Merge pull request #1118 from abster12/master (d5282b37) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/layers.py

+33 −0

Original line number	Diff line number	Diff line
		@@ -1542,6 +1542,12 @@ 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)
		@@ -4309,3 +4315,30 @@ class GraphCNN(Layer):
		result = tf.matmul(A_reshape, B)
		result = tf.reshape(result, tf.stack([A_shape[0], A_shape[1], axis_2]))
		return result


		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)
		try:
		self._shape = self.in_layers[1].shape
		except:
		pass

		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		inputs = self._get_input_tensors(in_layers)
		if len(inputs) != 2:
		raise ValueError()
		labels, logits = inputs[0], inputs[1]
		reduction = tf.losses.Reduction
		out_tensor = tf.losses.hinge_loss(
		labels=labels, logits=logits, reduction=reduction.NONE)
		if set_tensors:
		self.out_tensor = out_tensor
		return out_tensor

deepchem/models/tensorgraph/tests/test_layers.py

+27 −0

Original line number	Diff line number	Diff line
		@@ -25,6 +25,7 @@ from deepchem.models.tensorgraph.layers import GRU
		from deepchem.models.tensorgraph.layers import Gather
		from deepchem.models.tensorgraph.layers import GraphConv
		from deepchem.models.tensorgraph.layers import GraphGather
		from deepchem.models.tensorgraph.layers import Hingeloss
		from deepchem.models.tensorgraph.layers import Input
		from deepchem.models.tensorgraph.layers import InputFifoQueue
		from deepchem.models.tensorgraph.layers import InteratomicL2Distances
		@@ -45,6 +46,7 @@ from deepchem.models.tensorgraph.layers import Sigmoid
		from deepchem.models.tensorgraph.layers import SigmoidCrossEntropy
		from deepchem.models.tensorgraph.layers import SoftMax
		from deepchem.models.tensorgraph.layers import SoftMaxCrossEntropy
		from deepchem.models.tensorgraph.layers import SparseSoftMaxCrossEntropy
		from deepchem.models.tensorgraph.layers import StopGradient
		from deepchem.models.tensorgraph.layers import TensorWrapper
		from deepchem.models.tensorgraph.layers import TimeSeriesDense
		@@ -381,6 +383,18 @@ class TestLayers(test_util.TensorFlowTestCase):
		out_tensor = out_tensor.eval()
		assert out_tensor.shape == (batch_size,)

		def test_sparse_softmax_cross_entropy(self):
		batch_size = 10
		n_features = 5
		logit_tensor = np.random.rand(batch_size, n_features)
		label_tensor = np.random.rand(batch_size)
		with self.test_session() as sess:
		logit_tensor = tf.convert_to_tensor(logit_tensor, dtype=tf.float32)
		label_tensor = tf.convert_to_tensor(label_tensor, dtype=tf.int32)
		out_tensor = SparseSoftMaxCrossEntropy()(label_tensor, logit_tensor)
		out_tensor = out_tensor.eval()
		assert out_tensor.shape == (batch_size,)

		def test_reduce_mean(self):
		"""Test that ReduceMean can be invoked."""
		batch_size = 10
		@@ -875,3 +889,16 @@ class TestLayers(test_util.TensorFlowTestCase):
		assert out_tensor.shape == (batch_size, n_tasks)
		irv_reg = IRVRegularize(irv_layer, 1.)()
		assert irv_reg.eval() >= 0

		def test_hingeloss(self):

		labels = 1
		logits = 1
		logits_tensor = np.random.rand(logits)
		labels_tensor = np.random.rand(labels)
		with self.test_session() as sess:
		logits_tensor = tf.convert_to_tensor(logits_tensor, dtype=tf.float32)
		labels_tensor = tf.convert_to_tensor(labels_tensor, dtype=tf.float32)
		out_tensor = Hingeloss()(labels_tensor, logits_tensor)
		out_tensor = out_tensor.eval()
		assert out_tensor.shape == (labels,)

deepchem/models/tensorgraph/tests/test_layers_pickle.py

+22 −1

Original line number	Diff line number	Diff line
		@@ -10,7 +10,7 @@ from deepchem.models.tensorgraph.layers import Feature, Conv1D, Dense, Flatten,
		SoftMaxCrossEntropy, ReduceMean, ToFloat, ReduceSquareDifference, Conv2D, MaxPool2D, ReduceSum, GraphConv, GraphPool, \
		GraphGather, BatchNorm, WeightedError, ReLU, \
		Conv3D, MaxPool3D, Conv2DTranspose, Conv3DTranspose, \
		LSTMStep, AttnLSTMEmbedding, IterRefLSTMEmbedding, GraphEmbedPoolLayer, GraphCNN, Cast
		LSTMStep, AttnLSTMEmbedding, IterRefLSTMEmbedding, GraphEmbedPoolLayer, GraphCNN, Cast,Hingeloss,SparseSoftMaxCrossEntropy
		from deepchem.models.tensorgraph.symmetry_functions import AtomicDifferentiatedDense
		from deepchem.models.tensorgraph.IRV import IRVLayer, IRVRegularize, Slice

		@@ -269,6 +269,17 @@ def test_SoftmaxCrossEntropy_pickle():
		tg.save()


		def test_SparseSoftmaxCrossEntropy_pickle():
		tg = TensorGraph()
		logits = Feature(shape=(tg.batch_size, 5))
		labels = Feature(shape=(tg.batch_size,), dtype=tf.int32)
		layer = SparseSoftMaxCrossEntropy(in_layers=[labels, logits])
		tg.add_output(layer)
		tg.set_loss(layer)
		tg.build()
		tg.save()


		def test_SigmoidCrossEntropy_pickle():
		tg = TensorGraph()
		feature = Feature(shape=(tg.batch_size, 1))
		@@ -682,3 +693,13 @@ def test_Slice_pickle():
		tg.set_loss(out)
		tg.build()
		tg.save()


		def test_hingeloss_pickle():
		tg = TensorGraph()
		feature = Feature(shape=(1, None))
		layer = Hingeloss(in_layers=[feature, feature])
		tg.add_output(layer)
		tg.set_loss(layer)
		tg.build()
		tg.save()

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