def test_categorical_crossentropy(self):
y_pred = tf.Variable([[1., 0., 1.], [2., 1., 0.]])
y_true = tf.Variable([[1., MAGIC_NUMBER, 1.], [1., MAGIC_NUMBER, 0.]])
y_pred_softmax = tf.nn.softmax(y_pred)
# Truth with Magic number is wrong
npt.assert_array_almost_equal(categorical_crossentropy(y_true, y_pred_softmax).eval(session=get_session()),
categorical_crossentropy(y_true, y_pred, from_logits=True).eval(
session=get_session()), decimal=3)
def test_categorical_crossentropy(self):
# Truth with Magic number is wrong
y_pred = tf.constant([[1., 0., 1.], [2., 1., 0.]])
y_true = tf.constant([[1., MAGIC_NUMBER, 1.], [1., MAGIC_NUMBER, 0.]])
y_pred_softmax = tf.nn.softmax(y_pred)
npt.assert_array_almost_equal(categorical_crossentropy(y_true, y_pred_softmax).numpy(),
categorical_crossentropy(y_true, y_pred, from_logits=True).numpy(), decimal=3)