batch_idx = 0
best_bleu = None
for epoch in range(MAX_EPOCHES):
random.shuffle(train_data)
dial_shown = False
total_samples = 0
skipped_samples = 0
bleus_argmax = []
bleus_sample = []
for batch in data.iterate_batches(train_data, BATCH_SIZE):
batch_idx += 1
optimiser.zero_grad()
input_seq, input_batch, output_batch = \
model.pack_batch_no_out(batch, net.emb, device)
enc = net.encode(input_seq)
net_policies = []
net_actions = []
net_advantages = []
beg_embedding = net.emb(beg_token)
for idx, inp_idx in enumerate(input_batch):
total_samples += 1
ref_indices = [
indices[1:] for indices in output_batch[idx]
]
item_enc = net.get_encoded_item(enc, idx)
r_argmax, actions = net.decode_chain_argmax(
item_enc,
optimiser = optim.Adam(net.parameters(), lr=LEARNING_RATE, eps=1e-3)
batch_idx = 0
best_bleu = None
for epoch in range(MAX_EPOCHES):
random.shuffle(train_data)
dial_shown = False
total_samples = 0
skipped_samples = 0
bleus_argmax = []
bleus_sample = []
for batch in data.iterate_batches(train_data, BATCH_SIZE):
batch_idx += 1
optimiser.zero_grad()
input_seq, input_batch, output_batch = model.pack_batch_no_out(
batch, net.emb, device)
enc = net.encode(input_seq)
net_policies = []
net_actions = []
net_advantages = []
beg_embedding = net.emb(beg_token)
for idx, inp_idx in enumerate(input_batch):
total_samples += 1
ref_indices = [
indices[1:] for indices in output_batch[idx]
]
item_enc = net.get_encoded_item(enc, idx)
r_argmax, actions = net.decode_chain_argmax(
item_enc,
optimiser = optim.Adam(net.parameters(), lr=LEARNING_RATE, eps=1e-3)
batch_idx = 0
best_bleu = None
for epoch in range(MAX_EPOCHES):
random.shuffle(train_data)
dial_shown = False
total_samples = 0
skipped_samples = 0
bleus_argmax = []
bleus_sample = []
for batch in data.iterate_batches(train_data, BATCH_SIZE):
batch_idx += 1
optimiser.zero_grad()
input_seq, input_batch, output_batch = model.pack_batch_no_out(
batch, net.emb, cuda=args.cuda)
enc = net.encode(input_seq)
net_policies = []
net_actions = []
net_advantages = []
beg_embedding = net.emb(beg_token)
for idx, inp_idx in enumerate(input_batch):
total_samples += 1
ref_indices = [
indices[1:] for indices in output_batch[idx]
]
item_enc = net.get_encoded_item(enc, idx)
r_argmax, actions = net.decode_chain_argmax(
item_enc,
optimiser = optim.Adam(net.parameters(), lr=LEARNING_RATE, eps=1e-3)
batch_idx = 0
best_bleu = None
for epoch in range(MAX_EPOCHES):
random.shuffle(train_data)
dial_shown = False
total_samples = 0
skipped_samples = 0
bleus_argmax = []
bleus_sample = []
for batch in data.iterate_batches(train_data, BATCH_SIZE):
batch_idx += 1
optimiser.zero_grad()
input_seq, input_batch, output_batch = model.pack_batch_no_out(batch, net.emb, device)
enc = net.encode(input_seq)
net_policies = []
net_actions = []
net_advantages = []
beg_embedding = net.emb(beg_token)
for idx, inp_idx in enumerate(input_batch):
total_samples += 1
ref_indices = [
indices[1:]
for indices in output_batch[idx]
]
item_enc = net.get_encoded_item(enc, idx)
r_argmax, actions = net.decode_chain_argmax(item_enc, beg_embedding, data.MAX_TOKENS,