1 files changed, 97 insertions, 0 deletions
diff --git a/train_lstm.py b/train_lstm.py
new file mode 100644
index 0000000..b90f02b
--- /dev/null
+++ b/train_lstm.py
@@ -0,0 +1,97 @@
+from argparse import ArgumentParser
+
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+
+from dataset import Dataset
+from model import Model
+from predict import predict
+
+
+parser = ArgumentParser()
+parser.add_argument('-d', '--device', default='cpu',
+                    help='device to train with')
+parser.add_argument('-i', '--input', default='data',
+                    help='training data input file')
+parser.add_argument('-o', '--output', default='model.pt',
+                    help='trained model output file')
+parser.add_argument('-e', '--epochs', default=10, type=int,
+                    help='number of epochs to train for')
+parser.add_argument('-s', '--seq-size', default=32, type=int,
+                    help='sequence size')
+parser.add_argument('-b', '--batch-size', default=256, type=int,
+                    help='size of each training batch')
+parser.add_argument('-m', '--embedding-dim', default=64, type=int,
+                    help='size of the embedding')
+parser.add_argument('-l', '--lstm-size', default=256, type=int,
+                    help='size of the LSTM hidden state')
+parser.add_argument('-a', '--layers', default=3, type=int,
+                    help='number of LSTM layers')
+parser.add_argument('-r', '--dropout', default=0.2, type=int,
+                    help='how much dropout to apply')
+parser.add_argument('-n', '--max-norm', default=5, type=int,
+                    help='maximum norm for gradient clipping')
+args = parser.parse_args()
+
+
+# Prepare dataloader
+dataset = Dataset(args.input, args.seq_size)
+dataloader = DataLoader(dataset, args.batch_size)
+print(len(dataloader))
+
+
+# Prepare model
+device = torch.device(args.device)
+model = Model(dataset, args.embedding_dim, args.lstm_size,
+              args.layers, args.dropout).to(device)
+print(model)
+
+
+loss_fn = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
+
+
+for t in range(args.epochs):
+    state_h, state_c = model.zero_state(args.batch_size)
+    state_h = state_h.to(device)
+    state_c = state_c.to(device)
+
+    iteration = 0
+    for batch, (X, y) in enumerate(dataloader):
+        iteration += 1
+
+        model.train()
+
+        optimizer.zero_grad()
+
+        X = X.to(device)
+        y = y.to(device)
+
+        # Compute prediction error
+        logits, (state_h, state_c) = model(X, (state_h, state_c))
+        loss = loss_fn(logits.transpose(1, 2), y)
+
+        loss_value = loss.item()
+
+        # Backpropogation
+        loss.backward()
+
+        state_h = state_h.detach()
+        state_c = state_c.detach()
+
+        _ = torch.nn.utils.clip_grad_norm_(
+            model.parameters(), args.max_norm)
+
+        optimizer.step()
+
+        if iteration % 1 == 0:
+            print('Epoch: {}/{}'.format(t, args.epochs),
+                  'Iteration: {}'.format(iteration),
+                  'Loss: {}'.format(loss_value))
+
+        if iteration % 10 == 0:
+            print(' '.join(predict(args.device, dataset, model, 'i am')))
+
+
+torch.save(model, args.output)