fixed model_ops tests (533d841b) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/tests/test_model_ops.py

+9 −8

Original line number	Diff line number	Diff line
		@@ -7,7 +7,10 @@ import numpy as np
		import tensorflow as tf
		from tensorflow.python.framework import test_util

		import deepchem as dc
		from deepchem.models.tensorgraph.model_ops import add_bias
		from deepchem.models.tensorgraph.model_ops import fully_connected_layer
		from deepchem.models.tensorgraph.model_ops import multitask_logits
		from deepchem.models.tensorgraph.model_ops import softmax_N

		test_random_seed = 20151102

		@@ -21,7 +24,7 @@ class TestModelOps(test_util.TensorFlowTestCase):
		def test_add_bias(self):
		with self.test_session() as sess:
		w_t = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], shape=[2, 3])
		w_biased_t = dc.nn.add_bias(w_t, init=tf.constant(5.0, shape=[3]))
		w_biased_t = add_bias(w_t, init=tf.constant(5.0, shape=[3]))
		sess.run(tf.global_variables_initializer())
		w, w_biased, bias = sess.run([w_t, w_biased_t] + tf.trainable_variables())
		self.assertAllEqual(w, [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
		@@ -32,7 +35,7 @@ class TestModelOps(test_util.TensorFlowTestCase):
		with self.test_session() as sess:
		features = np.random.random((128, 100))
		features_t = tf.constant(features, dtype=tf.float32)
		dense_t = dc.nn.fully_connected_layer(features_t, 50)
		dense_t = 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())
		@@ -44,7 +47,7 @@ class TestModelOps(test_util.TensorFlowTestCase):
		num_tasks = 3
		np.random.seed(test_random_seed)
		features = np.random.random((5, 100))
		logits_t = dc.nn.multitask_logits(
		logits_t = multitask_logits(
		tf.constant(features, dtype=tf.float32), num_tasks)
		sess.run(tf.global_variables_initializer())
		output = sess.run(tf.trainable_variables() + logits_t)
		@@ -60,14 +63,12 @@ class TestModelOps(test_util.TensorFlowTestCase):
		expected = np.asarray(
		[[[0.5, 0.5], [0.45, 0.55]], [[0.27, 0.73], [0.5, 0.5]]], dtype=float)
		with self.test_session() as sess:
		computed = sess.run(
		dc.nn.softmax_N(tf.constant(features, dtype=tf.float32)))
		computed = sess.run(softmax_N(tf.constant(features, dtype=tf.float32)))
		self.assertAllClose(np.around(computed, 2), expected)

		def test_softmax_N_with_numpy(self):
		features = np.random.random((2, 3, 4))
		expected = np.exp(features) / np.exp(features).sum(axis=-1, keepdims=True)
		with self.test_session() as sess:
		computed = sess.run(
		dc.nn.softmax_N(tf.constant(features, dtype=tf.float32)))
		computed = sess.run(softmax_N(tf.constant(features, dtype=tf.float32)))
		self.assertAllClose(computed, expected)

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