dupa
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wangobango
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65
main.py
65
main.py
@ -1,4 +1,5 @@
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import os
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from typing import Counter
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from util import Model
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import spacy
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import torch
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@ -38,7 +39,7 @@ def compute_class_vector(mark, classes):
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for x in range(len(classes)):
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if classes[x] == mark[0]:
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result[x] = 1.0
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return torch.tensor(result, dtype=torch.long)
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return torch.tensor(result, dtype=torch.float)
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def prepare_input(index, data, context_size):
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x = data[index: index + context_size]
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@ -76,28 +77,32 @@ context_size = 5
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model = Model()
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epochs = 5
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output_prefix = "model"
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train_loss_acc = 30
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device=torch.device("cuda")
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model = model.cuda()
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optimizer = torch.optim.AdamW(model.parameters(), lr=0.001)
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scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 15, 0.0001)
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loss_function = torch.nn.MSELoss()
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model.train()
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optimizer = torch.optim.AdamW(model.parameters(), lr=0.02)
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loss_function = softXEnt
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"""
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TODO
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1) dodać przetwarzanie baczowe
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2) dodać osobną sieć w pełni połączoną która używa dźwięku żeby wykrywać czy użyć interpunkcji czy nie
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2) zmienić loss function
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"""
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hidden_state = torch.randn((2, 1, 300), requires_grad=True).to(device)
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cell_state = torch.randn((2, 1, 300), requires_grad=True).to(device)
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# hidden_state = torch.randn((2, 1, 300), requires_grad=True).to(device)
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# cell_state = torch.randn((2, 1, 300), requires_grad=True).to(device)
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counter = 0
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# model.load_state_dict(torch.load("model-4.pt"))
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if mode == "train":
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for epoch in range(epochs):
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for path in tqdm.tqdm(data_paths):
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for path in tqdm.tqdm(data_paths[:20]):
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with open(path, mode="r", encoding="utf-8") as file:
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list = file.readlines()
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list = file.readlines()[:100]
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for i in range(0, len(list) - context_size - 1 - 1):
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model.zero_grad()
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x = list[i: i + context_size]
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@ -105,40 +110,54 @@ if mode == "train":
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x_1 = [line2word(list[i + context_size + 1])]
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mark = find_interpunction(x[-1], classes)
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if mark == '':
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continue
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x = x + x_1
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x = words_to_vecs(x)
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x = torch.tensor(x, dtype=torch.float)
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x = x.to(device)
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output, (hidden_state, cell_state) = model.forward(x, hidden_state, cell_state)
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output, (_,_) = model.forward(x)
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output = output.squeeze(1)
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loss = loss_function(output, compute_class_vector([mark], classes).to(device))
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class_vector = compute_class_vector([mark], classes).to(device)
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loss = loss_function(torch.mean(output, 0), class_vector)
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if counter % 10 == 0:
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print(torch.mean(output, 0))
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print(loss)
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print(class_vector)
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loss.backward()
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if counter % train_loss_acc == 0:
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scheduler.step()
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optimizer.step()
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hidden_state = hidden_state.detach()
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cell_state = cell_state.detach()
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optimizer.zero_grad()
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model.zero_grad()
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counter += 1
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print("Epoch: {}".format(epoch))
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torch.save(
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model.state_dict(),
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os.path.join("./", f"{output_prefix}-{epoch}.pt"),
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)
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with open("hidden_state.pickle", "wb") as hs:
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pickle.dump(hidden_state, hs)
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with open("cell_state.pickle", "wb") as cs:
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pickle.dump(cell_state, cs)
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# with open("hidden_state.pickle", "wb") as hs:
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# pickle.dump(hidden_state, hs)
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# with open("cell_state.pickle", "wb") as cs:
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# pickle.dump(cell_state, cs)
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elif mode == "evaluate":
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correct = 0
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incorrect = 0
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threshold = 0.3
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model.load_state_dict(torch.load("model-0.pt"))
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model.load_state_dict(torch.load("model-4.pt"))
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model.eval()
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with open("hidden_state.pickle", "rb") as hs:
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hidden_state = pickle.load(hs)
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with open("cell_state.pickle", "rb") as cs:
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cell_state = pickle.load(cs)
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# with open("hidden_state.pickle", "rb") as hs:
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# hidden_state = pickle.load(hs)
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# with open("cell_state.pickle", "rb") as cs:
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# cell_state = pickle.load(cs)
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for pathA, pathB in zip(data_no_punc_paths, data_paths):
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with open(pathA, mode="r", encoding='utf-8') as file:
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with open(pathB, mode="r", encoding='utf-8') as file2:
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@ -156,7 +175,7 @@ elif mode == "evaluate":
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mark_y = find_interpunction(x[-1], classes)
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x = torch.tensor(x, dtype=torch.float).to(device)
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output, (hidden_state, cell_state) = model.forward(x, hidden_state, cell_state)
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output, (hidden_state, cell_state) = model.forward(x)
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output = output.cpu()
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output = output.detach().numpy()
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output = np.mean(output, axis=0)
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@ -164,7 +183,7 @@ elif mode == "evaluate":
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result_index = np.argmax(output)
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# if output[result_index] < threshold:
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# incorrect += 1
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print(output)
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if len(mark_y) > 0:
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if classes[np.argmax(output)] == mark_y:
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correct += 1
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6
util.py
6
util.py
@ -20,11 +20,11 @@ class Model(torch.nn.Module):
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self.dense2 = torch.nn.Linear(300, 8)
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self.softmax = torch.nn.Softmax(dim=0)
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def forward(self, data, hidden_state, cell_state):
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def forward(self, data):
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data = self.dense1(data.T)
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data = self.tanh1(data)
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data, (hidden_state, cell_state) = self.lstm(data.unsqueeze(1), (hidden_state, cell_state))
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# data, (hidden_state, cell_state) = self.lstm(data.unsqueeze(1))
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# data, (hidden_state, cell_state) = self.lstm(data.unsqueeze(1), (hidden_state, cell_state))
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data, (hidden_state, cell_state) = self.lstm(data.unsqueeze(1))
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data = self.dense2(data)
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data = self.softmax(data)
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return data, (hidden_state, cell_state)
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