Loading deepchem/models/tensorgraph/layers.py +11 −0 Original line number Diff line number Diff line Loading @@ -1536,6 +1536,12 @@ class InteratomicL2Distances(Layer): class SparseSoftMaxCrossEntropy(Layer): """Computes Sparse softmax cross entropy between logits and labels. labels: Tensor of shape [d_0,d_1,....,d_{r-1}](where r is rank of logits) and must be of dtype int32 or int64. logits: Unscaled log probabilities of shape [d_0,....d{r-1},num_classes] and of dtype float32 or float64. Note: the rank of the logits should be 1 greater than that of labels. The output will be a tensor of same shape as labels and of same type as logits with the loss. """ def __init__(self, in_layers=None, **kwargs): super(SparseSoftMaxCrossEntropy, self).__init__(in_layers, **kwargs) Loading Loading @@ -4306,6 +4312,11 @@ class GraphCNN(Layer): class Hingeloss(Layer): """This layer computes the hinge loss on inputs:[labels,logits] labels: The values of this tensor is expected to be 1.0 or 0.0. The shape should be the same as logits. logits: Holds the log probabilities for labels, a float tensor. The output is a weighted loss tensor of same shape as labels. """ def __init__(self, in_layers=None, **kwargs): super(Hingeloss, self).__init__(in_layers, **kwargs) Loading Loading
deepchem/models/tensorgraph/layers.py +11 −0 Original line number Diff line number Diff line Loading @@ -1536,6 +1536,12 @@ class InteratomicL2Distances(Layer): class SparseSoftMaxCrossEntropy(Layer): """Computes Sparse softmax cross entropy between logits and labels. labels: Tensor of shape [d_0,d_1,....,d_{r-1}](where r is rank of logits) and must be of dtype int32 or int64. logits: Unscaled log probabilities of shape [d_0,....d{r-1},num_classes] and of dtype float32 or float64. Note: the rank of the logits should be 1 greater than that of labels. The output will be a tensor of same shape as labels and of same type as logits with the loss. """ def __init__(self, in_layers=None, **kwargs): super(SparseSoftMaxCrossEntropy, self).__init__(in_layers, **kwargs) Loading Loading @@ -4306,6 +4312,11 @@ class GraphCNN(Layer): class Hingeloss(Layer): """This layer computes the hinge loss on inputs:[labels,logits] labels: The values of this tensor is expected to be 1.0 or 0.0. The shape should be the same as logits. logits: Holds the log probabilities for labels, a float tensor. The output is a weighted loss tensor of same shape as labels. """ def __init__(self, in_layers=None, **kwargs): super(Hingeloss, self).__init__(in_layers, **kwargs) Loading
