Hi guys,
I have array of rgb images with shape (3000, 3, 96, 96 ) with shape of labels like (3000, 4). labels are hot vector. but I am getting error like “Expected input batch_size (150) to match target batch_size (50)” when it tries to calculate the loss. would you please tell me what should i change?
network:
class RNNModel(nn.Module):
def __init__(self, input_dim, hidden_dim, layer_dim, output_dim):
super(RNNModel, self).__init__()
# Hidden dimensions
self.hidden_dim = hidden_dim
# Number of hidden layers
self.layer_dim = layer_dim
# Building your RNN
# batch_first=True causes input/output tensors to be of shape
# (batch_dim, seq_dim, input_dim)
# batch_dim = number of samples per batch
self.rnn = nn.RNN(input_dim, hidden_dim, layer_dim, batch_first=True, nonlinearity='relu')
# Readout layer
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
# Initialize hidden state with zeros
# (layer_dim, batch_size, hidden_dim)
h0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_()
# We need to detach the hidden state to prevent exploding/vanishing gradients
# This is part of truncated backpropagation through time (BPTT)
out, hn = self.rnn(x, h0.detach())
out = self.fc(out[:, -1, :])
return out
with parameters:
input_dim = 96
hidden_dim = 100
layer_dim = 1
output_dim = 4
model = RNNModel(input_dim, hidden_dim, layer_dim, output_dim)
criterion = nn.CrossEntropyLoss()
learning_rate = 0.01
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
print(len(list(model.parameters())))
print(list(model.parameters())[0].size()) # Input --> Hidden (A1) torch.Size([100, 96])
print(list(model.parameters())[2].size()) # Input --> Hidden BIAS (B1) torch.Size([100])
print(list(model.parameters())[1].size()) # Hidden --> Hidden (A3) torch.Size([100, 100])
print(list(model.parameters())[3].size()) # Hidden --> Hidden BIAS(B3) torch.Size([100])
print(list(model.parameters())[4].size()) # Hidden --> Output (A2) torch.Size([4, 100])
print(list(model.parameters())[5].size()) # Hidden -> Output BIAS (B2)torch.Size([4])
with train loop of:
# Number of steps to unroll
seq_dim = 96
num_epochs = 5
iter = 0
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader_fo):
model.train()
# Load images as tensors with gradient accumulation abilities
images = images.float()
images = images.view(-1, seq_dim, input_dim).requires_grad_()
# Clear gradients w.r.t. parameters
optimizer.zero_grad()
# Forward pass to get output/logits
# outputs.size() --> 100, 4
outputs = model(images)
# Calculate Loss: softmax --> cross entropy loss
loss = criterion(outputs, labels)
# Getting gradients w.r.t. parameters
loss.backward()
# Updating parameters
optimizer.step()
iter += 1
if iter % 500 == 0:
model.eval()
# Calculate Accuracy
correct = 0
total = 0
# Iterate through test dataset
for images, labels in vali_loader_fo:
# Load images to a Torch tensors with gradient accumulation abilities
images = images.view(-1, seq_dim, input_dim)
# Forward pass only to get logits/output
outputs = model(images)
# Get predictions from the maximum value
_, predicted = torch.max(outputs.data, 1)
# Total number of labels
total += labels.size(0)
# Total correct predictions
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
# Print Loss
print('Iteration: {}. Loss: {}. Accuracy: {}'.format(iter, loss.item(), accuracy))