"/root/ebms_regression/1dregression/training_logs/model_%s_%d/checkpoints/model_%s_epoch_%d.pth"
% (model_id, model_i, model_id, epoch)))
x_values = []
x_values_2_scores = {}
network.eval() # (set in eval mode, this affects BatchNorm and dropout)
for step, (x) in enumerate(val_loader):
x = x.cuda().unsqueeze(1) # (shape: (batch_size, 1))
y_samples = np.linspace(-3.0, 3.0,
num_samples) # (shape: (num_samples, ))
y_samples = y_samples.astype(np.float32)
y_samples = torch.from_numpy(
y_samples).cuda() # (shape: (batch_size, num_samples))
x_features = network.feature_net(x)
scores = network.predictor_net(
x_features,
y_samples.expand(x.shape[0],
-1)) # (shape: (batch_size, num_samples))
x_values.extend(x.squeeze(1).cpu().tolist())
for i, x_val in enumerate(x):
x_values_2_scores[x_val.item()] = scores[i, :].cpu().numpy()
print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$")
num_x_values = float(len(x_values))
print(num_x_values)
optimizer = torch.optim.Adam(network.parameters(), lr=learning_rate)
epoch_losses_train = []
for epoch in range(num_epochs):
print ("###########################")
print ("######## NEW EPOCH ########")
print ("###########################")
print ("model: %d/%d | epoch: %d/%d" % (i+1, num_models, epoch+1, num_epochs))
network.train() # (set in training mode, this affects BatchNorm and dropout)
batch_losses = []
for step, (xs, ys) in enumerate(train_loader):
xs = xs.cuda().unsqueeze(1) # (shape: (batch_size, 1))
ys = ys.cuda().unsqueeze(1) # (shape: (batch_size, 1))
x_features = network.feature_net(xs) # (shape: (batch_size, hidden_dim))
scores_gt = network.predictor_net(x_features, ys) # (shape: (batch_size, 1))
scores_gt = scores_gt.squeeze(1) # (shape: (batch_size))
y_samples_zero, q_y_samples, q_ys = sample_gmm_centered(stds, num_samples=num_samples)
y_samples_zero = y_samples_zero.cuda() # (shape: (num_samples, 1))
y_samples_zero = y_samples_zero.squeeze(1) # (shape: (num_samples))
q_y_samples = q_y_samples.cuda() # (shape: (num_samples))
y_samples = ys + y_samples_zero.unsqueeze(0) # (shape: (batch_size, num_samples))
q_y_samples = q_y_samples.unsqueeze(0)*torch.ones(y_samples.size()).cuda() # (shape: (batch_size, num_samples))
q_ys = q_ys[0]*torch.ones(xs.size(0)).cuda() # (shape: (batch_size))
scores_samples = network.predictor_net(x_features, y_samples) # (shape: (batch_size, num_samples))
########################################################################
# compute loss:
