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Solved deprecated warnings and errors: #27

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Jan 7, 2019
Merged

Solved deprecated warnings and errors: #27

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8 changes: 4 additions & 4 deletions reco_encoder/model/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,11 +28,11 @@ def activation(input, kind):
else:
raise ValueError('Unknown non-linearity type')

def MSEloss(inputs, targets, size_avarage=False):
def MSEloss(inputs, targets, size_average=False):
mask = targets != 0
num_ratings = torch.sum(mask.float())
criterion = nn.MSELoss(size_average=size_avarage)
return criterion(inputs * mask.float(), targets), Variable(torch.Tensor([1.0])) if size_avarage else num_ratings
criterion = nn.MSELoss(reduction='sum' if not size_average else 'mean')
return criterion(inputs * mask.float(), targets), Variable(torch.Tensor([1.0])) if size_average else num_ratings

class AutoEncoder(nn.Module):
def __init__(self, layer_sizes, nl_type='selu', is_constrained=True, dp_drop_prob=0.0, last_layer_activations=True):
Expand All @@ -57,7 +57,7 @@ def __init__(self, layer_sizes, nl_type='selu', is_constrained=True, dp_drop_pro
self.encode_w = nn.ParameterList(
[nn.Parameter(torch.rand(layer_sizes[i + 1], layer_sizes[i])) for i in range(len(layer_sizes) - 1)])
for ind, w in enumerate(self.encode_w):
weight_init.xavier_uniform(w)
weight_init.xavier_uniform_(w)

self.encode_b = nn.ParameterList(
[nn.Parameter(torch.zeros(layer_sizes[i + 1])) for i in range(len(layer_sizes) - 1)])
Expand Down