Hello!
I have created dataset for 4 diff functions. My dataset is one-hot-encoded. Basically just 40 data elements, each being a one-hot-encoded string of 11 characters. There are total of 31 mappings.
So my dataset looks like tensor of (40,11,31)
My output is a label, with one one-hot-encoded letter. e.g. h,f
so label tensor is of the size (40,1,31)
My code looks like this
Error occurs on line
loss = criterion(outputs, labels)
```python
import torch
import torch.nn as nn
from torch.autograd import Variable
###################### define parameters for RNN #############
inputs = Variable(torch.Tensor(input_tensors_one_hot))
labels = Variable(torch.LongTensor(tensor_of_labels))
# print(f'im inputs size {inputs.size()}' )
print(f'im labels size {labels}' )
num_classes = 31
input_size = 31 # one-hot size
hidden_size = 31 # output from the LSTM. 31 to directly predict one-hot
batch_size = 1 # one string
sequence_length = 11 # string length == 11
num_layers = 1 # one-layer rnn
# print(inputs[0])
################ Define RNN #################
class RNN(nn.Module):
def __init__(self, num_classes, input_size, hidden_size, num_layers):
super(RNN, self).__init__()
self.num_classes = num_classes
self.num_layers = num_layers
self.input_size = input_size
self.hidden_size = hidden_size
self.sequence_length = sequence_length
self.rnn = nn.RNN(input_size=31, hidden_size=31, batch_first=True)
def forward(self, x):
# Initialize hidden and cell states
# (num_layers * num_directions, batch, hidden_size) for batch_first=True
h_0 = Variable(torch.zeros(
self.num_layers, x.size(0), self.hidden_size))
# Reshape input
x.view(x.size(0), self.sequence_length, self.input_size)
# Propagate input through RNN
# Input: (batch, seq_len, input_size)
# h_0: (num_layers * num_directions, batch, hidden_size)
out, _ = self.rnn(x, h_0)
print(f'im hidden size shape {out.size()}')
# dense_outputs = self.fc(out)
# print(f'im dense outputs {dense_outputs}')
return out.reshape(-1, num_classes)
# return out.view(-1, num_classes)
# Instantiate RNN model
rnn = RNN(num_classes, input_size, hidden_size, num_layers)
print(rnn)
# Set loss and optimizer function
# CrossEntropyLoss = LogSoftmax + NLLLoss
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(rnn.parameters(), lr=0.1)
# Train the model
for epoch in range(100):
outputs = rnn(inputs)
print('Im output size ', outputs.size())
optimizer.zero_grad()
print('Im label size ', labels.size())
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
_, idx = outputs.max(1)
idx = idx.data.numpy()
# result_str = [idx2char[c] for c in idx.squeeze()]
# print("epoch: %d, loss: %1.3f" % (epoch + 1, loss.data[0]))
# print("Predicted string: ", ''.join(result_str))
print("Predicted string: ", ''.join(idx))
print("Learning finished!")
