Hi,
I applied LSTM as input CNN feaures. I observed until 10 epochs but the loss value does not decrease.It is usually around 1.60(loss value).
I did not find where is problem.Please help me .
I have to set 1 to mini-batch size ,because of

cuda runtime error (2) : out of memory

I have 5 classes and total number of video is 300.The videos are different length.And max length is 600.
Lr : 1e-4
Lstm-layers : 1
Hidden size: 512
Fc-size: 1024
I scaled down hidden size to 64, but result there is no change.
def __init__(self, original_model, arch, num_classes, lstm_layers, hidden_size, fc_size):
	super(LSTMModel, self).__init__()
	self.hidden_size = hidden_size
	self.num_classes = num_classes
	self.fc_size = fc_size
	if arch.startswith('resnet50'):
		self.features = nn.Sequential(*list(original_model.children())[:-1])
		for i, param in enumerate(self.features.parameters()):
			param.requires_grad = False
		self.fc_pre = nn.Sequential(nn.Linear(2048, fc_size), nn.Dropout())
		self.rnn = nn.LSTM(input_size = fc_size,
					hidden_size = hidden_size,
					num_layers = lstm_layers,
					batch_first = True)
		self.fc = nn.Linear(hidden_size, num_classes)

	else:
		raise Exception("This architecture has not been supported yet")

def init_hidden(self, num_layers, batch_size):

	return (torch.zeros(num_layers, batch_size, self.hidden_size).cuda(),
			torch.zeros(num_layers, batch_size, self.hidden_size).cuda())

def forward(self, inputs, hidden=None, steps=0):
	length = len(inputs)
	fs = torch.zeros(inputs[0].size(0), length, self.rnn.input_size).cuda()

	for i in range(length):
		f = self.features(inputs[i])
		f = f.view(f.size(0), -1)
		f = self.fc_pre(f)
		fs[:, i, :] = f

	outputs, hidden = self.rnn(fs, hidden)
	outputs = self.fc(outputs)
	return outputs