From 2df896a769b83e7b2ab680b4fe777090f20408b0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Quentin=20GALLOU=C3=89DEC?= <gallouedec.quentin@gmail.com>
Date: Fri, 7 Oct 2022 14:15:52 +0200
Subject: [PATCH] replace `evaluate_mlp` by `run_mlp_training` and add
`train_mlp` func
---
README.md | 48 +++++++++++++++++++++++++++++++++++++++---------
1 file changed, 39 insertions(+), 9 deletions(-)
diff --git a/README.md b/README.md
index 0ccd3fe..a7a2eb5 100644
--- a/README.md
+++ b/README.md
@@ -171,14 +171,42 @@ Instead of the MSE loss, we prefer to use a binary cross-entropy loss. We also w
10. Write the function `one_hot` taking a (n)-D array as parameters and returning the corresponding (n+1)-D one-hot matrix.
11. 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.
-12. Write the function `evaluate_mlp` taking as parameter:
+11. 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.
+ The function must return:
+ - `w1`, `b1`, `w2` and `b2` the updated weights and biases of the network,
+ - `loss` the loss, for monitoring purpose.
+12. 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`,
+ - `learning_rate` the learning rate, and
+ - `num_epoch` the number of training epoch,
+
+ 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.
+13. 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.
+14. 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 train and (accuracy computed on the train set) and the test accuracy (accuracy computed on the test set).
-13. For `split_factor=0.9`, `d_h=64`, `learning_rate=0.1` and `num_epoch=10_000`, plot the evolution of accuracies across learning epochs. Save the graph as an image named `mlp.png` in the `results` directory.
+ 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.
+15. 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
@@ -218,16 +246,18 @@ In this section, we present all the items on which the work is evaluated.
- ( /1) The function `knn_predict` works as described
- ( /1) The function `evaluate_knn` works as described
- ( /1) The graph `knn.png` shows the results obtained
-- ( /1.5) The function `learn_once_mse` works as described
+- ( /1) The function `learn_once_mse` works as described
- ( /1) The function `one_hot` works as described
-- ( /1.5) The function `learn_once_cross_entropy` works as described
-- ( /1) The function `evaluate_mlp` works as described
+- ( /1) The function `learn_once_cross_entropy` works as described
+- ( /1) The function `train_mlp` works as described
+- ( /1) The function `test_mlp` works as described
+- ( /1) The function `run_mlp_training` works as described
- ( /1) The graph `mlp.png` shows the results obtained
- ( /1) The project has a good README.
- ( /2) The guidlines about the project structure are all followed
-- ( /2) All functions are documented
-- ( /1) All functions are documented and follow the pydocstyle
-- ( /1) The code is properly formatted
+- ( /1) All functions are documented
+- ( /1) All functions are documented clearly and follow the PEP257 recommendations
+- ( /1) The code is properly formatted, i.e. follow the PEP8 recommendations
**Bonus**
--
GitLab