def categorical_crossentropy(y_true, y_pred, from_logits=False):
"""
Categorical cross-entropy between an output tensor and a target tensor, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(tf.Tensor, tf.Variable)
:param y_pred: Prediction
:type y_pred: Union(tf.Tensor, tf.Variable)
:param from_logits: From logits space or not. If you want to use logits, please use from_logits=True
:type from_logits: boolean
:return: Categorical Cross-Entropy
:rtype: tf.Tensor
:History: 2018-Jan-14 - Written - Henry Leung (University of Toronto)
"""
# calculate correction term first
correction = magic_correction_term(y_true)
# Deal with magic number
y_true = tf.where(tf.equal(y_true, MAGIC_NUMBER), tf.zeros_like(y_true),
y_true)
# Note: tf.nn.softmax_cross_entropy_with_logits_v2 expects logits, we expects probabilities by default.
if not from_logits:
epsilon_tensor = tf.cast(tf.constant(keras.backend.epsilon()),
tf.float32)
# scale preds so that the class probas of each sample sum to 1
y_pred /= tf.reduce_sum(y_pred, len(y_pred.get_shape()) - 1, True)
# manual computation of crossentropy
y_pred = tf.clip_by_value(y_pred, epsilon_tensor, 1. - epsilon_tensor)
return -tf.reduce_sum(y_true * tf.log(y_pred),
len(y_pred.get_shape()) - 1) * correction
else:
return tf.nn.softmax_cross_entropy_with_logits_v2(
labels=y_true, logits=y_pred) * correction
def test_loss_magic(self):
# =============Magic correction term============= #
with tf.device("/cpu:0"), context.eager_mode():
y_true = tf.constant([[2., MAGIC_NUMBER, MAGIC_NUMBER],
[2., MAGIC_NUMBER, 4.]])
npt.assert_array_equal(
magic_correction_term(y_true).numpy(), [3., 1.5])
def binary_crossentropy(y_true, y_pred, from_logits=False):
"""
Binary cross-entropy between an output tensor and a target tensor, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(tf.Tensor, tf.Variable)
:param y_pred: Prediction
:type y_pred: Union(tf.Tensor, tf.Variable)
:param from_logits: From logits space or not. If you want to use logits, please use from_logits=True
:type from_logits: boolean
:return: Binary Cross-Entropy
:rtype: tf.Tensor
:History: 2018-Jan-14 - Written - Henry Leung (University of Toronto)
"""
# Note: tf.nn.sigmoid_cross_entropy_with_logits expects logits, we expects probabilities by default.
if not from_logits:
epsilon_tensor = tf.cast(tf.constant(keras.backend.epsilon()),
tf.float32)
# transform back to logits
y_pred = tf.clip_by_value(y_pred, epsilon_tensor, 1. - epsilon_tensor)
y_pred = tf.log(y_pred / (1. - y_pred))
cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=y_true,
logits=y_pred)
corrected_cross_entropy = tf.where(tf.equal(y_true, MAGIC_NUMBER),
tf.zeros_like(cross_entropy),
cross_entropy)
return tf.reduce_mean(corrected_cross_entropy,
axis=-1) * magic_correction_term(y_true)
def mean_squared_logarithmic_error(y_true, y_pred, sample_weight=None):
"""
Calculate mean squared logarithmic error, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(tf.Tensor, tf.Variable)
:param y_pred: Prediction
:type y_pred: Union(tf.Tensor, tf.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(tf.Tensor, tf.Variable, list)
:return: Mean Squared Logarithmic Error
:rtype: tf.Tensor
:History: 2018-Feb-17 - Written - Henry Leung (University of Toronto)
"""
tf_inf = tf.cast(tf.constant(1) / tf.constant(0), tf.float32)
epsilon_tensor = tf.cast(tf.constant(tfk.backend.epsilon()), tf.float32)
first_log = tf.math.log(
tf.clip_by_value(y_pred, epsilon_tensor, tf_inf) + 1.)
second_log = tf.math.log(
tf.clip_by_value(y_true, epsilon_tensor, tf_inf) + 1.)
log_diff = tf.where(tf.equal(y_true, MAGIC_NUMBER), tf.zeros_like(y_true),
tf.square(first_log - second_log))
losses = tf.reduce_mean(log_diff, axis=-1) * magic_correction_term(y_true)
return weighted_loss(losses, sample_weight)
def robust_binary_crossentropy(y_true, y_pred, logit_var):
"""
Calculate binary accuracy, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(tf.Tensor, tf.Variable)
:param y_pred: Prediction in logits space
:type y_pred: Union(tf.Tensor, tf.Variable)
:param logit_var: Predictive variance in logits space
:type logit_var: Union(tf.Tensor, tf.Variable)
:return: categorical cross-entropy
:rtype: tf.Tensor
:History: 2018-Mar-15 - Written - Henry Leung (University of Toronto)
"""
variance_depressor = tf.reduce_mean(
tf.exp(logit_var) - tf.ones_like(logit_var))
undistorted_loss = binary_crossentropy(y_true, y_pred, from_logits=True)
dist = distributions.Normal(loc=y_pred, scale=logit_var)
mc_result = tf.map_fn(lambda x: -tf.nn.elu(
undistorted_loss - binary_crossentropy(y_true, x, from_logits=True)),
dist.sample([25]),
dtype=tf.float32)
variance_loss = tf.reduce_mean(mc_result, axis=0) * undistorted_loss
return (variance_loss + undistorted_loss +
variance_depressor) * magic_correction_term(y_true)
def robust_binary_crossentropy(y_true, y_pred, logit_var, sample_weight):
"""
Calculate binary accuracy, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(tf.Tensor, tf.Variable)
:param y_pred: Prediction in logits space
:type y_pred: Union(tf.Tensor, tf.Variable)
:param logit_var: Predictive variance in logits space
:type logit_var: Union(tf.Tensor, tf.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(tf.Tensor, tf.Variable, list)
:return: categorical cross-entropy
:rtype: tf.Tensor
:History: 2018-Mar-15 - Written - Henry Leung (University of Toronto)
"""
variance_depressor = tf.reduce_mean(tf.exp(logit_var) - tf.ones_like(logit_var))
undistorted_loss = binary_crossentropy(y_true, y_pred, from_logits=True)
dist = tfd.Normal(loc=y_pred, scale=logit_var)
mc_num = 25
batch_size = tf.shape(y_pred)[0]
label_size = tf.shape(y_pred)[-1]
mc_result = -tf.nn.elu(tf.tile(undistorted_loss, [mc_num]) - binary_crossentropy(tf.tile(y_true, [mc_num, 1]),
tf.reshape(dist.sample([mc_num]),
(batch_size * mc_num,
label_size)),
from_logits=True))
variance_loss = tf.reduce_mean(tf.reshape(mc_result, (mc_num, batch_size)), axis=0) * undistorted_loss
losses = (variance_loss + undistorted_loss + variance_depressor) * magic_correction_term(y_true)
return weighted_loss(losses, sample_weight)
def robust_mse(y_true, y_pred, variance, labels_err):
"""
Calculate predictive variance, and takes account of labels error in Bayesian Neural Network
:param y_true: Ground Truth
:type y_true: Union(tf.Tensor, tf.Variable)
:param y_pred: Prediction
:type y_pred: Union(tf.Tensor, tf.Variable)
:param variance: Predictive Variance
:type variance: Union(tf.Tensor, tf.Variable)
:param labels_err: Known labels error, give zeros if unknown/unavailable
:type labels_err: Union(tf.Tensor, tf.Variable)
:return: Robust Mean Squared Error, can be used directly with Tensorflow
:rtype: tf.Tensor
:History: 2018-April-07 - Written - Henry Leung (University of Toronto)
"""
# labels_err still contains magic_number
labels_err_y = tf.where(tf.equal(y_true, MAGIC_NUMBER),
tf.zeros_like(y_true), labels_err)
# Neural Net is predicting log(var), so take exp, takes account the target variance, and take log back
y_pred_corrected = tf.log(tf.exp(variance) + tf.square(labels_err_y))
wrapper_output = tf.where(
tf.equal(y_true, MAGIC_NUMBER), tf.zeros_like(y_true),
0.5 * tf.square(y_true - y_pred) * (tf.exp(-y_pred_corrected)) +
0.5 * y_pred_corrected)
return tf.reduce_mean(wrapper_output,
axis=-1) * magic_correction_term(y_true)
def test_loss_magic(self):
# =============Magic correction term============= #
y_true = tf.constant([[2., MAGIC_NUMBER, MAGIC_NUMBER],
[2., MAGIC_NUMBER, 4.]])
npt.assert_array_equal(
magic_correction_term(y_true).eval(session=get_session()),
[3., 1.5])
def mean_error(y_true, y_pred):
"""
Calculate mean error as a way to get the bias in prediction, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(tf.Tensor, tf.Variable)
:param y_pred: Prediction
:type y_pred: Union(tf.Tensor, tf.Variable)
:return: Mean Error
:rtype: tf.Tensor
:History: 2018-May-22 - Written - Henry Leung (University of Toronto)
"""
return tf.reduce_mean(tf.where(tf.equal(y_true, MAGIC_NUMBER), tf.zeros_like(y_true), y_true - y_pred),
axis=-1) * magic_correction_term(y_true)
def mean_squared_error(y_true, y_pred):
"""
Calculate mean square error losses
:param y_true: Ground Truth
:type y_true: Union(tf.Tensor, tf.Variable)
:param y_pred: Prediction
:type y_pred: Union(tf.Tensor, tf.Variable)
:return: Mean Squared Error
:rtype: tf.Tensor
:History: 2017-Nov-16 - Written - Henry Leung (University of Toronto)
"""
return tf.reduce_mean(tf.where(tf.equal(y_true, MAGIC_NUMBER), tf.zeros_like(y_true),
tf.square(y_true - y_pred)), axis=-1) * magic_correction_term(y_true)
def mean_absolute_error(y_true, y_pred):
"""
Calculate mean absolute error, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(tf.Tensor, tf.Variable)
:param y_pred: Prediction
:type y_pred: Union(tf.Tensor, tf.Variable)
:return: Mean Absolute Error
:rtype: tf.Tensor
:History: 2018-Jan-14 - Written - Henry Leung (University of Toronto)
"""
return tf.reduce_mean(tf.where(tf.equal(y_true, MAGIC_NUMBER), tf.zeros_like(y_true),
tf.abs(y_true - y_pred)), axis=-1) * magic_correction_term(y_true)
def categorical_accuracy(y_true, y_pred):
"""
Calculate categorical accuracy, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(tf.Tensor, tf.Variable)
:param y_pred: Prediction
:type y_pred: Union(tf.Tensor, tf.Variable)
:return: Categorical Classification Accuracy
:rtype: tf.Tensor
:History: 2018-Jan-21 - Written - Henry Leung (University of Toronto)
"""
y_true = tf.where(magic_num_check(y_true), tf.zeros_like(y_true), y_true)
return tf.cast(tf.equal(tf.argmax(y_true, axis=-1), tf.argmax(y_pred, axis=-1)),
tf.float32) * magic_correction_term(y_true)
def mean_squared_error(y_true, y_pred, sample_weight=None):
"""
Calculate mean square error losses
:param y_true: Ground Truth
:type y_true: Union(tf.Tensor, tf.Variable)
:param y_pred: Prediction
:type y_pred: Union(tf.Tensor, tf.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(tf.Tensor, tf.Variable, list)
:return: Mean Squared Error
:rtype: tf.Tensor
:History: 2017-Nov-16 - Written - Henry Leung (University of Toronto)
"""
losses = tf.reduce_mean(tf.where(magic_num_check(y_true), tf.zeros_like(y_true),
tf.square(y_true - y_pred)), axis=-1) * magic_correction_term(y_true)
return weighted_loss(losses, sample_weight)
def mean_error(y_true, y_pred, sample_weight=None):
"""
Calculate mean error as a way to get the bias in prediction, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(tf.Tensor, tf.Variable)
:param y_pred: Prediction
:type y_pred: Union(tf.Tensor, tf.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(tf.Tensor, tf.Variable, list)
:return: Mean Error
:rtype: tf.Tensor
:History: 2018-May-22 - Written - Henry Leung (University of Toronto)
"""
losses = tf.reduce_mean(tf.where(magic_num_check(y_true), tf.zeros_like(y_true), y_true - y_pred),
axis=-1) * magic_correction_term(y_true)
return weighted_loss(losses, sample_weight)
def mean_percentage_error(y_true, y_pred):
"""
Calculate mean percentage error, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(tf.Tensor, tf.Variable)
:param y_pred: Prediction
:type y_pred: Union(tf.Tensor, tf.Variable)
:return: Mean Percentage Error
:rtype: tf.Tensor
:History: 2018-Jun-06 - Written - Henry Leung (University of Toronto)
"""
tf_inf = tf.cast(tf.constant(1) / tf.constant(0), tf.float32)
epsilon_tensor = tf.cast(tf.constant(keras.backend.epsilon()), tf.float32)
diff = y_true - y_pred / tf.clip_by_value(y_true, epsilon_tensor, tf_inf)
diff_corrected = tf.where(tf.equal(y_true, MAGIC_NUMBER), tf.zeros_like(y_true), diff)
return 100. * tf.reduce_mean(diff_corrected, axis=-1) * magic_correction_term(y_true)
def mean_percentage_error(y_true, y_pred, sample_weight=None):
"""
Calculate mean percentage error, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(tf.Tensor, tf.Variable)
:param y_pred: Prediction
:type y_pred: Union(tf.Tensor, tf.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(tf.Tensor, tf.Variable, list)
:return: Mean Percentage Error
:rtype: tf.Tensor
:History: 2018-Jun-06 - Written - Henry Leung (University of Toronto)
"""
tf_inf = tf.cast(tf.constant(1) / tf.constant(0), tf.float32)
epsilon_tensor = tf.cast(tf.constant(tfk.backend.epsilon()), tf.float32)
diff = y_true - y_pred / tf.clip_by_value(y_true, epsilon_tensor, tf_inf)
diff_corrected = tf.where(magic_num_check(y_true), tf.zeros_like(y_true), diff)
losses = 100. * tf.reduce_mean(diff_corrected, axis=-1) * magic_correction_term(y_true)
return weighted_loss(losses, sample_weight)
def binary_accuracy_internal(y_true, y_pred):
if from_logits:
y_pred = tf.nn.sigmoid(y_pred)
return tf.reduce_mean(tf.cast(tf.equal(y_true, tf.round(y_pred)),
tf.float32),
axis=-1) * magic_correction_term(y_true)
def test_loss_func(self):
# make sure custom reduce_var works
var_array = [1, 2, 3, 4, 5]
self.assertEqual(reduce_var(tf.Variable(var_array)).eval(session=get_session()), np.var(var_array))
# =============Magic correction term============= #
y_true = tf.Variable([[2., MAGIC_NUMBER, MAGIC_NUMBER], [2., MAGIC_NUMBER, 4.]])
npt.assert_array_equal(magic_correction_term(y_true).eval(session=get_session()), [3., 1.5])
# =============MSE/MAE============= #
y_pred = tf.Variable([[2., 3., 4.], [2., 3., 7.]])
y_pred_2 = tf.Variable([[2., 9., 4.], [2., 0., 7.]])
y_true = tf.Variable([[2., MAGIC_NUMBER, 4.], [2., MAGIC_NUMBER, 4.]])
npt.assert_almost_equal(mean_absolute_error(y_true, y_pred).eval(session=get_session()), [0., 3. / 2.])
npt.assert_almost_equal(mean_squared_error(y_true, y_pred).eval(session=get_session()), [0., 9. / 2])
# make sure neural network prediction won't matter for magic number term
npt.assert_almost_equal(mean_absolute_error(y_true, y_pred).eval(session=get_session()),
mean_absolute_error(y_true, y_pred_2).eval(session=get_session()))
npt.assert_almost_equal(mean_squared_error(y_true, y_pred).eval(session=get_session()),
mean_squared_error(y_true, y_pred_2).eval(session=get_session()))
# =============Mean Error============= #
y_pred = tf.Variable([[1., 3., 4.], [2., 3., 7.]])
y_true = tf.Variable([[2., MAGIC_NUMBER, 3.], [2., MAGIC_NUMBER, 7.]])
npt.assert_almost_equal(mean_error(y_true, y_pred).eval(session=get_session()), [0., 0.])
# =============Accuracy============= #
y_pred = tf.Variable([[1., 0., 0.], [1., 0., 0.]])
y_true = tf.Variable([[1., MAGIC_NUMBER, 1.], [0., MAGIC_NUMBER, 1.]])
npt.assert_array_equal(categorical_accuracy(y_true, y_pred).eval(session=get_session()), [1., 0.])
npt.assert_almost_equal(binary_accuracy(from_logits=False)(y_true, y_pred).eval(session=get_session()),
[1. / 2., 0.])
# =============Abs Percentage Accuracy============= #
y_pred = tf.Variable([[1., 0., 0.], [1., 0., 0.]])
y_pred_2 = tf.Variable([[1., 9., 0.], [1., -1., 0.]])
y_true = tf.Variable([[1., MAGIC_NUMBER, 1.], [1., MAGIC_NUMBER, 1.]])
npt.assert_array_almost_equal(mean_absolute_percentage_error(y_true, y_pred).eval(session=get_session()),
[50., 50.], decimal=3)
# make sure neural network prediction won't matter for magic number term
npt.assert_array_almost_equal(mean_absolute_percentage_error(y_true, y_pred).eval(session=get_session()),
mean_absolute_percentage_error(y_true, y_pred_2).eval(session=get_session()),
decimal=3)
# =============Percentage Accuracy============= #
y_pred = tf.Variable([[1., 0., 0.], [1., 0., 0.]])
y_pred_2 = tf.Variable([[1., 9., 0.], [1., -1., 0.]])
y_true = tf.Variable([[1., MAGIC_NUMBER, 1.], [1., MAGIC_NUMBER, 1.]])
npt.assert_array_almost_equal(mean_percentage_error(y_true, y_pred).eval(session=get_session()),
[50., 50.], decimal=3)
# make sure neural network prediction won't matter for magic number term
npt.assert_array_almost_equal(mean_percentage_error(y_true, y_pred).eval(session=get_session()),
mean_percentage_error(y_true, y_pred_2).eval(session=get_session()),
decimal=3)
# =============Mean Squared Log Error============= #
y_pred = tf.Variable([[1., 0., 0.], [1., 0., 0.]])
y_pred_2 = tf.Variable([[1., 9., 0.], [1., -1., 0.]])
y_true = tf.Variable([[1., MAGIC_NUMBER, 1.], [1., MAGIC_NUMBER, 1.]])
npt.assert_array_almost_equal(mean_squared_logarithmic_error(y_true, y_pred).eval(session=get_session()),
[0.24, 0.24], decimal=3)
# make sure neural network prediction won't matter for magic number term
npt.assert_array_almost_equal(mean_squared_logarithmic_error(y_true, y_pred).eval(session=get_session()),
mean_squared_logarithmic_error(y_true, y_pred_2).eval(session=get_session()),
decimal=3)
# =============Zeros Loss============= #
y_pred = tf.Variable([[1., 0., 0.], [5., -9., 2.]])
y_true = tf.Variable([[1., MAGIC_NUMBER, 1.], [1., MAGIC_NUMBER, 1.]])
npt.assert_array_almost_equal(zeros_loss(y_true, y_pred).eval(session=get_session()), [0., 0.])
