def test_binary_crossentropy(self):
y_pred = tf.constant([[0.5, 0.0, 1.0], [2.0, 0.0, -1.0]])
y_pred_2 = tf.constant([[0.5, 2.0, 1.0], [2.0, 2.0, -1.0]])
y_true = tf.constant([[1.0, MAGIC_NUMBER, 1.0],
[1.0, MAGIC_NUMBER, 0.0]])
y_pred_sigmoid = tf.nn.sigmoid(y_pred)
y_pred_2_sigmoid = tf.nn.sigmoid(y_pred_2)
# Truth with Magic number is wrong
npt.assert_array_almost_equal(
binary_crossentropy(y_true, y_pred_sigmoid).numpy(),
binary_crossentropy(y_true, y_pred, from_logits=True).numpy(),
decimal=3,
)
# make sure neural network prediction won't matter for magic number term
npt.assert_array_almost_equal(
binary_crossentropy(y_true, y_pred_2, from_logits=True).numpy(),
binary_crossentropy(y_true, y_pred, from_logits=True).numpy(),
decimal=3,
)
npt.assert_array_almost_equal(
binary_crossentropy(y_true, y_pred_sigmoid).numpy(),
binary_crossentropy(y_true, y_pred_2_sigmoid).numpy(),
decimal=3,
)
def test_binary_crossentropy(self):
y_pred = tf.Variable([[0.5, 0., 1.], [2., 0., -1.]])
y_pred_2 = tf.Variable([[0.5, 2., 1.], [2., 2., -1.]])
y_true = tf.Variable([[1., MAGIC_NUMBER, 1.], [1., MAGIC_NUMBER, 0.]])
y_pred_sigmoid = tf.nn.sigmoid(y_pred)
y_pred_2_sigmoid = tf.nn.sigmoid(y_pred_2)
# Truth with Magic number is wrong
npt.assert_array_almost_equal(
binary_crossentropy(y_true,
y_pred_sigmoid).eval(session=get_session()),
binary_crossentropy(y_true, y_pred,
from_logits=True).eval(session=get_session()),
decimal=3)
# make sure neural network prediction won't matter for magic number term
npt.assert_array_almost_equal(
binary_crossentropy(y_true, y_pred_2,
from_logits=True).eval(session=get_session()),
binary_crossentropy(y_true, y_pred,
from_logits=True).eval(session=get_session()),
decimal=3)
npt.assert_array_almost_equal(
binary_crossentropy(y_true,
y_pred_sigmoid).eval(session=get_session()),
binary_crossentropy(y_true,
y_pred_2_sigmoid).eval(session=get_session()),
decimal=3)
def test_binary_crossentropy(self):
with tf.device("/cpu:0"), context.eager_mode():
y_pred = tf.constant([[0.5, 0., 1.], [2., 0., -1.]])
y_pred_2 = tf.constant([[0.5, 2., 1.], [2., 2., -1.]])
y_true = tf.constant([[1., MAGIC_NUMBER, 1.],
[1., MAGIC_NUMBER, 0.]])
y_pred_sigmoid = tf.nn.sigmoid(y_pred)
y_pred_2_sigmoid = tf.nn.sigmoid(y_pred_2)
# Truth with Magic number is wrong
npt.assert_array_almost_equal(
binary_crossentropy(y_true, y_pred_sigmoid).numpy(),
binary_crossentropy(y_true, y_pred, from_logits=True).numpy(),
decimal=3)
# make sure neural network prediction won't matter for magic number term
npt.assert_array_almost_equal(
binary_crossentropy(y_true, y_pred_2,
from_logits=True).numpy(),
binary_crossentropy(y_true, y_pred, from_logits=True).numpy(),
decimal=3)
npt.assert_array_almost_equal(
binary_crossentropy(y_true, y_pred_sigmoid).numpy(),
binary_crossentropy(y_true, y_pred_2_sigmoid).numpy(),
decimal=3)
