def __init__(self, embedding, batch_size):
TEXT, vocab_size, word_embeddings, self.train_iter, self.valid_iter, self.test_iter = load_dataset.load(embedding=embedding, batch_size=batch_size)
self.embedding = embedding
output_size = 10
learning_rate = 2e-5
self.model = LogisticRegressionModel(output_size, vocab_size, 300, word_embeddings)
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, self.model.parameters()), weight_decay=0.0005, lr=0.0001)
loss_fn = F.cross_entropy
self.training_handler = TrainingHandler(optimizer, loss_fn, batch_size)
def __init__(self, embedding, batch_size):
TEXT, vocab_size, word_embeddings, self.train_iter, self.valid_iter, self.test_iter = load_dataset.load(
embedding=embedding, batch_size=batch_size)
self.embedding = embedding
output_size = 10
hidden_size = 256
embedding_length = 300
self.model = LSTMClassifier(batch_size, output_size, hidden_size,
vocab_size, embedding_length,
word_embeddings)
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
self.model.parameters()),
weight_decay=0.0005,
lr=0.0001)
loss_fn = F.cross_entropy
self.training_handler = TrainingHandler(optimizer, loss_fn, batch_size)
def __init__(self, embedding, batch_size):
TEXT, vocab_size, word_embeddings, self.train_iter, self.valid_iter, self.test_iter = load_dataset.load(
embedding=embedding, batch_size=batch_size)
self.embedding = embedding
output_size = 10
in_channel = 1
out_channel = 16
kernel_heights = [3, 5, 7]
keep_probab = 0
stride = 1
padding = [1, 2, 3]
embedding_length = 300
self.model = CNN(batch_size, output_size, in_channel, out_channel,
kernel_heights, stride, padding, keep_probab,
vocab_size, embedding_length, word_embeddings)
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
self.model.parameters()),
weight_decay=0.0005,
lr=0.0001)
loss_fn = F.cross_entropy
self.training_handler = TrainingHandler(optimizer, loss_fn, batch_size)