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Variable input and target sizes #5

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Variable input and target sizes #5

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9 changes: 5 additions & 4 deletions code/lstm_encoder_decoder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -98,7 +98,7 @@ def forward(self, x_input, encoder_hidden_states):
class lstm_seq2seq(nn.Module):
''' train LSTM encoder-decoder and make predictions '''

def __init__(self, input_size, hidden_size):
def __init__(self, input_size,target_size,hidden_size):

'''
: param input_size: the number of expected features in the input X
Expand All @@ -108,10 +108,11 @@ def __init__(self, input_size, hidden_size):
super(lstm_seq2seq, self).__init__()

self.input_size = input_size
self.target_size= target_size
self.hidden_size = hidden_size

self.encoder = lstm_encoder(input_size = input_size, hidden_size = hidden_size)
self.decoder = lstm_decoder(input_size = input_size, hidden_size = hidden_size)
self.decoder = lstm_decoder(input_size = target_size, hidden_size = hidden_size)


def train_model(self, input_tensor, target_tensor, n_epochs, target_len, batch_size, training_prediction = 'recursive', teacher_forcing_ratio = 0.5, learning_rate = 0.01, dynamic_tf = False):
Expand Down Expand Up @@ -161,7 +162,7 @@ def train_model(self, input_tensor, target_tensor, n_epochs, target_len, batch_s
target_batch = target_tensor[:, b: b + batch_size, :]

# outputs tensor
outputs = torch.zeros(target_len, batch_size, input_batch.shape[2])
outputs = torch.zeros(target_len, batch_size, target_batch.shape[2])

# initialize hidden state
encoder_hidden = self.encoder.init_hidden(batch_size)
Expand All @@ -173,7 +174,7 @@ def train_model(self, input_tensor, target_tensor, n_epochs, target_len, batch_s
encoder_output, encoder_hidden = self.encoder(input_batch)

# decoder with teacher forcing
decoder_input = input_batch[-1, :, :] # shape: (batch_size, input_size)
decoder_input = target_batch[-1, :, :] # shape: (batch_size, input_size)
decoder_hidden = encoder_hidden

if training_prediction == 'recursive':
Expand Down