def test_add_loss_step(self):
with tf.Graph().as_default():
# Given
logits = tf.placeholder(tf.float32, [2, 2], name='logits')
labels = tf.placeholder(tf.float32, [2, 2], name='labels')
# When
cross_entropy = graph.add_loss_step(logits, labels)
# Then
self.assertIsNotNone(cross_entropy)
self.assertEqual(type(cross_entropy).__name__, "Tensor")
def test_run_loss(self):
with tf.Graph().as_default():
with tf.Session() as sess:
# Given
logits = tf.Variable([[np.exp(0.1), np.exp(0.9)], [np.exp(0.9), np.exp(0.1)]], dtype=tf.float32)
labels = tf.Variable([[1.0, 0.0], [1.0, 0.0]], dtype=tf.float32)
# When
cross_entropy = graph.add_loss_step(logits, labels)
# Then
tf.initialize_all_variables().run()
self.assertEqual(sess.run(cross_entropy), -0.25)
