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/ lstm-peephole.py
Created
May 24, 2020 22:41
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| class CustomLSTM(nn.Module): | |
| def __init__(self, input_sz, hidden_sz, peephole=False): | |
| super().__init__() | |
| self.input_sz = input_sz | |
| self.hidden_size = hidden_sz | |
| self.peephole = peephole | |
| self.W = nn.Parameter(torch.Tensor(input_sz, hidden_sz * 4)) | |
| self.U = nn.Parameter(torch.Tensor(hidden_sz, hidden_sz * 4)) | |
| self.bias = nn.Parameter(torch.Tensor(hidden_sz * 4)) | |
| self.init_weights() |
piEsposito
/ optimized-lstm.py
Created
May 24, 2020 22:37
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| class CustomLSTM(nn.Module): | |
| def __init__(self, input_sz, hidden_sz): | |
| super().__init__() | |
| self.input_sz = input_sz | |
| self.hidden_size = hidden_sz | |
| self.W = nn.Parameter(torch.Tensor(input_sz, hidden_sz * 4)) | |
| self.U = nn.Parameter(torch.Tensor(hidden_sz, hidden_sz * 4)) | |
| self.bias = nn.Parameter(torch.Tensor(hidden_sz * 4)) | |
| self.init_weights() | |
piEsposito
/ ff-lstm.py
Created
May 24, 2020 22:15
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| def forward(self, | |
| x, | |
| init_states=None): | |
| """ | |
| assumes x.shape represents (batch_size, sequence_size, input_size) | |
| """ | |
| bs, seq_sz, _ = x.size() | |
| hidden_seq = [] | |
piEsposito
/ lstm-weight-init.py
Created
May 24, 2020 22:14
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| def init_weights(self): | |
| stdv = 1.0 / math.sqrt(self.hidden_size) | |
| for weight in self.parameters(): | |
| weight.data.uniform_(-stdv, stdv) |
piEsposito
/ custom-lstm-init.py
Created
May 24, 2020 22:05
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| class NaiveCustomLSTM(nn.Module): | |
| def __init__(self, input_sz: int, hidden_sz: int): | |
| super().__init__() | |
| self.input_size = input_sz | |
| self.hidden_size = hidden_sz | |
| #i_t | |
| self.U_i = nn.Parameter(torch.Tensor(input_sz, hidden_sz)) | |
| self.V_i = nn.Parameter(torch.Tensor(hidden_sz, hidden_sz)) | |
| self.b_i = nn.Parameter(torch.Tensor(hidden_sz)) |
piEsposito
/ lstm-post-imports.py
Created
May 24, 2020 22:02
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| import math | |
| import torch | |
| import torch.nn as nn |
piEsposito
/ nlp-tt-train-loop.py
Created
April 28, 2020 18:04
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| epoch_bar = tqdm(range(10), | |
| desc="Training", | |
| position=0, | |
| total=2) | |
| acc = 0 | |
| for epoch in epoch_bar: | |
| batch_bar = tqdm(enumerate(train_loader), | |
| desc="Epoch: {}".format(str(epoch)), |
piEsposito
/ nlp-tt-train-objects.py
Last active
April 28, 2020 18:08
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| ds_train = torch.utils.data.TensorDataset(X_train, y_train) | |
| train_loader = torch.utils.data.DataLoader(ds_train, batch_size=64, shuffle=True) | |
| ds_test = torch.utils.data.TensorDataset(X_test, y_test) | |
| test_loader = torch.utils.data.DataLoader(ds_test, batch_size=64, shuffle=True) | |
| classifier = Net() | |
| device = torch.device('cpu') | |
| optimizer = optim.Adam(classifier.parameters(), lr=.002) | |
| criterion = nn.CrossEntropyLoss() |
piEsposito
/ nlp-tt-torch-nn.py
Created
April 28, 2020 17:48
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| class Net(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.embedding = nn.Embedding(len(encoder.vocab)+1, 32) | |
| self.lstm = nn.LSTM(32, 32, batch_first=True) | |
| self.fc1 = nn.Linear(32, 2) | |
| def forward(self, x): | |
| x_ = self.embedding(x) | |
| x_, (h_n, c_n) = self.lstm(x_) |
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/ nlp-tt-pad-tensor-ds.py
Last active
April 28, 2020 17:42
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| padded_dataset = [] | |
| for i in tqdm(range(len(reviews))): | |
| padded_dataset.append(pad_tensor(reviews[i], int(max_pad_length))) | |
| X = torch.stack(padded_dataset) | |
| y = torch.tensor(labels) | |
| #train test splitting | |
| X_train, X_test, y_train, y_test = train_test_split(X, | |
| y, |
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