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])