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# +
from Anechoic import get_ttv

train, test, validate = get_ttv('./data', columns='reflection_count')

# +
import torch

device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f'Using {device} device')
# -

from torch import nn
class NeuralNetwork(nn.Module):
    def __init__(self, input_shape, output_shape):
        super(NeuralNetwork, self).__init__()
        self.flatten = nn.Flatten()
        self.linear_relu_stack = nn.Sequential(
            nn.Linear(input_shape, 512),
            nn.ReLU(),
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512, output_shape)
        )
        
    def forward(self, x):
        x = self.flatten(x)
        logits = self.linear_relu_stack(x)
        return logits


model = NeuralNetwork(100, 10).to(device)
print(model)



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