from_type4 = type4[0:n_inputs]
from_type4_targets = type4_targets[0:n_inputs]
inputs = torch.cat((from_type1, from_type2, from_type3, from_type4), 0 )
targets = torch.cat( (from_type1_targets,from_type2_targets,from_type3_targets,from_type4_targets) ,0)
args = dict()
args['n_inputs'] = n_inputs
args['n_neurons'] = n_neurons
# Model
net = NeuralNet(hidden_neurons=args['n_neurons'])
criterion = nn.MSELoss(reduction="mean")
optimizer = optim.SGD(net.parameters(), lr=learning_rate)
hold_loss=[]
EPOCHS = math.ceil(K /(n_inputs * 4))
# prog_bar = Bar('Training...', suffix='%(percent).1f%% - %(eta)ds - %(index)d / %(max)d', max=EPOCHS )
# Train loop
for epoch in range(0, EPOCHS):
running_loss = 0.0
# Batch gradient descent
optimizer.zero_grad()
output = net(inputs)
loss = criterion(output, targets)
running_loss += loss.data
loss.backward()
optimizer.step()
