Creating and shaping data for 1D CNN

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I have a (244, 108) numpy array. It contains percentage change of close value of a trade for each minute in one day ie 108 values and like that for 244 days. Basically its a 1D vector. So in order to do 1D CNN how should I shape my data?

What i have done:

x.shape = (244, 108)
x = np.expand_dims(x, axis=2)
x.shape = (243, 108, 1)
y.shape = (243,)

Model:

class Net(torch.nn.Module):   
    def __init__(self):
        super(Net, self).__init__()

        self.layer1 = torch.nn.Conv1d(in_channels=108, out_channels=1, kernel_size=1, stride=1)
        self.act1 = torch.nn.ReLU()
        self.act2 = torch.nn.MaxPool1d(kernel_size=1, stride=1)
        self.layer2 = torch.nn.Conv1d(in_channels=1, out_channels=1, kernel_size=1, stride=1)
        self.act3 = torch.nn.ReLU()
        self.act4 = torch.nn.MaxPool1d(kernel_size=1, stride=1)


        self.linear_layers = nn.Linear(1, 1)


    # Defining the forward pass    
    def forward(self, x):
        x = self.layer1(x)
        x = self.act1(x)
        x = self.act2(x)
        x = self.layer2(x)
        x = self.act3(x)
        x = self.act4(x)
        x = self.linear_layers(x)
        return x
conv-neural-network pytorch machine-learning
1 Answers

If each day should be separate instance for convolution your data should have the shape (248, 1, 108). This seems more reasonable.

If you want all your days and minutes to be a continuum for network to learn it should be of shape (1, 1, 248*108).

Basically first dimension is batch (how many training samples), second is the number of channels or features of sample (only one in your case) and last is the number of timesteps.

Edit

Your pooling layer should be torch.nn.AdaptiveMaxPool1d(1). You should also reshape output from this layer like this: pooled.reshape(x.shape[0], -1) before pushing it through torch.nn.Linear layer.

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