Merge pull request #826 from peastman/operators (89a3ef4e) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/layers.py

+33 −0

Original line number	Diff line number	Diff line
		@@ -162,6 +162,39 @@ class Layer(object):
		else:
		return self.out_tensor

		def __add__(self, other):
		if not isinstance(other, Layer):
		other = Constant(other)
		return Add([self, other])

		def __radd__(self, other):
		if not isinstance(other, Layer):
		other = Constant(other)
		return Add([other, self])

		def __sub__(self, other):
		if not isinstance(other, Layer):
		other = Constant(other)
		return Add([self, other], weights=[1.0, -1.0])

		def __rsub__(self, other):
		if not isinstance(other, Layer):
		other = Constant(other)
		return Add([other, self], weights=[1.0, -1.0])

		def __mul__(self, other):
		if not isinstance(other, Layer):
		other = Constant(other)
		return Multiply([self, other])

		def __rmul__(self, other):
		if not isinstance(other, Layer):
		other = Constant(other)
		return Multiply([other, self])

		def __neg__(self):
		return Multiply([self, Constant(-1.0)])


		def _convert_layer_to_tensor(value, dtype=None, name=None, as_ref=False):
		return tf.convert_to_tensor(value.out_tensor, dtype=dtype, name=name)

deepchem/models/tensorgraph/tensor_graph.py

+6 −7

Original line number	Diff line number	Diff line
		@@ -291,16 +291,15 @@ class TensorGraph(Model):
		elif not isinstance(outputs, collections.Sequence):
		outputs = [outputs]
		with self._get_tf("Graph").as_default():
		out_tensors = [x.out_tensor for x in self.outputs]
		# Gather results for each output
		results = [[] for out in out_tensors]
		results = [[] for out in outputs]
		for feed_dict in generator:
		feed_dict = {
		self.layers[k.name].out_tensor: v
		for k, v in six.iteritems(feed_dict)
		}
		feed_dict[self._training_placeholder] = 0.0
		feed_results = self.session.run(out_tensors, feed_dict=feed_dict)
		feed_results = self.session.run(outputs, feed_dict=feed_dict)
		if len(feed_results) > 1:
		if len(transformers):
		raise ValueError("Does not support transformations "
		@@ -328,7 +327,7 @@ class TensorGraph(Model):
		"""
		return self.predict_on_generator(generator, transformers, outputs)

		def predict_on_batch(self, X, transformers=[]):
		def predict_on_batch(self, X, transformers=[], outputs=None):
		"""Generates predictions for input samples, processing samples in a batch.

		Parameters
		@@ -344,9 +343,9 @@ class TensorGraph(Model):
		"""
		dataset = NumpyDataset(X=X, y=None)
		generator = self.default_generator(dataset, predict=True, pad_batches=False)
		return self.predict_on_generator(generator, transformers)
		return self.predict_on_generator(generator, transformers, outputs)

		def predict_proba_on_batch(self, X, transformers=[]):
		def predict_proba_on_batch(self, X, transformers=[], outputs=None):
		"""Generates predictions for input samples, processing samples in a batch.

		Parameters
		@@ -360,7 +359,7 @@ class TensorGraph(Model):
		-------
		A Numpy array of predictions.
		"""
		return self.predict_on_batch(X, transformers)
		return self.predict_on_batch(X, transformers, outputs)

		def predict(self, dataset, transformers=[], outputs=None):
		"""

deepchem/models/tensorgraph/tests/test_tensor_graph.py

+34 −1

Original line number	Diff line number	Diff line
		@@ -9,7 +9,7 @@ import tensorflow as tf
		import deepchem as dc
		from deepchem.data import NumpyDataset
		from deepchem.data.datasets import Databag
		from deepchem.models.tensorgraph.layers import Dense, SoftMaxCrossEntropy, ReduceMean, SoftMax
		from deepchem.models.tensorgraph.layers import Dense, SoftMaxCrossEntropy, ReduceMean, SoftMax, Constant
		from deepchem.models.tensorgraph.layers import Feature, Label
		from deepchem.models.tensorgraph.layers import ReduceSquareDifference
		from deepchem.models.tensorgraph.tensor_graph import TensorGraph
		@@ -281,3 +281,36 @@ class TestTensorGraph(unittest.TestCase):
		epochs=1, batch_size=tg.batch_size, pad_batches=True))
		prediction = tg.predict_on_generator(databag.iterbatches())
		assert_true(np.all(np.isclose(prediction[0], prediction[1], atol=0.01)))

		def test_operators(self):
		"""Test math operators on Layers."""
		v1 = np.random.uniform(size=(2, 3)).astype(np.float32)
		v2 = np.random.uniform(size=(2, 3)).astype(np.float32)
		c1 = Constant(v1)
		c2 = Constant(v2)
		tg = dc.models.TensorGraph()
		tg.set_loss(c1)
		expected = []
		tg.add_output(c1 + c2)
		expected.append(v1 + v2)
		tg.add_output(c1 + v2)
		expected.append(v1 + v2)
		tg.add_output(1 + c2)
		expected.append(1 + v2)
		tg.add_output(c1 - c2)
		expected.append(v1 - v2)
		tg.add_output(c1 - v2)
		expected.append(v1 - v2)
		tg.add_output(1 - c2)
		expected.append(1 - v2)
		tg.add_output(c1 * c2)
		expected.append(v1 * v2)
		tg.add_output(c1 * v2)
		expected.append(v1 * v2)
		tg.add_output(2 * c2)
		expected.append(2 * v2)
		tg.add_output(-c1)
		expected.append(-v1)
		for o, e in zip(tg.outputs, expected):
		value = tg.predict_on_batch(np.array([0]), outputs=o)
		assert np.array_equal(e, value)

Admin message