From d8893c2c0e45578bd1d87e87f4162d9f0ef3dc99 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Quentin=20Gallou=C3=A9dec?=
<45557362+qgallouedec@users.noreply.github.com>
Date: Tue, 4 Oct 2022 22:41:41 +0200
Subject: [PATCH] Remove duplicated line ; add num epochs
---
README.md | 7 +++----
1 file changed, 3 insertions(+), 4 deletions(-)
diff --git a/README.md b/README.md
index 85b3bbf..e436730 100644
--- a/README.md
+++ b/README.md
@@ -106,8 +106,7 @@ This database can be obtained at the address https://www.cs.toronto.edu/~kriz/ci
- `labels_test` the corresponding labels, and
- `k` the number of of neighbors.
This function must return the classification rate (accuracy).
-4. For `split_factor=0.9`, plot the variation of the accuracy as a function of `k` (from 1 to 20). Save the plot as an image under the directory `results`.
-5. For `split_factor=0.9`, plot the variation of the accuracy as a function of `k` (from 1 to 20). Save the plot as an image in the directory `results`.
+4. For `split_factor=0.9`, plot the variation of the accuracy as a function of `k` (from 1 to 20). Save the plot as an image in the directory `results`.
## Artificial Neural Network
@@ -177,8 +176,8 @@ For classification task, we prefer to use a binary cross-entropy loss. We also w
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:
- `data_train`, `labels_train`, `data_test`, `labels_test`, the training and testing data,
- - `learning_rate` the learning rate,
- - `num_epoch` the number of training epoch
+ - `learning_rate` the learning rate, and
+ - `num_epoch` the number of training epoch,
that train an MLP classifier and return the test accuracy computed on the test set.
--
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