def test_minibatch_correct(self):
# Reorded minibatches should have the same value.
xent1 = util.mnist_cross_entropy(
tf.concat([real_digit(), real_digit(), fake_digit()], 0),
tf.concat([one_hot_real(), one_hot1(), one_hot1()], 0))
xent2 = util.mnist_cross_entropy(
tf.concat([real_digit(), fake_digit(), real_digit()], 0),
tf.concat([one_hot_real(), one_hot1(), one_hot1()], 0))
with self.test_session():
self.assertNear(6.972539, xent1.eval(), 1e-5)
self.assertNear(xent1.eval(), xent2.eval(), 1e-5)
def test_minibatch_correct(self):
# Reorded minibatches should have the same value.
xent1 = util.mnist_cross_entropy(
tf.concat([real_digit(), real_digit(), fake_digit()], 0),
tf.concat([one_hot_real(), one_hot1(), one_hot1()], 0))
xent2 = util.mnist_cross_entropy(
tf.concat([real_digit(), fake_digit(), real_digit()], 0),
tf.concat([one_hot_real(), one_hot1(), one_hot1()], 0))
with self.test_session():
self.assertNear(6.972539, xent1.eval(), 1e-5)
self.assertNear(xent1.eval(), xent2.eval(), 1e-5)
def test_single_example_correct(self):
# The correct label should have low cross entropy.
correct_xent = util.mnist_cross_entropy(real_digit(), one_hot_real())
# The incorrect label should have high cross entropy.
wrong_xent = util.mnist_cross_entropy(real_digit(), one_hot1())
# A random digit should have medium cross entropy for any label.
fake_xent1 = util.mnist_cross_entropy(fake_digit(), one_hot_real())
fake_xent6 = util.mnist_cross_entropy(fake_digit(), one_hot1())
with self.test_session():
self.assertNear(0.00996, correct_xent.eval(), 1e-5)
self.assertNear(18.63073, wrong_xent.eval(), 1e-5)
self.assertNear(2.2, fake_xent1.eval(), 1e-1)
self.assertNear(2.2, fake_xent6.eval(), 1e-1)
def test_single_example_correct(self):
# The correct label should have low cross entropy.
correct_xent = util.mnist_cross_entropy(real_digit(), one_hot_real())
# The incorrect label should have high cross entropy.
wrong_xent = util.mnist_cross_entropy(real_digit(), one_hot1())
# A random digit should have medium cross entropy for any label.
fake_xent1 = util.mnist_cross_entropy(fake_digit(), one_hot_real())
fake_xent6 = util.mnist_cross_entropy(fake_digit(), one_hot1())
with self.test_session():
self.assertNear(0.00996, correct_xent.eval(), 1e-5)
self.assertNear(18.63073, wrong_xent.eval(), 1e-5)
self.assertNear(2.2, fake_xent1.eval(), 1e-1)
self.assertNear(2.2, fake_xent6.eval(), 1e-1)
def test_deterministic(self):
xent = util.mnist_cross_entropy(real_digit(), one_hot_real())
with self.test_session():
ent1 = xent.eval()
ent2 = xent.eval()
self.assertEqual(ent1, ent2)
with self.test_session():
ent3 = xent.eval()
self.assertEqual(ent1, ent3)
def test_deterministic(self):
xent = util.mnist_cross_entropy(real_digit(), one_hot_real())
with self.test_session():
ent1 = xent.eval()
ent2 = xent.eval()
self.assertEqual(ent1, ent2)
with self.test_session():
ent3 = xent.eval()
self.assertEqual(ent1, ent3)
def test_any_batch_size(self):
num_classes = 10
one_label = np.array([[1] + [0] * (num_classes - 1)])
inputs = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
one_hot_label = tf.placeholder(tf.int32, shape=[None, num_classes])
entropy = util.mnist_cross_entropy(inputs, one_hot_label)
for batch_size in [4, 16, 30]:
with self.test_session() as sess:
sess.run(entropy, feed_dict={
inputs: np.zeros([batch_size, 28, 28, 1]),
one_hot_label: np.concatenate([one_label] * batch_size)})
def test_any_batch_size(self):
num_classes = 10
one_label = np.array([[1] + [0] * (num_classes - 1)])
inputs = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
one_hot_label = tf.placeholder(tf.int32, shape=[None, num_classes])
entropy = util.mnist_cross_entropy(inputs, one_hot_label)
for batch_size in [4, 16, 30]:
with self.test_session() as sess:
sess.run(entropy, feed_dict={
inputs: np.zeros([batch_size, 28, 28, 1]),
one_hot_label: np.concatenate([one_label] * batch_size)})