Change DTNNTensorGraph to DTNNModel (b6a0cff5) · Commits · 钟慕尧 / deepchem

deepchem/models/init.py

+1 −1

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		@@ -17,7 +17,7 @@ from deepchem.models.tensorgraph.IRV import TensorflowMultiTaskIRVClassifier
		from deepchem.models.tensorgraph.robust_multitask import RobustMultitaskClassifier
		from deepchem.models.tensorgraph.robust_multitask import RobustMultitaskRegressor
		from deepchem.models.tensorgraph.progressive_multitask import ProgressiveMultitaskRegressor, ProgressiveMultitaskClassifier
		from deepchem.models.tensorgraph.models.graph_models import WeaveModel, DTNNTensorGraph, DAGTensorGraph, GraphConvModel, MPNNTensorGraph
		from deepchem.models.tensorgraph.models.graph_models import WeaveModel, DTNNModel, DAGTensorGraph, GraphConvModel, MPNNTensorGraph
		from deepchem.models.tensorgraph.models.symmetry_function_regression import BPSymmetryFunctionRegression, ANIRegression

		from deepchem.models.tensorgraph.models.seqtoseq import SeqToSeq

+3 −3

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		@@ -194,7 +194,7 @@ class WeaveModel(TensorGraph):
		return out


		class DTNNTensorGraph(TensorGraph):
		class DTNNModel(TensorGraph):

		def __init__(self,
		n_tasks,
		@@ -237,7 +237,7 @@ class DTNNTensorGraph(TensorGraph):
		self.steps = np.expand_dims(self.steps, 0)
		self.output_activation = output_activation
		self.mode = mode
		super(DTNNTensorGraph, self).__init__(**kwargs)
		super(DTNNModel, self).__init__(**kwargs)
		assert self.mode == "regression"
		self.build_graph()

		@@ -351,7 +351,7 @@ class DTNNTensorGraph(TensorGraph):
		if transformers != [] and not isinstance(outputs, collections.Sequence):
		raise ValueError(
		"DTNN does not support single tensor output with transformers")
		retval = super(DTNNTensorGraph, self).predict(dataset, outputs=outputs)
		retval = super(DTNNModel, self).predict(dataset, outputs=outputs)
		if not isinstance(outputs, collections.Sequence):
		return retval
		retval = np.concatenate(retval, axis=-1)

+1 −1

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		@@ -540,7 +540,7 @@ class TestOverfit(test_util.TensorFlowTestCase):
		n_tasks = y.shape[1]
		batch_size = 10

		model = dc.models.DTNNTensorGraph(
		model = dc.models.DTNNModel(
		n_tasks,
		n_embedding=20,
		n_distance=100,

+1 −1

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		@@ -524,7 +524,7 @@ def benchmark_regression(train_dataset,
		n_distance = hyper_parameters['n_distance']
		assert len(n_features) == 2, 'DTNN is only applicable to qm datasets'

		model = deepchem.models.DTNNTensorGraph(
		model = deepchem.models.DTNNModel(
		len(tasks),
		n_embedding=n_embedding,
		n_distance=n_distance,

+1 −1

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		@@ -29,7 +29,7 @@ distance_min = -1.
		distance_max = 9.2
		n_hidden = 15

		model = dc.models.DTNNTensorGraph(
		model = dc.models.DTNNModel(
		len(tasks),
		n_embedding=n_embedding,
		n_hidden=n_hidden,