# Loss Functions and Metrics

astroNN provides modified loss functions under astroNN.nn.losses module which are capable to deal with incomplete labels which are represented by magicnumber in astroNN configuration file or Magic Number in equations below. Since they are built on Tensorflow and follows Keras API requirement, all astroNN loss functions are fully compatible with Keras with Tensorflow backend, as well as directly be imported and used with Tensorflow, for most loss functions, the first argument is ground truth tensor and the second argument is prediction tensor from neural network.

Note

Always make sure when you are normalizing your data, keep the magic number as magic number. If you use astroNN normalizer, astroNN will take care of that.

Here are some explanations on variables in the following loss functions:

$$y_i$$ means the ground truth labels, always represented by python variable y_true in astroNN

$$\hat{y_i}$$ means the prediction from neural network, always represented by python variable y_pred in astroNN

## Correction Term for Magic Number

astroNN.nn.losses.magic_correction_term(y_true)[source]

Calculate a correction term to prevent the loss being “lowered” by magic_num or NaN

Parameters

y_true (tf.Tensor) – Ground Truth

Returns

Correction Term

Return type

tf.Tensor

History
2018-Jan-30 - Written - Henry Leung (University of Toronto)
2018-Feb-17 - Updated - Henry Leung (University of Toronto)

Since astroNN deals with magic number by assuming the prediction from neural network for those ground truth with Magic Number is right, so we need a correction term.

The correction term in astroNN is defined by the following equation and we call the equation $$\mathcal{F}_{correction}$$

$\mathcal{F}_{correction} = \frac{\text{Non-Magic Number Count} + \text{Magic Number Count}}{\text{Non Magic Number Count}}$

In case of no labels with Magic Number is presented, $$\mathcal{F}_{correction}$$ will equal to 1

## Mean Squared Error

astroNN.nn.losses.mean_squared_error(y_true, y_pred, sample_weight=None)[source]

Calculate mean square error losses

Parameters
• y_true (Union(tf.Tensor, tf.Variable)) – Ground Truth

• y_pred (Union(tf.Tensor, tf.Variable)) – Prediction

• sample_weight (Union(tf.Tensor, tf.Variable, list)) – Sample weights

Returns

Mean Squared Error

Return type

tf.Tensor

History

2017-Nov-16 - Written - Henry Leung (University of Toronto)

MSE is based on the equation

$\begin{split}Loss_i = \begin{cases} \begin{split} (\hat{y_i}-y_i)^2 & \text{ for } y_i \neq \text{Magic Number}\\ 0 & \text{ for } y_i = \text{Magic Number} \end{split} \end{cases}\end{split}$

And thus the loss for mini-batch is

$Loss_{NN} = \frac{1}{D} \sum_{i=1}^{batch} (Loss_i \mathcal{F}_{correction, i})$

It can be used with Keras, you just have to import the function from astroNN

1def keras_model():
2    # Your keras_model define here
3    return model
4
5model = keras_model()
6# remember to import astroNN's loss function first
7model.compile(loss=mean_squared_error, ...)


## Mean Absolute Error

astroNN.nn.losses.mean_absolute_error(y_true, y_pred, sample_weight=None)[source]

Calculate mean absolute error, ignoring the magic number

Parameters
• y_true (Union(tf.Tensor, tf.Variable)) – Ground Truth

• y_pred (Union(tf.Tensor, tf.Variable)) – Prediction

• sample_weight (Union(tf.Tensor, tf.Variable, list)) – Sample weights

Returns

Mean Absolute Error

Return type

tf.Tensor

History

2018-Jan-14 - Written - Henry Leung (University of Toronto)

MAE is based on the equation

$\begin{split}Loss_i = \begin{cases} \begin{split} \left| \hat{y_i}-y_i \right| & \text{ for } y_i \neq \text{Magic Number}\\ 0 & \text{ for } y_i = \text{Magic Number} \end{split} \end{cases}\end{split}$

And thus the loss for mini-batch is

$Loss_{NN} = \frac{1}{D} \sum_{i=1}^{batch} (Loss_i \mathcal{F}_{correction, i})$

It can be used with Keras, you just have to import the function from astroNN

1def keras_model():
2    # Your keras_model define here
3    return model
4
5model = keras_model()
6# remember to import astroNN's loss function first
7model.compile(loss=mean_absolute_error, ...)


## Mean Error

astroNN.nn.losses.mean_error(y_true, y_pred, sample_weight=None)[source]

Calculate mean error as a way to get the bias in prediction, ignoring the magic number

Parameters
• y_true (Union(tf.Tensor, tf.Variable)) – Ground Truth

• y_pred (Union(tf.Tensor, tf.Variable)) – Prediction

• sample_weight (Union(tf.Tensor, tf.Variable, list)) – Sample weights

Returns

Mean Error

Return type

tf.Tensor

History

2018-May-22 - Written - Henry Leung (University of Toronto)

Mean Error is a metrics to evaluate the bias of prediction and is based on the equation

$\begin{split}Loss_i = \begin{cases} \begin{split} \hat{y_i}-y_i & \text{ for } y_i \neq \text{Magic Number}\\ 0 & \text{ for } y_i = \text{Magic Number} \end{split} \end{cases}\end{split}$

And thus the loss for mini-batch is

$Loss_{NN} = \frac{1}{D} \sum_{i=1}^{batch} (Loss_i \mathcal{F}_{correction, i})$

It can be used with Keras, you just have to import the function from astroNN

1def keras_model():
2    # Your keras_model define here
3    return model
4
5model = keras_model()
6# remember to import astroNN's loss function first
7model.compile(loss=mean_error, ...)


## Regression Loss and Predictive Variance Loss for Bayesian Neural Net

astroNN.nn.losses.robust_mse(y_true, y_pred, variance, labels_err, sample_weight=None)[source]

Calculate predictive variance, and takes account of labels error in Bayesian Neural Network

Parameters
• y_true (Union(tf.Tensor, tf.Variable)) – Ground Truth

• y_pred (Union(tf.Tensor, tf.Variable)) – Prediction

• variance (Union(tf.Tensor, tf.Variable)) – Log Predictive Variance

• labels_err (Union(tf.Tensor, tf.Variable)) – Known labels error, give zeros if unknown/unavailable

• sample_weight (Union(tf.Tensor, tf.Variable, list)) – Sample weights

Returns

Robust Mean Squared Error, can be used directly with Tensorflow

Return type

tf.Tensor

History

2018-April-07 - Written - Henry Leung (University of Toronto)

astroNN.nn.losses.mse_lin_wrapper(var, labels_err)[source]

Calculate predictive variance, and takes account of labels error in Bayesian Neural Network

Parameters
• var (Union(tf.Tensor, tf.Variable)) – Predictive Variance

• labels_err (Union(tf.Tensor, tf.Variable)) – Known labels error, give zeros if unknown/unavailable

Returns

Robust MSE function for labels prediction neurones, which matches Keras losses API

Return type

function

Returned Funtion Parameter
function(y_true, y_pred)
- y_true (tf.Tensor): Ground Truth
- y_pred (tf.Tensor): Prediction
Return (tf.Tensor): Robust Mean Squared Error
History

2017-Nov-16 - Written - Henry Leung (University of Toronto)

astroNN.nn.losses.mse_var_wrapper(lin, labels_err)[source]

Calculate predictive variance, and takes account of labels error in Bayesian Neural Network

Parameters
• lin (Union(tf.Tensor, tf.Variable)) – Prediction

• labels_err (Union(tf.Tensor, tf.Variable)) – Known labels error, give zeros if unknown/unavailable

Returns

Robust MSE function for predictive variance neurones which matches Keras losses API

Return type

function

Returned Funtion Parameter
function(y_true, y_pred)
- y_true (tf.Tensor): Ground Truth
- y_pred (tf.Tensor): Predictive Variance
Return (tf.Tensor): Robust Mean Squared Error
History

2017-Nov-16 - Written - Henry Leung (University of Toronto)

It is based on the equation implemented as robust_mse(), please notice $$s_i$$ is representing $$log((\sigma_{predictive, i})^2 + (\sigma_{known, i})^2)$$. Neural network not predicting variance directly to avoid numerical instability but predicting $$log((\sigma_{i})^2)$$

$\begin{split}Loss_i = \begin{cases} \begin{split} \frac{1}{2} (\hat{y_i}-y_i)^2 e^{-s_i} + \frac{1}{2}(s_i) & \text{ for } y_i \neq \text{Magic Number}\\ 0 & \text{ for } y_i = \text{Magic Number} \end{split} \end{cases}\end{split}$

And thus the loss for mini-batch is

$Loss_{BNN} = \frac{1}{D} \sum_{i=1}^{batch} (Loss_i \mathcal{F}_{correction, i})$

They basically do the same things and can be used with Keras, you just have to import the functions from astroNN

 1def keras_model():
2    # Your keras_model define here
3
4    # model for the training process
5    model = Model(inputs=[input_tensor, labels_err_tensor], outputs=[output, variance_output])
6
7    # model for the prediction
8    model_prediction = Model(inputs=input_tensor, outputs=[output, variance_output])
9
10    variance_output = Dense(name='variance_output', ...)
11    output = Dense(name='output', ...)
12
13    predictive_variance_loss = mse_var_wrapper(output, labels_err_tensor)
14    output_loss = mse_lin_wrapper(predictive_variance, labels_err_tensor)
15
16    return model, model_prediction, output_loss, predictive_variance_loss
17
18model, model_prediction, output_loss, predictive_variance_loss = keras_model()
19# remember to import astroNN loss function first
20model.compile(loss={'output': output_loss, 'variance_output': predictive_variance_loss}, ...)


To better understand this loss function, you can see the following plot of Loss vs Variance colored by squared difference which is $$(\hat{y_i}-y_i)^2$$ ## Mean Squared Logarithmic Error

astroNN.nn.losses.mean_squared_logarithmic_error(y_true, y_pred, sample_weight=None)[source]

Calculate mean squared logarithmic error, ignoring the magic number

Parameters
• y_true (Union(tf.Tensor, tf.Variable)) – Ground Truth

• y_pred (Union(tf.Tensor, tf.Variable)) – Prediction

• sample_weight (Union(tf.Tensor, tf.Variable, list)) – Sample weights

Returns

Mean Squared Logarithmic Error

Return type

tf.Tensor

History

2018-Feb-17 - Written - Henry Leung (University of Toronto)

MSLE will first clip the values of prediction from neural net for the sake of numerical stability,

\begin{align}\begin{aligned}\begin{split}y_i = \begin{cases} \begin{split} \epsilon + 1 & \text{ for } y_i < \epsilon \\ y_i + 1 & \text{ for otherwise } \end{split} \end{cases}\end{split}\\\text{where } \epsilon \text{ is a small constant}\end{aligned}\end{align}

Then MSLE is based on the equation

$\begin{split}Loss_i = \begin{cases} \begin{split} (\log{(\hat{y_i})} - \log{(y_i)})^2 & \text{ for } y_i \neq \text{Magic Number}\\ 0 & \text{ for } y_i = \text{Magic Number} \end{split} \end{cases}\end{split}$

And thus the loss for mini-batch is

$Loss_{NN} = \frac{1}{D} \sum_{i=1}^{batch} (Loss_i \mathcal{F}_{correction, i})$

It can be used with Keras, you just have to import the function from astroNN

1def keras_model():
2    # Your keras_model define here
3    return model
4
5model = keras_model()
6# remember to import astroNN's loss function first
7model.compile(loss=mean_squared_logarithmic_error, ...)


## Mean Absolute Percentage Error

astroNN.nn.losses.mean_absolute_percentage_error(y_true, y_pred, sample_weight=None)[source]

Calculate mean absolute percentage error, ignoring the magic number

Parameters
• y_true (Union(tf.Tensor, tf.Variable)) – Ground Truth

• y_pred (Union(tf.Tensor, tf.Variable)) – Prediction

• sample_weight (Union(tf.Tensor, tf.Variable, list)) – Sample weights

Returns

Mean Absolute Percentage Error

Return type

tf.Tensor

History

2018-Feb-17 - Written - Henry Leung (University of Toronto)

Mean Absolute Percentage Error will first clip the values of prediction from neural net for the sake of numerical stability,

\begin{align}\begin{aligned}\begin{split}y_i = \begin{cases} \begin{split} \epsilon & \text{ for } y_i < \epsilon \\ y_i & \text{ for otherwise } \end{split} \end{cases}\end{split}\\\text{where } \epsilon \text{ is a small constant}\end{aligned}\end{align}

Then Mean Absolute Percentage Error is based on the equation

$\begin{split}Loss_i = \begin{cases} \begin{split} 100 \text{ } \frac{\left| y_i - \hat{y_i} \right|}{y_i} & \text{ for } y_i \neq \text{Magic Number}\\ 0 & \text{ for } y_i = \text{Magic Number} \end{split} \end{cases}\end{split}$

And thus the loss for mini-batch is

$Loss_{NN} = \frac{1}{D} \sum_{i=1}^{batch} (Loss_i \mathcal{F}_{correction, i})$

It can be used with Keras, you just have to import the function from astroNN

1def keras_model():
2    # Your keras_model define here
3    return model
4
5model = keras_model()
6# remember to import astroNN's loss function first
7model.compile(loss=mean_absolute_percentage_error, ...)


## Mean Percentage Error

astroNN.nn.losses.mean_percentage_error(y_true, y_pred, sample_weight=None)[source]

Calculate mean percentage error, ignoring the magic number

Parameters
• y_true (Union(tf.Tensor, tf.Variable)) – Ground Truth

• y_pred (Union(tf.Tensor, tf.Variable)) – Prediction

• sample_weight (Union(tf.Tensor, tf.Variable, list)) – Sample weights

Returns

Mean Percentage Error

Return type

tf.Tensor

History

2018-Jun-06 - Written - Henry Leung (University of Toronto)

Mean Percentage Error will first clip the values of prediction from neural net for the sake of numerical stability,

\begin{align}\begin{aligned}\begin{split}y_i = \begin{cases} \begin{split} \epsilon & \text{ for } y_i < \epsilon \\ y_i & \text{ for otherwise } \end{split} \end{cases}\end{split}\\\text{where } \epsilon \text{ is a small constant}\end{aligned}\end{align}

Then Mean Percentage Error is based on the equation

$\begin{split}Loss_i = \begin{cases} \begin{split} 100 \text{ } \frac{y_i - \hat{y_i}}{y_i} & \text{ for } y_i \neq \text{Magic Number}\\ 0 & \text{ for } y_i = \text{Magic Number} \end{split} \end{cases}\end{split}$

And thus the loss for mini-batch is

$Loss_{NN} = \frac{1}{D} \sum_{i=1}^{batch} (Loss_i \mathcal{F}_{correction, i})$

It can be used with Keras, you just have to import the function from astroNN

1def keras_model():
2    # Your keras_model define here
3    return model
4
5model = keras_model()
6# remember to import astroNN's loss function first
7model.compile(loss=mean_percentage_error, ...)


## Categorical Cross-Entropy

astroNN.nn.losses.categorical_crossentropy(y_true, y_pred, sample_weight=None, from_logits=False)[source]

Categorical cross-entropy between an output tensor and a target tensor, ignoring the magic number

Parameters
• y_true (Union(tf.Tensor, tf.Variable)) – Ground Truth

• y_pred (Union(tf.Tensor, tf.Variable)) – Prediction

• sample_weight (Union(tf.Tensor, tf.Variable, list)) – Sample weights

• from_logits (boolean) – From logits space or not. If you want to use logits, please use from_logits=True

Returns

Categorical Cross-Entropy

Return type

tf.Tensor

History

2018-Jan-14 - Written - Henry Leung (University of Toronto)

Categorical Cross-Entropy will first clip the values of prediction from neural net for the sake of numerical stability if the prediction is not coming from logits (before softmax activated)

\begin{align}\begin{aligned}\begin{split}\hat{y_i} = \begin{cases} \begin{split} \epsilon & \text{ for } \hat{y_i} < \epsilon \\ 1 - \epsilon & \text{ for } \hat{y_i} > 1 - \epsilon \\ \hat{y_i} & \text{ for otherwise } \end{split} \end{cases}\end{split}\\\text{where } \epsilon \text{ is a small constant}\end{aligned}\end{align}

and then based on the equation

$\begin{split}Loss_i = \begin{cases} \begin{split} y_i \log{(\hat{y_i})} & \text{ for } y_i \neq \text{Magic Number}\\ 0 & \text{ for } y_i = \text{Magic Number} \end{split} \end{cases}\end{split}$

And thus the loss for mini-batch is

$Loss_{NN} = - \frac{1}{D} \sum_{i=1}^{batch} (Loss_i \mathcal{F}_{correction, i})$

It can be used with Keras, you just have to import the function from astroNN

1def keras_model():
2    # Your keras_model define here
3    return model
4
5model = keras_model()
6# remember to import astroNN's loss function first
7model.compile(loss=categorical_crossentropy(from_logits=False), ...)


## Binary Cross-Entropy

astroNN.nn.losses.binary_crossentropy(y_true, y_pred, sample_weight=None, from_logits=False)[source]

Binary cross-entropy between an output tensor and a target tensor, ignoring the magic number

Parameters
• y_true (Union(tf.Tensor, tf.Variable)) – Ground Truth

• y_pred (Union(tf.Tensor, tf.Variable)) – Prediction

• from_logits (boolean) – From logits space or not. If you want to use logits, please use from_logits=True

• sample_weight (Union(tf.Tensor, tf.Variable, list)) – Sample weights

Returns

Binary Cross-Entropy

Return type

tf.Tensor

History

2018-Jan-14 - Written - Henry Leung (University of Toronto)

Binary Cross-Entropy will first clip the values of prediction from neural net for the sake of numerical stability if from_logits=False

\begin{align}\begin{aligned}\begin{split}\hat{y_i} = \begin{cases} \begin{split} \epsilon & \text{ for } \hat{y_i} < \epsilon \\ 1 - \epsilon & \text{ for } \hat{y_i} > 1 - \epsilon \\ \hat{y_i} & \text{ for otherwise } \end{split} \end{cases}\end{split}\\\text{where } \epsilon \text{ is a small constant}\end{aligned}\end{align}

and then based on the equation

$\begin{split}Loss_i = \begin{cases} \begin{split} y_i \log{(\hat{y_i})} + (1-y_i)\log{(1-\hat{y_i})} & \text{ for } y_i \neq \text{Magic Number}\\ \hat{y_i} \log{(\hat{y_i})} + (1-\hat{y_i})\log{(1-\hat{y_i})} & \text{ for } y_i = \text{Magic Number} \end{split} \end{cases}\end{split}$

to avoid numerical instability if from_logits=True, we can reformulate it as

$\begin{split}Loss_i = \begin{cases} \begin{split} \max{(\hat{y_i}, 0)} - y_i \hat{y_i} + \log{(1+e^{-\|\hat{y_i}\|})} & \text{ for } y_i \neq \text{Magic Number}\\ 0 & \text{ for } y_i = \text{Magic Number} \end{split} \end{cases}\end{split}$

And thus the loss for mini-batch is

$Loss_{NN} = - \frac{1}{D} \sum_{i=1}^{batch} (Loss_i \mathcal{F}_{correction, i})$

It can be used with Keras, you just have to import the function from astroNN

1def keras_model():
2    # Your keras_model define here
3    return model
4
5model = keras_model()
6# remember to import astroNN's loss function first
7model.compile(loss=binary_crossentropy(from_logits=False), ...)


## Categorical Cross-Entropy and Predictive Logits Variance for Bayesian Neural Net

astroNN.nn.losses.robust_categorical_crossentropy(y_true, y_pred, logit_var, sample_weight)[source]

Calculate categorical accuracy, ignoring the magic number

Parameters
• y_true (Union(tf.Tensor, tf.Variable)) – Ground Truth

• y_pred (Union(tf.Tensor, tf.Variable)) – Prediction in logits space

• logit_var (Union(tf.Tensor, tf.Variable)) – Predictive variance in logits space

• sample_weight (Union(tf.Tensor, tf.Variable, list)) – Sample weights

Returns

categorical cross-entropy

Return type

tf.Tensor

History

2018-Mar-15 - Written - Henry Leung (University of Toronto)

astroNN.nn.losses.bayesian_categorical_crossentropy_wrapper(logit_var)[source]
Categorical crossentropy between an output tensor and a target tensor for Bayesian Neural Network
equation (12) of arxiv:1703.04977
Parameters

logit_var (Union(tf.Tensor, tf.Variable)) – Predictive variance

Returns

Robust categorical_crossentropy function for predictive variance neurones which matches Keras losses API

Return type

function

Returned Function Parameter
function(y_true, y_pred)
- y_true (tf.Tensor): Ground Truth
- y_pred (tf.Tensor): Prediction in logits space
Return (tf.Tensor): Robust categorical crossentropy
History

2018-Mar-15 - Written - Henry Leung (University of Toronto)

astroNN.nn.losses.bayesian_categorical_crossentropy_var_wrapper(logits)[source]
Categorical crossentropy between an output tensor and a target tensor for Bayesian Neural Network
equation (12) of arxiv:1703.04977
Parameters

logits (Union(tf.Tensor, tf.Variable)) – Prediction in logits space

Returns

Robust categorical_crossentropy function for predictive variance neurones which matches Keras losses API

Return type

function

Returned Function Parameter
function(y_true, y_pred)
- y_true (tf.Tensor): Ground Truth
- y_pred (tf.Tensor): Predictive variance in logits space
Return (tf.Tensor): Robust categorical crossentropy
History

2018-Mar-15 - Written - Henry Leung (University of Toronto)

It is based on Equation 12 from arxiv:1703.04977. $$s_i$$ is representing the predictive variance of logits

$\begin{split}Loss_i = \begin{cases} \begin{split} \text{Categorical Cross-Entropy} + \text{Distorted Categorical Cross-Entropy} + e^{s_i} - 1 & \text{ for } y_i \neq \text{Magic Number}\\ 0 & \text{ for } y_i = \text{Magic Number} \end{split} \end{cases}\end{split}$

where Distorted Categorical Cross-Entropy is defined as

$\text{elu} (\text{Categorical Cross-Entropy}(y_i, \hat{y_i}) - \text{Categorical Cross-Entropy}(y_i, \mathcal{N}(\hat{y_i}, \sqrt{s_i})))$

And thus the loss for mini-batch is

$Loss_{BNN} = \frac{1}{D} \sum_{i=1}^{batch} (Loss_i \mathcal{F}_{correction, i})$

bayesian_categorical_crossentropy_wrapper is for the prediction neurones

bayesian_categorical_crossentropy_var_wrapper is for the predictive variance neurones

They basically do the same things and can be used with Keras, you just have to import the functions from astroNN

 1def keras_model():
2    # Your keras_model define here
3
4    # model for the training process
5    model = Model(inputs=[input_tensor], outputs=[output, variance_output])
6
7    # model for the prediction
8    model_prediction = Model(inputs=input_tensor, outputs=[output, variance_output])
9
10    variance_output = Dense(name='predictive_variance', ...)
11    output = Dense(name='output', ...)
12
13    predictive_variance_loss = bayesian_categorical_crossentropy_var_wrapper(output)
14    output_loss = bayesian_categorical_crossentropy_wrapper(predictive_variance)
15
16    return model, model_prediction, output_loss, predictive_variance_loss
17
18model, model_prediction, output_loss, predictive_variance_loss = keras_model()
19# remember to import astroNN loss function first
20model.compile(loss={'output': output_loss, 'variance_output': predictive_variance_loss}, ...)


## Binary Cross-Entropy and Predictive Logits Variance for Bayesian Neural Net

astroNN.nn.losses.robust_binary_crossentropy(y_true, y_pred, logit_var, sample_weight)[source]

Calculate binary accuracy, ignoring the magic number

Parameters
• y_true (Union(tf.Tensor, tf.Variable)) – Ground Truth

• y_pred (Union(tf.Tensor, tf.Variable)) – Prediction in logits space

• logit_var (Union(tf.Tensor, tf.Variable)) – Predictive variance in logits space

• sample_weight (Union(tf.Tensor, tf.Variable, list)) – Sample weights

Returns

categorical cross-entropy

Return type

tf.Tensor

History

2018-Mar-15 - Written - Henry Leung (University of Toronto)

astroNN.nn.losses.bayesian_binary_crossentropy_wrapper(logit_var)[source]
Binary crossentropy between an output tensor and a target tensor for Bayesian Neural Network
equation (12) of arxiv:1703.04977
Parameters

logit_var (Union(tf.Tensor, tf.Variable)) – Predictive variance

Returns

Robust binary_crossentropy function for predictive variance neurones which matches Keras losses API

Return type

function

Returned Function Parameter
function(y_true, y_pred)
- y_true (tf.Tensor): Ground Truth
- y_pred (tf.Tensor): Prediction in logits space
Return (tf.Tensor): Robust binary crossentropy
History

2018-Mar-15 - Written - Henry Leung (University of Toronto)

astroNN.nn.losses.bayesian_binary_crossentropy_var_wrapper(logits)[source]
Binary crossentropy between an output tensor and a target tensor for Bayesian Neural Network
equation (12) of arxiv:1703.04977
Parameters

logits (Union(tf.Tensor, tf.Variable)) – Prediction in logits space

Returns

Robust binary_crossentropy function for predictive variance neurones which matches Keras losses API

Return type

function

Returned Function Parameter
function(y_true, y_pred)
- y_true (tf.Tensor): Ground Truth
- y_pred (tf.Tensor): Predictive variance in logits space
Return (tf.Tensor): Robust binary crossentropy
History

2018-Mar-15 - Written - Henry Leung (University of Toronto)

It is based on Equation 12 from arxiv:1703.04977. $$s_i$$ is representing the predictive variance of logits

$\begin{split}Loss_i = \begin{cases} \begin{split} \text{Binary Cross-Entropy} + \text{Distorted Binary Cross-Entropy} + e^{s_i} - 1 & \text{ for } y_i \neq \text{Magic Number}\\ 0 & \text{ for } y_i = \text{Magic Number} \end{split} \end{cases}\end{split}$

where Distorted Binary Cross-Entropy is defined as

$\text{elu} (\text{Binary Cross-Entropy}(y_i, \hat{y_i}) - \text{Binary Cross-Entropy}(y_i, \mathcal{N}(\hat{y_i}, \sqrt{s_i})))$

And thus the loss for mini-batch is

$Loss_{BNN} = \frac{1}{D} \sum_{i=1}^{batch} (Loss_i \mathcal{F}_{correction, i})$

bayesian_binary_crossentropy_wrapper is for the prediction neurones

bayesian_binary_crossentropy_var_wrapper is for the predictive variance neurones

They basically do the same things and can be used with Keras, you just have to import the functions from astroNN

 1def keras_model():
2    # Your keras_model define here
3
4    # model for the training process
5    model = Model(inputs=[input_tensor], outputs=[output, variance_output])
6
7    # model for the prediction
8    model_prediction = Model(inputs=input_tensor, outputs=[output, variance_output])
9
10    variance_output = Dense(name='predictive_variance', ...)
11    output = Dense(name='output', ...)
12
13    predictive_variance_loss = bayesian_binary_crossentropy_var_wrapper(output)
14    output_loss = bayesian_binary_crossentropy_wrapper(predictive_variance)
15
16    return model, model_prediction, output_loss, predictive_variance_loss
17
18model, model_prediction, output_loss, predictive_variance_loss = keras_model()
19# remember to import astroNN loss function first
20model.compile(loss={'output': output_loss, 'variance_output': predictive_variance_loss}, ...)


## Categorical Classification Accuracy

astroNN.nn.losses.categorical_accuracy(y_true, y_pred)[source]

Calculate categorical accuracy, ignoring the magic number

Parameters
• y_true (Union(tf.Tensor, tf.Variable)) – Ground Truth

• y_pred (Union(tf.Tensor, tf.Variable)) – Prediction

Returns

Categorical Classification Accuracy

Return type

tf.Tensor

History

2018-Jan-21 - Written - Henry Leung (University of Toronto)

Categorical Classification Accuracy will first deal with Magic Number

$\begin{split}Loss_i = \begin{cases} \begin{split} y_i & \text{ for } y_i \neq \text{Magic Number}\\ 0 & \text{ for } y_i = \text{Magic Number} \end{split} \end{cases}\end{split}$

Then based on the equation

$\begin{split}Accuracy_i = \begin{cases} \begin{split} 1 & \text{ for } \text{Argmax}(y_i) = \text{Argmax}(\hat{y_i})\\ 0 & \text{ for } \text{Argmax}(y_i) \neq \text{Argmax}(\hat{y_i}) \end{split} \end{cases}\end{split}$

And thus the accuracy for is

$Accuracy = \frac{1}{D} \sum_{i=1}^{labels} (Accuracy_i \mathcal{F}_{correction, i})$

It can be used with Keras, you just have to import the function from astroNN

1def keras_model():
2    # Your keras_model define here
3    return model
4
5model = keras_model()
6# remember to import astroNN's metrics function first
7model.compile(metrics=categorical_accuracy, ...)


Note

Please make sure you use categorical_accuracy when using categorical_crossentropy as the loss function

## Binary Classification Accuracy

astroNN.nn.losses.binary_accuracy(*args, **kwargs)[source]

Calculate binary accuracy, ignoring the magic number

Parameters
• y_true (Union(tf.Tensor, tf.Variable)) – Ground Truth

• y_pred (Union(tf.Tensor, tf.Variable)) – Prediction

Returns

Binary accuracy

Return type

tf.Tensor

History

2018-Jan-31 - Written - Henry Leung (University of Toronto)

Binary Classification Accuracy will round the values of prediction if from_logits=False or will apply sigmoid first and then round the values of prediction if from_logits=True

$\begin{split}\hat{y_i} = \begin{cases} \begin{split} 1 & \text{ for } \hat{y_i} > 0.5 \\ 0 & \text{ for } \hat{y_i} \leq 0.5 \end{split} \end{cases}\end{split}$

and then based on the equation

$\begin{split}Accuracy_i = \begin{cases} \begin{split} 1 & \text{ for } y_i = \hat{y_i}\\ 0 & \text{ for } y_i \neq \hat{y_i} \end{split} \end{cases}\end{split}$

And thus the accuracy for is

$Accuracy = \frac{1}{D} \sum_{i=1}^{labels} (Accuracy_i \mathcal{F}_{correction, i})$

It can be used with Keras, you just have to import the function from astroNN

1def keras_model():
2    # Your keras_model define here
3    return model
4
5model = keras_model()
6# remember to import astroNN's metrics function first
7model.compile(metrics=binary_accuracy(from_logits=False), ...)


Note

Please make sure you use binary_accuracy when using binary_crossentropy as the loss function

## Zeros Loss

astroNN.nn.losses.zeros_loss(y_true, y_pred, sample_weight=None)[source]

Always return zeros

Parameters
• y_true (Union(tf.Tensor, tf.Variable)) – Ground Truth

• y_pred (Union(tf.Tensor, tf.Variable)) – Prediction

• sample_weight (Union(tf.Tensor, tf.Variable, list)) – Sample weights

Returns

Zeros

Return type

tf.Tensor

History

2018-May-24 - Written - Henry Leung (University of Toronto)

zeros_loss is a loss function that will always return zero loss and the function matches Keras API. It is mainly designed to do testing or experiments.

It can be used with Keras, you just have to import the function from astroNN

1def keras_model():
2    # Your keras_model define here
3    return model
4
5model = keras_model()
6# remember to import astroNN's loss function first
7model.compile(loss=zeros_loss, ...)