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predict.py

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+import numpy as np
+import torch
+from gensim import downloader
+import gensim
+
+
+class NeuralNetworkModel(torch.nn.Module):
+
+    def __init__(self):
+        super(NeuralNetworkModel, self).__init__()
+        self.fc1 = torch.nn.Linear(100, 500)
+        self.fc2 = torch.nn.Linear(500, 1)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = torch.relu(x)
+        x = self.fc2(x)
+        x = torch.sigmoid(x)
+        return x
+
+
+BATCH_SIZE = 5
+PATH = "new_model_full.pt"
+model = torch.load(PATH)
+model.eval()
+
+glove_vectors = downloader.load("glove-wiki-gigaword-100")
+
+with open(r"C:\Users\Kacper Dudzic\Desktop\paranormal-or-skeptic\dev-0\in.tsv", "r", encoding="utf-8") as dev_in, \
+        open(r"C:\Users\Kacper Dudzic\Desktop\paranormal-or-skeptic\test-A\in.tsv", "r", encoding="utf-8") as test_in:
+    X_dev = [line for line in dev_in.readlines()]
+    X_dev = [np.mean([glove_vectors[tk] for tk in gensim.utils.tokenize(text, lowercase=True) if tk in glove_vectors] or
+                     [np.zeros(100)], axis=0) for text in X_dev]
+
+    X_test = [line for line in test_in.readlines()]
+    X_test = [np.mean([glove_vectors[tk] for tk in gensim.utils.tokenize(text, lowercase=True) if tk in glove_vectors] or
+                [np.zeros(100)], axis=0) for text in X_test]
+
+with open('dev_out.tsv', 'w', encoding='utf-8') as dev_out, open('test_out.tsv', 'w', encoding='utf-8') as test_out:
+    dev_predictions = []
+    test_predictions = []
+
+    for i in range(0, len(X_dev), BATCH_SIZE):
+        X = X_dev[i:i + BATCH_SIZE]
+        X = torch.tensor(np.array(X).astype(np.float32))
+        Y_predictions = (model(X) > 0.5)
+        dev_predictions.extend(Y_predictions)
+
+    for i in range(0, len(X_test), BATCH_SIZE):
+        X = X_test[i:i + BATCH_SIZE]
+        X = torch.tensor(np.array(X).astype(np.float32))
+        Y_predictions = (model(X) > 0.5)
+        test_predictions.extend(Y_predictions)
+
+    for pred in dev_predictions:
+        dev_out.write(str(pred.int()[0].item()) + '\n')
+
+    for pred in test_predictions:
+        test_out.write(str(pred.int()[0].item()) + '\n')

train.py

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+import numpy as np
+import torch
+from gensim import downloader
+import gensim
+
+with open(r"C:\Users\Kacper Dudzic\Desktop\paranormal-or-skeptic\train\in.tsv", "r", encoding="utf-8") as f:
+    X_train = [line.strip() for line in f.readlines()]
+with open(r"C:\Users\Kacper Dudzic\Desktop\paranormal-or-skeptic\train\expected.tsv", "r", encoding="utf-8") as f:
+    Y_train = np.array([int(line.strip()) for line in f.readlines()])
+
+glove_vectors = downloader.load("glove-wiki-gigaword-100")
+X_train = [np.mean([glove_vectors[tk] for tk in gensim.utils.tokenize(text, lowercase=True) if tk in glove_vectors] or
+                   [np.zeros(100)], axis=0) for text in X_train]
+
+
+class NeuralNetworkModel(torch.nn.Module):
+
+    def __init__(self):
+        super(NeuralNetworkModel, self).__init__()
+        self.fc1 = torch.nn.Linear(100, 500)
+        self.fc2 = torch.nn.Linear(500, 1)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = torch.relu(x)
+        x = self.fc2(x)
+        x = torch.sigmoid(x)
+        return x
+
+
+nn_model = NeuralNetworkModel()
+criterion = torch.nn.BCELoss()
+optimizer = torch.optim.Adam(nn_model.parameters())
+
+BATCH_SIZE = 5
+for epoch in range(5):
+    loss_score = 0
+    acc_score = 0
+    items_total = 0
+    nn_model.train()
+    for i in range(0, Y_train.shape[0], BATCH_SIZE):
+        X = X_train[i:i + BATCH_SIZE]
+        X = torch.tensor(np.array(X).astype(np.float32))
+        Y = Y_train[i:i + BATCH_SIZE]
+        Y = torch.tensor(Y.astype(np.float32)).reshape(-1, 1)
+        Y_predictions = nn_model(X)
+        acc_score += torch.sum((Y_predictions > 0.5) == Y).item()
+        items_total += Y.shape[0]
+        optimizer.zero_grad()
+        loss = criterion(Y_predictions, Y)
+        loss.backward()
+        optimizer.step()
+
+        loss_score += loss.item() * Y.shape[0]
+
+    print(epoch)
+    print((loss_score / items_total), (acc_score / items_total))
+
+PATH = "new_model_full.pt"
+torch.save(nn_model, PATH)