Merge pull request #1059 from lilleswing/tf15 (fe11a1d0) · Commits · 钟慕尧 / deepchem

README.md

+1 −1

Original line number	Diff line number	Diff line
		@@ -59,7 +59,7 @@ git clone https://github.com/deepchem/deepchem.git # Clone deepchem source
		cd deepchem
		bash scripts/install_deepchem_conda.sh deepchem
		source activate deepchem
		yes \| pip install tensorflow-gpu==1.4.0 # If you want GPU support
		yes \| pip install tensorflow-gpu==1.5.0 # If you want GPU support
		python setup.py install # Manual install
		nosetests -a '!slow' -v deepchem --nologcapture # Run tests
		```

deepchem/models/tensorflow_models/tests/test_utils.py

+84 −100

Original line number	Diff line number	Diff line
		@@ -16,7 +16,6 @@

		import tempfile


		import numpy as np
		import scipy.stats
		import tensorflow as tf
		@@ -24,21 +23,19 @@ import tensorflow as tf
		from google.protobuf import text_format

		from tensorflow.python.framework import test_util
		from tensorflow.python.platform import flags
		from tensorflow.python.platform import googletest
		from tensorflow.python.training import checkpoint_state_pb2

		from deepchem.models.tensorflow_models import utils

		FLAGS = flags.FLAGS
		FLAGS.test_random_seed = 20151102
		test_random_seed = 20151102


		class UtilsTest(test_util.TensorFlowTestCase):

		def setUp(self):
		super(UtilsTest, self).setUp()
		np.random.seed(FLAGS.test_random_seed)
		np.random.seed(test_random_seed)

		def testParseCheckpoint(self):
		# parse CheckpointState proto
		@@ -78,85 +75,56 @@ class UtilsTest(test_util.TensorFlowTestCase):
		expected)

		def testMean(self):
		self.Check(utils.Mean,
		features=[0, 1],
		expected=0.5)
		self.Check(utils.Mean,
		features=[[0, 1],
		[2, 3]],
		expected=[0.5, 2.5],
		axis=1)
		self.Check(utils.Mean,
		features=[[[0, 1],
		[2, 3]],
		[[4, 5],
		[6, 7]]],
		self.Check(utils.Mean, features=[0, 1], expected=0.5)
		self.Check(
		utils.Mean, features=[[0, 1], [2, 3]], expected=[0.5, 2.5], axis=1)
		self.Check(
		utils.Mean,
		features=[[[0, 1], [2, 3]], [[4, 5], [6, 7]]],
		expected=[2.5, 4.5],
		axis=[0, 2])

		def testMeanWithMask(self):
		self.Check(utils.Mean,
		features=[[9999],
		[1],
		[2]],
		expected=1.5,
		mask=[0, 1, 1])
		self.Check(utils.Mean,
		features=[[0, 1],
		[9999, 9999]],
		self.Check(
		utils.Mean, features=[[9999], [1], [2]], expected=1.5, mask=[0, 1, 1])
		self.Check(
		utils.Mean,
		features=[[0, 1], [9999, 9999]],
		expected=[0, 1],
		axis=0,
		mask=[1, 0])
		self.Check(utils.Mean,
		features=[[[0, 1],
		[9999, 9999]],
		[[9999, 9999],
		[6, 7]]],
		self.Check(
		utils.Mean,
		features=[[[0, 1], [9999, 9999]], [[9999, 9999], [6, 7]]],
		expected=[0.5, 6.5],
		axis=[0, 2],
		mask=[[1, 0],
		[0, 1]])
		mask=[[1, 0], [0, 1]])

		def testVariance(self):
		self.Check(utils.Variance,
		features=[0, 1],
		expected=0.25)
		self.Check(utils.Variance,
		features=[[0, 2],
		[2, 3]],
		expected=[1, 0.25],
		axis=1)
		self.Check(utils.Variance,
		features=[[[0, 1],
		[2, 3]],
		[[4, 5],
		[6, 7]]],
		self.Check(utils.Variance, features=[0, 1], expected=0.25)
		self.Check(
		utils.Variance, features=[[0, 2], [2, 3]], expected=[1, 0.25], axis=1)
		self.Check(
		utils.Variance,
		features=[[[0, 1], [2, 3]], [[4, 5], [6, 7]]],
		expected=[4.25, 4.25],
		axis=[0, 2])

		def testVarianceWithMask(self):
		self.Check(utils.Variance,
		features=[[0],
		[1],
		[2]],
		expected=0.25,
		mask=[0, 1, 1])
		self.Check(utils.Variance,
		features=[[0, 2],
		[9999, 9999],
		[4, 4]],
		self.Check(
		utils.Variance, features=[[0], [1], [2]], expected=0.25, mask=[0, 1, 1])
		self.Check(
		utils.Variance,
		features=[[0, 2], [9999, 9999], [4, 4]],
		expected=[4, 1],
		axis=0,
		mask=[1, 0, 1])
		self.Check(utils.Variance,
		features=[[[0, 1],
		[9999, 9999]],
		[[9999, 9999],
		[6, 8]]],
		self.Check(
		utils.Variance,
		features=[[[0, 1], [9999, 9999]], [[9999, 9999], [6, 8]]],
		expected=[0.25, 1],
		axis=[0, 2],
		mask=[[1, 0],
		[0, 1]])
		mask=[[1, 0], [0, 1]])

		def testMoment(self):
		with self.test_session() as sess:
		@@ -169,50 +137,66 @@ class UtilsTest(test_util.TensorFlowTestCase):
		self.assertAllClose(
		sess.run(utils.Moment(k, features_t)[1]),
		scipy.stats.moment(features, k, axis=None),
		rtol=1e-5, atol=1e-5)
		rtol=1e-5,
		atol=1e-5)

		# standardized moments
		self.assertAllClose(
		sess.run(utils.Moment(k, features_t, standardize=True)[1]),
		np.divide(scipy.stats.moment(features, k, axis=None),
		np.divide(
		scipy.stats.moment(features, k, axis=None),
		np.power(features.std(), k)),
		rtol=1e-5, atol=1e-5)
		rtol=1e-5,
		atol=1e-5)

		# central across one axis
		self.assertAllClose(
		sess.run(utils.Moment(k, features_t, reduction_indices=1)[1]),
		scipy.stats.moment(features, k, axis=1),
		rtol=1e-5, atol=1e-5)
		rtol=1e-5,
		atol=1e-5)

		# standardized across one axis
		self.assertAllClose(
		sess.run(utils.Moment(k, features_t, standardize=True,
		reduction_indices=1)[1]),
		np.divide(scipy.stats.moment(features, k, axis=1),
		sess.run(
		utils.Moment(
		k, features_t, standardize=True, reduction_indices=1)[1]),
		np.divide(
		scipy.stats.moment(features, k, axis=1),
		np.power(features.std(axis=1), k)),
		rtol=1e-5, atol=1e-5)
		rtol=1e-5,
		atol=1e-5)

		def testSkewness(self):
		with self.test_session() as sess:
		features = np.random.random((3, 4, 5))
		features_t = tf.constant(features, dtype=tf.float32)
		self.assertAllClose(sess.run(utils.Skewness(features_t)),
		self.assertAllClose(
		sess.run(utils.Skewness(features_t)),
		scipy.stats.skew(features, axis=None),
		rtol=1e-5, atol=1e-5)
		self.assertAllClose(sess.run(utils.Skewness(features_t, 1)),
		rtol=1e-5,
		atol=1e-5)
		self.assertAllClose(
		sess.run(utils.Skewness(features_t, 1)),
		scipy.stats.skew(features, axis=1),
		rtol=1e-5, atol=1e-5)
		rtol=1e-5,
		atol=1e-5)

		def testKurtosis(self):
		with self.test_session() as sess:
		features = np.random.random((3, 4, 5))
		features_t = tf.constant(features, dtype=tf.float32)
		self.assertAllClose(sess.run(utils.Kurtosis(features_t)),
		self.assertAllClose(
		sess.run(utils.Kurtosis(features_t)),
		scipy.stats.kurtosis(features, axis=None),
		rtol=1e-5, atol=1e-5)
		self.assertAllClose(sess.run(utils.Kurtosis(features_t, 1)),
		rtol=1e-5,
		atol=1e-5)
		self.assertAllClose(
		sess.run(utils.Kurtosis(features_t, 1)),
		scipy.stats.kurtosis(features, axis=1),
		rtol=1e-5, atol=1e-5)
		rtol=1e-5,
		atol=1e-5)


		if __name__ == '__main__':
		googletest.main()

deepchem/nn/tests/test_model_ops.py

+6 −9

Original line number	Diff line number	Diff line
		"""Test ops for graph construction."""
		from __future__ import print_function
		from __future__ import division
		from __future__ import print_function
		from __future__ import unicode_literals

		import os
		import numpy as np
		import tensorflow as tf
		from tensorflow.python.framework import test_util

		import deepchem as dc
		from tensorflow.python.framework import test_util
		from tensorflow.python.platform import flags

		FLAGS = flags.FLAGS
		FLAGS.test_random_seed = 20151102
		test_random_seed = 20151102


		class TestModelOps(test_util.TensorFlowTestCase):
		@@ -37,15 +34,15 @@ class TestModelOps(test_util.TensorFlowTestCase):
		features_t = tf.constant(features, dtype=tf.float32)
		dense_t = dc.nn.fully_connected_layer(features_t, 50)
		sess.run(tf.global_variables_initializer())
		features, dense, w, b = sess.run([features_t, dense_t] +
		tf.trainable_variables())
		features, dense, w, b = sess.run(
		[features_t, dense_t] + tf.trainable_variables())
		expected = np.dot(features, w) + b
		self.assertAllClose(dense, expected)

		def test_multitask_logits(self):
		with self.test_session() as sess:
		num_tasks = 3
		np.random.seed(FLAGS.test_random_seed)
		np.random.seed(test_random_seed)
		features = np.random.random((5, 100))
		logits_t = dc.nn.multitask_logits(
		tf.constant(features, dtype=tf.float32), num_tasks)

scripts/install_deepchem_conda.sh

+1 −1

Original line number	Diff line number	Diff line
		@@ -36,7 +36,7 @@ conda install -y -q -c conda-forge keras=1.2.2
		conda install -y -q -c conda-forge networkx=1.11
		conda install -y -q -c conda-forge pillow=4.3.0
		conda install -y -q -c conda-forge pandas=0.22.0
		yes \| pip install $tensorflow==1.4.0
		yes \| pip install $tensorflow==1.5.0
		conda install -y -q -c conda-forge nose=1.3.7
		conda install -y -q -c conda-forge nose-timer=0.7.0
		conda install -y -q -c conda-forge flaky=3.3.0

Admin message