<p>Advice from Neil, yes, output targeting class labels is still classification.</p>

<p>Output range is in contiguous range:</p>

<ul>
<li>This is regression</li>
<li>For example: Linear activation, it has full numeric range of outputs.</li>
</ul>

<p>Output targeting class labels is classification:</p>

<ul>
<li>Single output neuron with sigmoid-like functions. This will classify to 2 classes, although Y data can be normalised to classify more classes.</li>
<li>Multiple output neurons (probabilities of classes) with sigmoid-like functions (<code>softmax</code> is mainly used). This will classify to 2 or more classes.</li>
</ul>

<p>Multiple combinations of loss functions and optimisers can make the single-neuron output layer work, with different configs for them. Note that learning rates of different optimisers are different, some take 1e-1, some need 1e-3 for good training.</p>

<p>For example, this combination should work:</p>

<pre><code>loss_fn   = tf.losses.LogCosh();
optimizer = tf.optimizers.RMSprop(1e-3);
</code></pre>

<p>From my trying out, these other combinations also work for single output neuron with my data (Adam, Adamax, Nadam, RMSprop work when learning_rate=1e-3 instead of 1e-1):</p>

<pre><code>                         Adadelta  Adagrad  Adam  Adamax  Ftrl  Nadam  RMSprop  SGD
BinaryCrossentropy       Yes       Yes      --    --      --    --     --       Yes
CategoricalCrossentropy  Yes       --       --    --      --    --     --       --
CategoricalHinge         --        --       --    --      --    --     --       --
CosineSimilarity         --        --       --    --      --    --     --       --
Hinge                    Yes       Yes      --    --      --    --     --       Yes
Huber                    Yes       Yes      --    --      --    --     --       Yes
LogCosh                  Yes       Yes      --    --      --    --     --       Yes
Poisson                  Yes       Yes      --    --      --    --     --       Yes
SquaredHinge             Yes       Yes      --    --      --    --     --       Yes

KLD: lambda a,b: KLD(a,b)
MAE,MAPE,MSE,MSLE: lambda a,b: Mxxx(a,b)
The above lambdas are direct functions, not classes like SGD, Adadelta, etc.
SparseCategorialCrossentropy: Seems not working with single output neuron.
</code></pre>

CC BY-SA 4.0
2019-10-06T06:34:00.437Z
2019-10-06T08:39:33.427Z
2019-10-06T08:39:33.427Z
Dan D.
<p>Here's a simple image classifier implemented in TensorFlow Keras (right click to open in new tab): <a href="https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/advanced.ipynb" rel="nofollow noreferrer">https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/advanced.ipynb</a></p>

<p>I altered it a bit to fit with my <strong>2-class dataset</strong>. And the output layer is:</p>

<pre><code>Dense(2, activation=tf.nn.softmax);
</code></pre>

<p>The loss function and optimiser are still the same as in the example in the link above.</p>

<pre><code>loss_fn   = tf.losses.SparseCategoricalCrossentropy();
optimizer = tf.optimizers.Adam();
</code></pre>

<p>I wish to turn it into a classifier with single output neuron as I have only 2 classes in dataset, and sigmoid does the 2 classes good. Tried some combinations of output activation functions + loss functions + optimisers, but the network doesn't work any more (ie. it doesn't converge).</p>

<p>For example, this doesn't work:</p>

<pre><code>//output layer
Dense(1, activation=tf.sigmoid);

//loss and optim
loss_fn   = tf.losses.mse;
optimizer = tf.optimizers.Adagrad(1e-1);
</code></pre>

<p>Which combination of <strong>output activation + loss + optimiser</strong> should work for the single-output-neuron model? <strong>And generically, which loss functions and optimisers should pair well?</strong></p>

<classification><tensorflow><keras><objective-functions><regression>
TF Keras: How to turn this probability-based classifier into single-output-neuron label-based classifier
Dan D.
# TF Keras: How to turn this probability-based classifier into single-output-neuron label-based classifier

(Asked by: username_0 on 2019-10-04)

Here's a simple image classifier implemented in TensorFlow Keras (right click to open in new tab): https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/advanced.ipynb
I altered it a bit to fit with my 2-class dataset. And the output layer is:
Dense(2, activation=tf.nn.softmax);

The loss function and optimiser are still the same as in the example in the link above.
loss_fn   = tf.losses.SparseCategoricalCrossentropy();
optimizer = tf.optimizers.Adam();

I wish to turn it into a classifier with single output neuron as I have only 2 classes in dataset, and sigmoid does the 2 classes good. Tried some combinations of output activation functions + loss functions + optimisers, but the network doesn't work any more (ie. it doesn't converge).
For example, this doesn't work:
//output layer
Dense(1, activation=tf.sigmoid);

//loss and optim
loss_fn   = tf.losses.mse;
optimizer = tf.optimizers.Adagrad(1e-1);

Which combination of output activation + loss + optimiser should work for the single-output-neuron model? And generically, which loss functions and optimisers should pair well?

**Tags:** classification, tensorflow, keras, objective-functions, regression


---

## 1 answers

### Answer by username_0 on 2019-10-06. Score: 0 | Accepted answer

Advice from Neil, yes, output targeting class labels is still classification.
Output range is in contiguous range:

This is regression
For example: Linear activation, it has full numeric range of outputs.

Output targeting class labels is classification:

Single output neuron with sigmoid-like functions. This will classify to 2 classes, although Y data can be normalised to classify more classes.
Multiple output neurons (probabilities of classes) with sigmoid-like functions (softmax is mainly used). This will classify to 2 or more classes.

Multiple combinations of loss functions and optimisers can make the single-neuron output layer work, with different configs for them. Note that learning rates of different optimisers are different, some take 1e-1, some need 1e-3 for good training.
For example, this combination should work:
loss_fn   = tf.losses.LogCosh();
optimizer = tf.optimizers.RMSprop(1e-3);

From my trying out, these other combinations also work for single output neuron with my data (Adam, Adamax, Nadam, RMSprop work when learning_rate=1e-3 instead of 1e-1):
                         Adadelta  Adagrad  Adam  Adamax  Ftrl  Nadam  RMSprop  SGD
BinaryCrossentropy       Yes       Yes      --    --      --    --     --       Yes
CategoricalCrossentropy  Yes       --       --    --      --    --     --       --
CategoricalHinge         --        --       --    --      --    --     --       --
CosineSimilarity         --        --       --    --      --    --     --       --
Hinge                    Yes       Yes      --    --      --    --     --       Yes
Huber                    Yes       Yes      --    --      --    --     --       Yes
LogCosh                  Yes       Yes      --    --      --    --     --       Yes
Poisson                  Yes       Yes      --    --      --    --     --       Yes
SquaredHinge             Yes       Yes      --    --      --    --     --       Yes

KLD: lambda a,b: KLD(a,b)
MAE,MAPE,MSE,MSLE: lambda a,b: Mxxx(a,b)
The above lambdas are direct functions, not classes like SGD, Adadelta, etc.
SparseCategorialCrossentropy: Seems not working with single output neuron.


---

