From e184eb06704f25cdd82409b2d1633f05fcc6507b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Quentin=20GALLOU=C3=89DEC?= <gallouedec.quentin@gmail.com>
Date: Thu, 27 Oct 2022 16:32:17 +0200
Subject: [PATCH] Clarify instructions about MSE and cross entropy
---
README.md | 21 +++++++++++----------
1 file changed, 11 insertions(+), 10 deletions(-)
diff --git a/README.md b/README.md
index 29d9117..d217ca7 100644
--- a/README.md
+++ b/README.md
@@ -160,29 +160,30 @@ print(loss)
- `w1`, `b1`, `w2` and `b2` the updated weights and biases of the network,
- `loss` the loss, for monitoring purpose.
-For the classification task, the target is the one-hot encoding of the label. Example:
+
+MSE loss is not well suited for a classification task. Instead, we want to use the binary cross-entropy loss. To use this loss, we need the target to be is the one-hot encoding of the desired labels. Example:
```
one_hot(labels=[1 2 0]) = [[0 1 0]
[0 0 1]
[1 0 0]]
```
-Instead of the MSE loss, we prefer to use a binary cross-entropy loss. We also want to replace the last activation layer of the network with a softmax layer.
+We also need that the last activation layer of the network to be a softmax layer.
11. Write the function `one_hot` taking a (n)-D array as parameters and returning the corresponding (n+1)-D one-hot matrix.
-12. Write a function `learn_once_cross_entropy` taking the the same parameters as `learn_once_mse` and returns the same outputs. The function must use a cross entropy loss and the last layer of the network must be a softmax. We admit that $`\frac{\partial C}{\partial Z^{(2)}} = A^{(2)} - Y`$. Where $`Y`$ is a one-hot vector encoding the label.
-13. Write the function `learn_once_cross_entropy` taking as parameters:
+12. Write the function `learn_once_cross_entropy` taking as parameters:
- `w1`, `b1`, `w2` and `b2` the weights and biases of the network,
- `data` a matrix of shape (`batch_size` x `d_in`),
- `labels_train` a vector of size `batch_size`, and
- `learning_rate` the learning rate,
- that perform one gradient descent step using a cross entropy loss. We alos want that the last layer of the network to be a softmax.
- We admit that $`\frac{\partial C}{\partial Z^{(2)}} = A^{(2)} - Y`$. Where $`Y`$ is a one-hot vector encoding the label.
+ that perform one gradient descent step using a binary cross-entropy loss.
+ We admit that $`\frac{\partial C}{\partial Z^{(2)}} = A^{(2)} - Y`$, where $`Y`$ is a one-hot vector encoding the label.
+
The function must return:
- `w1`, `b1`, `w2` and `b2` the updated weights and biases of the network,
- `loss` the loss, for monitoring purpose.
-14. Write the function `train_mlp` taking as parameters:
+13. Write the function `train_mlp` taking as parameters:
- `w1`, `b1`, `w2` and `b2` the weights and biases of the network,
- `data_train` a matrix of shape (`batch_size` x `d_in`),
- `labels_train` a vector of size `batch_size`,
@@ -192,21 +193,21 @@ Instead of the MSE loss, we prefer to use a binary cross-entropy loss. We also w
that perform `num_epoch` of training steps and returns:
- `w1`, `b1`, `w2` and `b2` the updated weights and biases of the network,
- `train_accuracies` the list of train accuracies across epochs as a list of floats.
-15. Write the function `test_mlp` taking as parameters:
+14. Write the function `test_mlp` taking as parameters:
- `w1`, `b1`, `w2` and `b2` the weights and biases of the network,
- `data_test` a matrix of shape (`batch_size` x `d_in`), and
- `labels_test` a vector of size `batch_size`,
testing the network on the test set and returns:
- `test_accuracy` the testing accuracy.
-16. Write the function `run_mlp_training` taking as parameter:
+15. Write the function `run_mlp_training` taking as parameter:
- `data_train`, `labels_train`, `data_test`, `labels_test`, the training and testing data,
- `d_h` the number of neurons in the hidden layer
- `learning_rate` the learning rate, and
- `num_epoch` the number of training epoch,
that train an MLP classifier and return the training accuracies across epochs as a list of floats and the final testing accuracy as a float.
-17. For `split_factor=0.9`, `d_h=64`, `learning_rate=0.1` and `num_epoch=100`, plot the evolution of learning accuracy across learning epochs. Save the graph as an image named `mlp.png` in the `results` directory.
+16. For `split_factor=0.9`, `d_h=64`, `learning_rate=0.1` and `num_epoch=100`, plot the evolution of learning accuracy across learning epochs. Save the graph as an image named `mlp.png` in the `results` directory.
## To go further
--
GitLab