Dot(concat, wf), bf))
igate = Sigmoid(Add(Dot(concat, wi), bi))
cgate = Mul(Tanh(Add(Dot(concat, wc), bc)), igate)
ogate = Sigmoid(Add(Dot(concat, wo), bo))
self.cout = Add(Mul(c, fgate), cgate)
self.hout = Mul(Tanh(self.cout), ogate)
def build_graph(self, batch):
enc_data = batch.data
dec_data = batch.expect
self.reset()
bsize = 1
enc_length = enc_data.shape[1]
dec_length = dec_data.shape[0]
outputs = []
hcollect = []
ccollect = []
self.feh0.value = np.zeros([bsize, self.half_dim])
self.fec0.value = np.zeros([bsize, self.half_dim])
self.beh0.value = np.zeros([bsize, self.half_dim])
self.bec0.value = np.zeros([bsize, self.half_dim])
for line_idx in range(self.num_line):
# Build Fwd Encode Graph
fh = self.feh0
fc = self.fec0
for idx in range(enc_length):
in_i = self.input()
in_i.value = enc_data[line_idx,
idx].reshape(1) # Get value from batch
x = Embed(in_i, self.feembed)
fh, fc = self.fenc_lstm_cell(x, fh, fc)
# Build Bwd Encode Graph
bh = self.beh0
bc = self.bec0
for idx in range(enc_length):
in_i = self.input()
in_i.value = enc_data[line_idx, enc_length - 1 - idx].reshape(
1) # Get value from batch
x = Embed(in_i, self.beembed)
bh, bc = self.benc_lstm_cell(x, bh, bc)
h = Concat(fh, bh)
c = Concat(fc, bc)
hcollect.append(h)
ccollect.append(c)
# Build Decode Graph
h = Average(Collect(hcollect))
c = Average(Collect(ccollect))
self.encoded_h = h
self.encoded_c = c
for idx in range(dec_length - 1):
in_i = self.input()
in_i.value = dec_data[idx].reshape(1)
x = Embed(in_i, self.dembed)
h, c = self.dec_lstm_cell(x, h, c)
out_i = SoftMax(Dot(h, self.dv2c))
outputs.append(out_i)
self.output(Collect(outputs))
self.expect(dec_data[1:])
def build_graph(self, batch):
enc_data = batch.data
self.reset()
bsize, enc_length = enc_data.shape
outputs = []
fwd_encode_result = [None] * enc_length
bwd_encode_result = [None] * enc_length
# Build Fwd Encode Graph
self.feh0.value = np.zeros([bsize, self.half_dim])
self.fec0.value = np.zeros([bsize, self.half_dim])
fh = self.feh0
fc = self.fec0
for idx in range(enc_length):
in_i = self.input()
in_i.value = enc_data[:, idx] # Get value from batch
x = Embed(in_i, self.feembed)
fh, fc = self.fenc_lstm_cell(x, fh, fc)
fwd_encode_result[idx] = fh
# Build Bwd Encode Graph
self.beh0.value = np.zeros([bsize, self.half_dim])
self.bec0.value = np.zeros([bsize, self.half_dim])
bh = self.beh0
bc = self.bec0
for idx in range(enc_length):
in_i = self.input()
in_i.value = enc_data[:,
enc_length - 1 - idx] # Get value from batch
x = Embed(in_i, self.beembed)
bh, bc = self.benc_lstm_cell(x, bh, bc)
bwd_encode_result[enc_length - 1 - idx] = bh
# Build Decode Graph
h = Concat(fh, bh)
# c = Concat(fc, bc)
self.encoded_h = h
# self.encoded_c = c
encode_result = []
for idx in range(enc_length):
encode_result.append(
Concat(fwd_encode_result[idx], bwd_encode_result[idx]))
encode_state = Collect(encode_result)
init = self.input()
init.value = np.zeros([bsize])
x = Embed(init, self.dembed)
for idx in range(self.predict_len):
c = Attention(encode_state, h)
h, c = self.dec_lstm_cell(x, h, c)
out_i = ArgMax(SoftMax(Dot(h, self.dv2c)))
outputs.append(out_i)
x = Embed(out_i, self.dembed)
self.output(Collect(outputs))
self.expect(np.zeros([bsize, self.predict_len]))
