Lazy Load Changes (e5d3678d) · Commits · 钟慕尧 / deepchem

.travis.yml

+1 −0

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		@@ -8,6 +8,7 @@ install:
		- if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then wget https://repo.continuum.io/archive/Anaconda2-4.3.0-Linux-x86_64.sh
		-O anaconda.sh; else wget https://repo.continuum.io/archive/Anaconda3-4.3.0-Linux-x86_64.sh
		-O anaconda.sh; fi
		- export python_version=$TRAVIS_PYTHON_VERSION
		- bash anaconda.sh -b -p $HOME/anaconda
		- export PATH="$HOME/anaconda/bin:$PATH"
		- hash -r

+4 −4

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		@@ -24,7 +24,7 @@ class SingletaskToMultitask(Model):
		"""

		def __init__(self, tasks, model_builder, model_dir=None, verbose=True):
		super().__init__(self, model_dir=model_dir, verbose=verbose)
		super(SingletaskToMultitask, self).__init__(self, model_dir=model_dir, verbose=verbose)
		self.tasks = tasks
		self.task_model_dirs = {}
		self.model_builder = model_builder

+2 −2

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		@@ -52,7 +52,7 @@ class Sequential(Model):
		"""

		def __init__(self, name=None, logdir=None):
		super().__init__(self, model_dir=logdir)
		super(Sequential, self).__init__(self, model_dir=logdir)
		self.layers = [] # stack of layers
		self.outputs = None # tensors (length 1)

+7 −7

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		@@ -192,7 +192,7 @@ class TensorflowGraphModel(Model):
		self.pad_batches = pad_batches
		self.seed = seed

		super().__init__(self, model_dir=logdir, verbose=verbose)
		super(TensorflowGraphModel, self).__init__(self, model_dir=logdir, verbose=verbose)

		# Guard variable to make sure we don't Restore() this model
		# from a disk checkpoint more than once.

+10 −10

Original line number	Diff line number	Diff line
		@@ -62,7 +62,7 @@ class TensorGraphMultiTaskClassifier(TensorGraph):
		n_classes: int
		the number of classes
		"""
		super().__init__(mode='classification', **kwargs)
		super(TensorGraphMultiTaskClassifier, self).__init__(mode='classification', **kwargs)
		self.n_tasks = n_tasks
		self.n_features = n_features
		self.n_classes = n_classes
		@@ -167,7 +167,7 @@ class TensorGraphMultiTaskRegressor(TensorGraph):
		dropouts: list
		the dropout probablity to use for each layer. The length of this list should equal len(layer_sizes).
		"""
		super().__init__(mode='regression', **kwargs)
		super(TensorGraphMultiTaskRegressor, self).__init__(mode='regression', **kwargs)
		self.n_tasks = n_tasks
		self.n_features = n_features

		@@ -296,7 +296,7 @@ class TensorGraphMultiTaskFitTransformRegressor(TensorGraphMultiTaskRegressor):
		X_b = transformer.X_transform(X_b)
		n_features = X_b.shape[1]
		print("n_features after fit_transform: %d" % int(n_features))
		super().__init__(n_tasks, n_features, batch_size=batch_size, **kwargs)
		super(TensorGraphMultiTaskFitTransformRegressor, self).__init__(n_tasks, n_features, batch_size=batch_size, **kwargs)

		def default_generator(self,
		dataset,
		@@ -332,7 +332,7 @@ class TensorGraphMultiTaskFitTransformRegressor(TensorGraphMultiTaskRegressor):
		feed_dict[self.features[0]] = X_t
		yield feed_dict

		return super().predict_proba_on_generator(transform_generator(),
		return super(TensorGraphMultiTaskFitTransformRegressor, self).predict_proba_on_generator(transform_generator(),
		transformers)