tgt_embed_layer.initialize(1)
second_input = tgt_embed_layer.belongs_to(decoder).compute(
feedback_var, mask=tgt_mask_var)
second_input = DimShuffle(1, 0, 2).compute(second_input)
recurrent_unit = GRU(args.hidden_size,
input_type="sequence",
output_type="sequence",
additional_input_dims=[args.word_embed])
expander_chain = Chain(Dense(600), Dense(args.tgt_vocab_size))
expander_chain.initialize(args.hidden_size)
attention_layer = SoftAttentionalLayer(recurrent_unit)
attention_var = attention_layer.belongs_to(decoder).compute(
hidden_layer,
mask=src_mask_var,
feedback=second_input,
steps=tgt_var.shape(1))
# expander
output_var = expander_chain.belongs_to(expander).compute(attention_var)
cost = TMCostLayer(tgt_var, tgt_mask_var,
args.tgt_vocab_size).compute(output_var)
model = ComputationalGraph(input_dim=[src_var, src_mask_var])
model.training_callbacks.append(training_monitor)
# decoder
# the first token is <s>=1
feedback_var = tgt_var.apply(lambda t: T.concatenate([T.ones((t.shape[0], 1), dtype="int32"), t[:, :-1]], axis=1))
tgt_embed_layer = WordEmbedding(args.word_embed, args.tgt_vocab_size)
tgt_embed_layer.initialize(1)
second_input = tgt_embed_layer.belongs_to(decoder).compute(feedback_var, mask=tgt_mask_var)
second_input = DimShuffle(1, 0, 2).compute(second_input)
recurrent_unit = LSTM(args.hidden_size, input_type="sequence", output_type="sequence", additional_input_dims=[args.word_embed])
attention_layer = SoftAttentionalLayer(recurrent_unit)
attention_var = attention_layer.belongs_to(decoder).compute(hidden_layer, mask=src_mask_var, feedback=second_input, steps=tgt_var.shape[1])
# expander
output_var = Chain(Dense(600), Dense(args.tgt_vocab_size)).belongs_to(expander).compute(attention_var)
cost = TMCostLayer(tgt_var, tgt_mask_var, args.tgt_vocab_size).compute(output_var)
model = ComputationalGraph(input_vars=[src_var, src_mask_var],
target_vars=[tgt_var, tgt_mask_var],
blocks=[encoder, decoder, expander],
cost=cost)
data = OnDiskDataset("{}/aspec.enja1_train.pkl".format(WMT_ROOT),
valid_path="{}/aspec.enja1_valid.pkl".format(WMT_ROOT),
backward_rnn_var = Chain(GRU(args.hidden_size, input_type="sequence", output_type="sequence", backward=True),
Reverse3D()).belongs_to(encoder).compute(encoder_embed)
encoder_output_var = Concatenate(axis=2).compute(forward_rnn_var, backward_rnn_var)
# decoder
decoder = Block()
last_token_var = create_var(T.ivector("tok"), test_shape=[64], test_dtype="int32")
seq_input_var = create_var(T.matrix('seq'), dim=args.hidden_size * 2, test_shape=[64, args.hidden_size * 2])
state_var = create_var(T.matrix("s"), dim=args.hidden_size, test_shape=[64, args.hidden_size])
input_embed = WordEmbedding(args.word_embed, args.tgt_vocab_size).belongs_to(decoder).compute(last_token_var)
recurrent_unit = GRU(args.hidden_size, input_type="sequence", output_type="sequence",
additional_input_dims=[input_embed.dim()])
attention_layer = SoftAttentionalLayer(recurrent_unit)
attention_layer.belongs_to(decoder).initialize(input_dim=args.hidden_size * 2)
step_parameters = attention_layer.get_step_inputs(seq_input_var, state=state_var, feedback=input_embed)
new_state = attention_layer.step(step_parameters)["state"]
new_state.output_dim = args.hidden_size
decoder_output_var = new_state
# expander
expander = Block()
expander_input_var = create_var(T.matrix("expander_input"), dim=args.hidden_size, test_shape=[64, args.hidden_size])
dense_var = Chain(Dense(600), Dense(args.tgt_vocab_size)).belongs_to(expander).compute(expander_input_var)
.belongs_to(encoder).compute(encoder_embed))
backward_rnn_var = Chain(GRU(args.hidden_size, input_type="sequence", output_type="sequence", backward=True),
Reverse3D()).belongs_to(encoder).compute(encoder_embed)
encoder_output_var = Concatenate(axis=2).compute(forward_rnn_var, backward_rnn_var)
# decoder
decoder = Block()
last_token_var = create_var(T.ivector("tok"), test_shape=[64], test_dtype="int32")
seq_input_var = create_var(T.matrix('seq'), dim=args.hidden_size * 2, test_shape=[64, args.hidden_size * 2])
state_var = create_var(T.matrix("s"), dim=args.hidden_size, test_shape=[64, args.hidden_size])
input_embed = WordEmbedding(args.word_embed, args.tgt_vocab_size).belongs_to(decoder).compute(last_token_var)
recurrent_unit = GRU(args.hidden_size, input_type="sequence", output_type="sequence", additional_input_dims=[input_embed.dim()])
attention_layer = SoftAttentionalLayer(recurrent_unit, test=True)
attention_layer.belongs_to(decoder).initialize(args.hidden_size * 2)
new_state = attention_layer.step({
"UaH": T.dot(seq_input_var.tensor, attention_layer.Ua),
"feedback": input_embed.tensor,
"inputs": seq_input_var.tensor,
"state": state_var.tensor
})["state"]
decoder_output_var = create_var(new_state, dim=args.hidden_size)
# expander
expander = Block()
expander_input_var = create_var(T.matrix("expander_input"), dim=args.hidden_size, test_shape=[64, args.hidden_size])
seq_input_var = create_var(T.matrix('seq'),
dim=args.hidden_size * 2,
test_shape=[64, args.hidden_size * 2])
state_var = create_var(T.matrix("s"),
dim=args.hidden_size,
test_shape=[64, args.hidden_size])
input_embed = WordEmbedding(
args.word_embed,
args.tgt_vocab_size).belongs_to(decoder).compute(last_token_var)
recurrent_unit = GRU(args.hidden_size,
input_type="sequence",
output_type="sequence",
additional_input_dims=[input_embed.dim()])
attention_layer = SoftAttentionalLayer(recurrent_unit, test=True)
attention_layer.belongs_to(decoder).initialize(args.hidden_size * 2)
new_state = attention_layer.step({
"UaH":
T.dot(seq_input_var.tensor, attention_layer.Ua),
"feedback":
input_embed.tensor,
"inputs":
seq_input_var.tensor,
"state":
state_var.tensor
})["state"]
decoder_output_var = create_var(new_state, dim=args.hidden_size)
# expander
