Loading deepchem/models/tensorflow_models/fcnet.py +9 −1 Original line number Diff line number Diff line Loading @@ -20,7 +20,7 @@ from deepchem.metrics import to_one_hot from deepchem.models.tensorgraph.tensor_graph import TensorGraph, TFWrapper from deepchem.models.tensorgraph.layers import Feature, Label, Weights, WeightedError, Dense, Dropout, Reshape, SoftMaxCrossEntropy, L2Loss from deepchem.models.tensorgraph.layers import Feature, Label, Weights, WeightedError, Dense, Dropout, WeightDecay, Reshape, SoftMaxCrossEntropy, L2Loss class TensorGraphMultiTaskClassifier(TensorGraph): def __init__(self, Loading @@ -29,6 +29,8 @@ class TensorGraphMultiTaskClassifier(TensorGraph): layer_sizes=[1000], weight_init_stddevs=[0.02], bias_init_consts=[1.0], weight_decay_penalty=0.0, weight_decay_penalty_type="l2", dropouts=[0.5], n_classes=2, **kwargs): Loading Loading @@ -60,6 +62,8 @@ class TensorGraphMultiTaskClassifier(TensorGraph): weights = Weights(shape=(None, n_tasks)) loss = Reshape(shape=(-1, n_tasks), in_layers=[SoftMaxCrossEntropy(in_layers=[labels, output])]) weighted_loss = WeightedError(in_layers=[loss, weights]) if weight_decay_penalty != 0.0: weighted_loss = WeightDecay(weight_decay_penalty, weight_decay_penalty_type, in_layers=[weighted_loss]) self.set_loss(weighted_loss) Loading Loading @@ -92,6 +96,8 @@ class TensorGraphMultiTaskRegressor(TensorGraph): layer_sizes=[1000], weight_init_stddevs=[0.02], bias_init_consts=[1.0], weight_decay_penalty=0.0, weight_decay_penalty_type="l2", dropouts=[0.5], **kwargs): super().__init__(mode='regression', **kwargs) Loading Loading @@ -121,6 +127,8 @@ class TensorGraphMultiTaskRegressor(TensorGraph): weights = Weights(shape=(None, n_tasks)) loss = Reshape(shape=(-1, n_tasks), in_layers=[L2Loss(in_layers=[labels, output])]) weighted_loss = WeightedError(in_layers=[loss, weights]) if weight_decay_penalty != 0.0: weighted_loss = WeightDecay(weight_decay_penalty, weight_decay_penalty_type, in_layers=[weighted_loss]) self.set_loss(weighted_loss) Loading deepchem/models/tensorgraph/layers.py +31 −0 Original line number Diff line number Diff line Loading @@ -1303,6 +1303,37 @@ class Dropout(Layer): return self.out_tensor class WeightDecay(Layer): """Apply a weight decay penalty. The input should be the loss value. This layer adds a weight decay penalty to it and outputs the sum. """ def __init__(self, penalty, penalty_type, **kwargs): """Create a weight decay penalty layer. Parameters ---------- penalty: float magnitude of the penalty term penalty_type: str type of penalty to compute, either 'l1' or 'l2' """ self.penalty = penalty self.penalty_type = penalty_type super(WeightDecay, self).__init__(**kwargs) def create_tensor(self, in_layers=None, **kwargs): if in_layers is None: in_layers = self.in_layers in_layers = convert_to_layers(in_layers) parent_tensor = in_layers[0].out_tensor self.out_tensor = parent_tensor+model_ops.weight_decay(self.penalty_type, self.penalty) return self.out_tensor class AtomicConvolution(Layer): def __init__(self, Loading Loading
deepchem/models/tensorflow_models/fcnet.py +9 −1 Original line number Diff line number Diff line Loading @@ -20,7 +20,7 @@ from deepchem.metrics import to_one_hot from deepchem.models.tensorgraph.tensor_graph import TensorGraph, TFWrapper from deepchem.models.tensorgraph.layers import Feature, Label, Weights, WeightedError, Dense, Dropout, Reshape, SoftMaxCrossEntropy, L2Loss from deepchem.models.tensorgraph.layers import Feature, Label, Weights, WeightedError, Dense, Dropout, WeightDecay, Reshape, SoftMaxCrossEntropy, L2Loss class TensorGraphMultiTaskClassifier(TensorGraph): def __init__(self, Loading @@ -29,6 +29,8 @@ class TensorGraphMultiTaskClassifier(TensorGraph): layer_sizes=[1000], weight_init_stddevs=[0.02], bias_init_consts=[1.0], weight_decay_penalty=0.0, weight_decay_penalty_type="l2", dropouts=[0.5], n_classes=2, **kwargs): Loading Loading @@ -60,6 +62,8 @@ class TensorGraphMultiTaskClassifier(TensorGraph): weights = Weights(shape=(None, n_tasks)) loss = Reshape(shape=(-1, n_tasks), in_layers=[SoftMaxCrossEntropy(in_layers=[labels, output])]) weighted_loss = WeightedError(in_layers=[loss, weights]) if weight_decay_penalty != 0.0: weighted_loss = WeightDecay(weight_decay_penalty, weight_decay_penalty_type, in_layers=[weighted_loss]) self.set_loss(weighted_loss) Loading Loading @@ -92,6 +96,8 @@ class TensorGraphMultiTaskRegressor(TensorGraph): layer_sizes=[1000], weight_init_stddevs=[0.02], bias_init_consts=[1.0], weight_decay_penalty=0.0, weight_decay_penalty_type="l2", dropouts=[0.5], **kwargs): super().__init__(mode='regression', **kwargs) Loading Loading @@ -121,6 +127,8 @@ class TensorGraphMultiTaskRegressor(TensorGraph): weights = Weights(shape=(None, n_tasks)) loss = Reshape(shape=(-1, n_tasks), in_layers=[L2Loss(in_layers=[labels, output])]) weighted_loss = WeightedError(in_layers=[loss, weights]) if weight_decay_penalty != 0.0: weighted_loss = WeightDecay(weight_decay_penalty, weight_decay_penalty_type, in_layers=[weighted_loss]) self.set_loss(weighted_loss) Loading
deepchem/models/tensorgraph/layers.py +31 −0 Original line number Diff line number Diff line Loading @@ -1303,6 +1303,37 @@ class Dropout(Layer): return self.out_tensor class WeightDecay(Layer): """Apply a weight decay penalty. The input should be the loss value. This layer adds a weight decay penalty to it and outputs the sum. """ def __init__(self, penalty, penalty_type, **kwargs): """Create a weight decay penalty layer. Parameters ---------- penalty: float magnitude of the penalty term penalty_type: str type of penalty to compute, either 'l1' or 'l2' """ self.penalty = penalty self.penalty_type = penalty_type super(WeightDecay, self).__init__(**kwargs) def create_tensor(self, in_layers=None, **kwargs): if in_layers is None: in_layers = self.in_layers in_layers = convert_to_layers(in_layers) parent_tensor = in_layers[0].out_tensor self.out_tensor = parent_tensor+model_ops.weight_decay(self.penalty_type, self.penalty) return self.out_tensor class AtomicConvolution(Layer): def __init__(self, Loading
