Created Log layer (73dd9b9b) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/layers.py

+21 −1

Original line number	Diff line number	Diff line
		@@ -301,7 +301,7 @@ class Dense(Layer):
		self.time_series = time_series
		try:
		parent_shape = self.in_layers[0].shape
		self._shape = (parent_shape[0], out_channels)
		self._shape = tuple(parent_shape[:-1] + [out_channels])
		except:
		pass
		self._reuse = False
		@@ -931,6 +931,26 @@ class Multiply(Layer):
		return out_tensor


		class Log(Layer):
		"""Compute the natural log of the input."""

		def __init__(self, in_layers=None, **kwargs):
		super(Log, self).__init__(in_layers, **kwargs)
		try:
		self._shape = in_layers[0].shape
		except:
		pass

		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		inputs = self._get_input_tensors(in_layers)
		if len(inputs) != 1:
		raise ValueError('Log must have a single parent')
		out_tensor = tf.log(inputs[0])
		if set_tensors:
		self.out_tensor = out_tensor
		return out_tensor


		class InteratomicL2Distances(Layer):
		"""Compute (squared) L2 Distances between atoms given neighbors."""

+1 −1

Original line number	Diff line number	Diff line
		@@ -167,7 +167,7 @@ class TensorGraph(Model):
		while True:
		yield {self._training_placeholder: 1.0}
		for d in feed_dict_generator:
		feed_dict = {k.out_tensor: v for k, v in six.iteritems(d)}
		feed_dict = dict(d)
		feed_dict[self._training_placeholder] = 1.0
		yield feed_dict

+8 −0

Original line number	Diff line number	Diff line
		@@ -26,6 +26,7 @@ from deepchem.models.tensorgraph.layers import Constant
		from deepchem.models.tensorgraph.layers import Variable
		from deepchem.models.tensorgraph.layers import Add
		from deepchem.models.tensorgraph.layers import Multiply
		from deepchem.models.tensorgraph.layers import Log
		from deepchem.models.tensorgraph.layers import InteratomicL2Distances
		from deepchem.models.tensorgraph.layers import SoftMaxCrossEntropy
		from deepchem.models.tensorgraph.layers import ReduceMean
		@@ -264,6 +265,13 @@ class TestLayers(test_util.TensorFlowTestCase):
		tf.constant(value3))
		assert np.array_equal(value1 * value2 * value3, out_tensor.eval())

		def test_log(self):
		"""Test that Log can be invoked."""
		value = np.random.uniform(size=(2, 3)).astype(np.float32)
		with self.test_session() as sess:
		result = Log()(value).eval()
		assert np.array_equal(np.log(value), result)

		def test_interatomic_distances(self):
		"""Test that the interatomic distance calculation works."""
		N_atoms = 5

+11 −1

Original line number	Diff line number	Diff line
		@@ -2,7 +2,7 @@ import numpy as np
		import tensorflow as tf
		from deepchem.models import TensorGraph
		from deepchem.models.tensorgraph.layers import Feature, Conv1D, Dense, Flatten, Reshape, Squeeze, Transpose, \
		CombineMeanStd, Repeat, Gather, GRU, L2Loss, Concat, SoftMax, Constant, Variable, Add, Multiply, InteratomicL2Distances, \
		CombineMeanStd, Repeat, Gather, GRU, L2Loss, Concat, SoftMax, Constant, Variable, Add, Multiply, Log, InteratomicL2Distances, \
		SoftMaxCrossEntropy, ReduceMean, ToFloat, ReduceSquareDifference, Conv2D, MaxPool, ReduceSum, GraphConv, GraphPool, \
		GraphGather, BatchNorm, WeightedError, \
		LSTMStep, AttnLSTMEmbedding, IterRefLSTMEmbedding
		@@ -163,6 +163,16 @@ def test_Variable_pickle():
		tg.save()


		def test_Log_pickle():
		tg = TensorGraph()
		feature = Feature(shape=(tg.batch_size, 1))
		layer = Log(feature)
		tg.add_output(layer)
		tg.set_loss(layer)
		tg.build()
		tg.save()


		def testInteratomicL2Distances():
		"""
		TODO(LESWING) what is ndim here?