for epoch in range(args.epochs):
train_loss_span = 0
train_loss_type = 0
train_loss_degree = 0
train_loss_pol = 0
train_loss_cm = 0
train_batches = 0
start_time = time.time()
pbar = ProgressBar(maxval=maxlen_train).start()
for i, batch in enumerate(
iterate_minibatches_((X_train, Y_labels_train),
args.minibatch,
shuffle=True)):
time.sleep(0.01)
pbar.update(i + 1)
inputs, labels = batch
train_loss_span += train_fn_span(inputs, labels[:, 0:2])
train_loss_type += train_fn_type(inputs, labels[:, 7:11])
train_loss_degree += train_fn_degree(inputs, labels[:, 11:15])
train_loss_pol += train_fn_pol(inputs, labels[:, 15:18])
train_loss_cm += train_fn_cm(inputs, labels[:, 18:23])
train_batches += 1
for epoch in range(args.epochs):
train_loss_span = 0
train_loss_dcr = 0
train_loss_type = 0
train_loss_degree = 0
train_loss_pol = 0
train_loss_cm = 0
train_loss_ca = 0
train_loss_per = 0
train_batches = 0
start_time = time.time()
pbar = ProgressBar(maxval=maxlen).start()
for i, batch in enumerate(iterate_minibatches_((X_train, Y_labels_train), args.minibatch, shuffle=True)):
time.sleep(0.01)
pbar.update(i + 1)
inputs, labels= batch
train_loss_span += train_fn_span(inputs, labels[:,0:2])
train_loss_dcr += train_fn_dcr(inputs, labels[:,2:6])
train_loss_type += train_fn_type(inputs, labels[:,6:9])
train_loss_degree += train_fn_degree(inputs, labels[:,9:12])
train_loss_pol += train_fn_pol(inputs, labels[:,12:14])
train_loss_cm += train_fn_cm(inputs, labels[:,14:18])
train_loss_ca += train_fn_ca(inputs, labels[:,18:21])
train_loss_per += train_fn_per(inputs, labels[:,21:24])
print("Starting training span model...")
best_val_acc = 0
maxlen_train = 0
for x in range(0, len(X_train) - args.minibatch + 1, args.minibatch):
maxlen_train += 1
for epoch in range(args.epochs):
train_loss = 0
train_batches = 0
start_time = time.time()
pbar = ProgressBar(maxval=maxlen_train).start()
for i, batch in enumerate(iterate_minibatches_((X_train, Y_labels_train), args.minibatch, shuffle=True)):
time.sleep(0.01)
pbar.update(i + 1)
inputs, labels= batch
train_loss += train_fn(inputs, labels)
train_batches += 1
pbar.finish()
val_loss = 0
val_acc = 0
val_batches = 0
input_var = T.imatrix("inputs")
target_var = T.fmatrix("targets")
wordEmbeddings = loadWord2VecMap(os.path.join(data_dir, "word2vec.bin"))
wordEmbeddings = wordEmbeddings.astype(np.float32)
train_fn, val_fn = build_network_2dconv(args, input_var, target_var, wordEmbeddings)
print("Starting training...")
best_val_acc = 0
best_val_pearson = 0
for epoch in range(args.epochs):
train_err = 0
train_batches = 0
start_time = time.time()
for batch in iterate_minibatches_((X_train, X_mask_train, Y_labels_train), args.minibatch, shuffle=True):
inputs, _, labels = batch
train_err += train_fn(inputs, labels)
train_batches += 1
val_err = 0
val_acc = 0
val_batches = 0
val_pearson = 0
for batch in iterate_minibatches_((X_dev, X_mask_dev, Y_labels_dev), len(X_dev), shuffle=False):
inputs, inputs_mask, labels = batch
err, acc = val_fn(inputs, labels)
train_loss_span = 0
train_loss_dcr = 0
train_loss_type = 0
train_loss_degree = 0
train_loss_pol = 0
train_loss_cm = 0
train_loss_ca = 0
train_loss_per = 0
train_batches = 0
start_time = time.time()
pbar = ProgressBar(maxval=maxlen).start()
for i, batch in enumerate(
iterate_minibatches_((X_train, Y_labels_train),
args.minibatch,
shuffle=True)):
time.sleep(0.01)
pbar.update(i + 1)
inputs, labels = batch
train_loss_span += train_fn_span(inputs, labels[:, 0:2])
train_loss_dcr += train_fn_dcr(inputs, labels[:, 2:6])
train_loss_type += train_fn_type(inputs, labels[:, 6:9])
train_loss_degree += train_fn_degree(inputs, labels[:, 9:12])
train_loss_pol += train_fn_pol(inputs, labels[:, 12:14])
train_loss_cm += train_fn_cm(inputs, labels[:, 14:18])
train_loss_ca += train_fn_ca(inputs, labels[:, 18:21])
train_loss_per += train_fn_per(inputs, labels[:, 21:24])
