def test_vs_tf_onehot(self):
with self.test_session():
labels = tf.constant([1, 2, 3, 0], dtype=tf.int64, name='labels')
tf_one_hot = tf.one_hot(labels, depth=4)
niftynet_one_hot = tf.sparse_tensor_to_dense(
labels_to_one_hot(labels, 4))
self.assertAllEqual(tf_one_hot.eval(), niftynet_one_hot.eval())
def test_one_hot(self):
ref = np.asarray([[[0., 1., 0., 0., 0.], [0., 0., 1., 0., 0.]],
[[0., 0., 0., 1., 0.], [0., 0., 0., 0., 1.]]],
dtype=np.float32)
with self.test_session():
labels = tf.constant([[1, 2], [3, 4]])
# import pdb; pdb.set_trace()
one_hot = tf.sparse_tensor_to_dense(labels_to_one_hot(labels,
5)).eval()
self.assertAllEqual(one_hot, ref)
def test_one_hot(self):
ref = np.asarray(
[[[ 0., 1., 0., 0., 0.], [ 0., 0., 1., 0., 0.]],
[[ 0., 0., 0., 1., 0.], [ 0., 0., 0., 0., 1.]]],
dtype=np.float32)
with self.test_session():
labels = tf.constant([[1, 2], [3, 4]])
#import pdb; pdb.set_trace()
one_hot = tf.sparse_tensor_to_dense(
labels_to_one_hot(labels, 5)).eval()
self.assertAllEqual(one_hot, ref)
def test_cross_entropy_value(self):
# test value is -0.5 * [1 * log(e / (1+e)) + 1 * log(e^2 / (e^2 + 1))]
with self.cached_session():
predicted = tf.constant(
[[0, 1], [2, 0]],
dtype=tf.float32, name='predicted')
labels = tf.constant([1, 0], dtype=tf.int64, name='labels')
predicted, labels = [tf.expand_dims(x, axis=0) for x in (predicted, labels)]
test_loss_func = LossFunction(2, loss_type='CrossEntropy')
computed_cross_entropy = test_loss_func(predicted, labels)
self.assertAlmostEqual(
computed_cross_entropy.eval(),
-.5 * (np.log(np.e / (1 + np.e)) + np.log(
np.e ** 2 / (1 + np.e ** 2))))
test_dense_loss = LossFunction(2, loss_type='CrossEntropy_Dense')
labels = tf.sparse_tensor_to_dense(labels_to_one_hot(labels, 2))
computed_cross_entropy = test_loss_func(predicted, tf.to_int32(labels))
self.assertAlmostEqual(
computed_cross_entropy.eval(),
-.5 * (np.log(np.e / (1 + np.e)) + np.log(
np.e ** 2 / (1 + np.e ** 2))))
def test_cross_entropy_value(self):
# test value is -0.5 * [1 * log(e / (1+e)) + 1 * log(e^2 / (e^2 + 1))]
with self.test_session():
predicted = tf.constant(
[[0, 1], [2, 0]],
dtype=tf.float32, name='predicted')
labels = tf.constant([1, 0], dtype=tf.int64, name='labels')
predicted, labels = [tf.expand_dims(x, axis=0) for x in (predicted, labels)]
test_loss_func = LossFunction(2, loss_type='CrossEntropy')
computed_cross_entropy = test_loss_func(predicted, labels)
self.assertAlmostEqual(
computed_cross_entropy.eval(),
-.5 * (np.log(np.e / (1 + np.e)) + np.log(
np.e ** 2 / (1 + np.e ** 2))))
test_dense_loss = LossFunction(2, loss_type='CrossEntropy_Dense')
labels = tf.sparse_tensor_to_dense(labels_to_one_hot(labels, 2))
computed_cross_entropy = test_loss_func(predicted, tf.to_int32(labels))
self.assertAlmostEqual(
computed_cross_entropy.eval(),
-.5 * (np.log(np.e / (1 + np.e)) + np.log(
np.e ** 2 / (1 + np.e ** 2))))
def test_vs_tf_onehot(self):
with self.test_session():
labels = tf.constant([1, 2, 3, 0], dtype=tf.int64, name='labels')
tf_one_hot = tf.one_hot(labels, depth=4)
niftynet_one_hot = tf.sparse_tensor_to_dense(labels_to_one_hot(labels, 4))
self.assertAllEqual(tf_one_hot.eval(), niftynet_one_hot.eval())
