Merge pull request #794 from peastman/inlayers (84c49cff) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/layers.py

+38 −38

Original line number	Diff line number	Diff line
		@@ -365,8 +365,8 @@ class Dense(Layer):
		class Flatten(Layer):
		"""Flatten every dimension except the first"""

		def __init__(self, **kwargs):
		super(Flatten, self).__init__(**kwargs)
		def __init__(self, in_layers=None, **kwargs):
		super(Flatten, self).__init__(in_layers, **kwargs)
		try:
		parent_shape = self.in_layers[0].shape
		s = list(parent_shape[:2])
		@@ -425,9 +425,9 @@ class Reshape(Layer):

		class Squeeze(Layer):

		def __init__(self, squeeze_dims=None, **kwargs):
		def __init__(self, in_layers=None, squeeze_dims=None, **kwargs):
		self.squeeze_dims = squeeze_dims
		super(Squeeze, self).__init__(**kwargs)
		super(Squeeze, self).__init__(in_layers, **kwargs)
		try:
		parent_shape = self.in_layers[0].shape
		if squeeze_dims is None:
		@@ -472,8 +472,8 @@ class Transpose(Layer):

		class CombineMeanStd(Layer):

		def __init__(self, **kwargs):
		super(CombineMeanStd, self).__init__(**kwargs)
		def __init__(self, in_layers=None, **kwargs):
		super(CombineMeanStd, self).__init__(in_layers, **kwargs)
		try:
		self._shape = self.in_layers[0].shape
		except:
		@@ -650,8 +650,8 @@ class Weights(Input):

		class L1Loss(Layer):

		def __init__(self, **kwargs):
		super(L1Loss, self).__init__(**kwargs)
		def __init__(self, in_layers=None, **kwargs):
		super(L1Loss, self).__init__(in_layers, **kwargs)

		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		inputs = self._get_input_tensors(in_layers, True)
		@@ -665,8 +665,8 @@ class L1Loss(Layer):

		class L2Loss(Layer):

		def __init__(self, **kwargs):
		super(L2Loss, self).__init__(**kwargs)
		def __init__(self, in_layers=None, **kwargs):
		super(L2Loss, self).__init__(in_layers, **kwargs)
		try:
		shape1 = self.in_layers[0].shape
		shape2 = self.in_layers[1].shape
		@@ -689,8 +689,8 @@ class L2Loss(Layer):

		class SoftMax(Layer):

		def __init__(self, **kwargs):
		super(SoftMax, self).__init__(**kwargs)
		def __init__(self, in_layers=None, **kwargs):
		super(SoftMax, self).__init__(in_layers, **kwargs)
		try:
		self._shape = tuple(self.in_layers[0].shape)
		except:
		@@ -709,9 +709,9 @@ class SoftMax(Layer):

		class Concat(Layer):

		def __init__(self, axis=1, **kwargs):
		def __init__(self, in_layers=None, axis=1, **kwargs):
		self.axis = axis
		super(Concat, self).__init__(**kwargs)
		super(Concat, self).__init__(in_layers, **kwargs)
		try:
		s = list(self.in_layers[0].shape)
		for parent in self.in_layers[1:]:
		@@ -734,9 +734,9 @@ class Concat(Layer):

		class Stack(Layer):

		def __init__(self, axis=1, **kwargs):
		def __init__(self, in_layers=None, axis=1, **kwargs):
		self.axis = axis
		super(Stack, self).__init__(**kwargs)
		super(Stack, self).__init__(in_layers, **kwargs)
		try:
		s = list(self.in_layers[0].shape)
		s.insert(axis, len(self.in_layers))
		@@ -810,7 +810,7 @@ class Variable(Layer):
		class Add(Layer):
		"""Compute the (optionally weighted) sum of the input layers."""

		def __init__(self, weights=None, **kwargs):
		def __init__(self, in_layers=None, weights=None, **kwargs):
		"""Create an Add layer.

		Parameters
		@@ -819,7 +819,7 @@ class Add(Layer):
		an array of length equal to the number of input layers, giving the weight
		to multiply each input by. If None, all weights are set to 1.
		"""
		super(Add, self).__init__(**kwargs)
		super(Add, self).__init__(in_layers, **kwargs)
		self.weights = weights
		try:
		shape1 = list(self.in_layers[0].shape)
		@@ -854,8 +854,8 @@ class Add(Layer):
		class Multiply(Layer):
		"""Compute the product of the input layers."""

		def __init__(self, **kwargs):
		super(Multiply, self).__init__(**kwargs)
		def __init__(self, in_layers=None, **kwargs):
		super(Multiply, self).__init__(in_layers, **kwargs)
		try:
		shape1 = list(self.in_layers[0].shape)
		shape2 = list(self.in_layers[1].shape)
		@@ -908,8 +908,8 @@ class InteratomicL2Distances(Layer):

		class SparseSoftMaxCrossEntropy(Layer):

		def __init__(self, **kwargs):
		super(SparseSoftMaxCrossEntropy, self).__init__(**kwargs)
		def __init__(self, in_layers=None, **kwargs):
		super(SparseSoftMaxCrossEntropy, self).__init__(in_layers, **kwargs)
		try:
		self._shape = (self.in_layers[1].shape[0], 1)
		except:
		@@ -930,8 +930,8 @@ class SparseSoftMaxCrossEntropy(Layer):

		class SoftMaxCrossEntropy(Layer):

		def __init__(self, **kwargs):
		super(SoftMaxCrossEntropy, self).__init__(**kwargs)
		def __init__(self, in_layers=None, **kwargs):
		super(SoftMaxCrossEntropy, self).__init__(in_layers, **kwargs)
		try:
		self._shape = (self.in_layers[1].shape[0], 1)
		except:
		@@ -952,9 +952,9 @@ class SoftMaxCrossEntropy(Layer):

		class ReduceMean(Layer):

		def __init__(self, axis=None, **kwargs):
		def __init__(self, in_layers=None, axis=None, **kwargs):
		self.axis = axis
		super(ReduceMean, self).__init__(**kwargs)
		super(ReduceMean, self).__init__(in_layers, **kwargs)
		if axis is None:
		self._shape = tuple()
		else:
		@@ -981,8 +981,8 @@ class ReduceMean(Layer):

		class ToFloat(Layer):

		def __init__(self, **kwargs):
		super(ToFloat, self).__init__(**kwargs)
		def __init__(self, in_layers=None, **kwargs):
		super(ToFloat, self).__init__(in_layers, **kwargs)
		try:
		self._shape = tuple(self.in_layers[0].shape)
		except:
		@@ -1000,9 +1000,9 @@ class ToFloat(Layer):

		class ReduceSum(Layer):

		def __init__(self, axis=None, **kwargs):
		def __init__(self, in_layers=None, axis=None, **kwargs):
		self.axis = axis
		super(ReduceSum, self).__init__(**kwargs)
		super(ReduceSum, self).__init__(in_layers, **kwargs)
		if axis is None:
		self._shape = tuple()
		else:
		@@ -1029,9 +1029,9 @@ class ReduceSum(Layer):

		class ReduceSquareDifference(Layer):

		def __init__(self, axis=None, **kwargs):
		def __init__(self, in_layers=None, axis=None, **kwargs):
		self.axis = axis
		super(ReduceSquareDifference, self).__init__(**kwargs)
		super(ReduceSquareDifference, self).__init__(in_layers, **kwargs)
		if axis is None:
		self._shape = tuple()
		else:
		@@ -1792,8 +1792,8 @@ class IterRefLSTMEmbedding(Layer):

		class BatchNorm(Layer):

		def __init__(self, **kwargs):
		super(BatchNorm, self).__init__(**kwargs)
		def __init__(self, in_layers=None, **kwargs):
		super(BatchNorm, self).__init__(in_layers, **kwargs)
		try:
		parent_shape = self.in_layers[0].shape
		self._shape = tuple(self.in_layers[0].shape)
		@@ -1855,8 +1855,8 @@ class BatchNormalization(Layer):

		class WeightedError(Layer):

		def __init__(self, **kwargs):
		super(WeightedError, self).__init__(**kwargs)
		def __init__(self, in_layers=None, **kwargs):
		super(WeightedError, self).__init__(in_layers, **kwargs)
		self._shape = tuple()

		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		@@ -1991,9 +1991,9 @@ class VinaFreeEnergy(Layer):
		class WeightedLinearCombo(Layer):
		"""Computes a weighted linear combination of input layers, with the weights defined by trainable variables."""

		def __init__(self, std=.3, **kwargs):
		def __init__(self, in_layers=None, std=.3, **kwargs):
		self.std = std
		super(WeightedLinearCombo, self).__init__(**kwargs)
		super(WeightedLinearCombo, self).__init__(in_layers, **kwargs)
		try:
		self._shape = tuple(self.in_layers[0].shape)
		except:

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