From ff85123939f98c471a4fce79590812f68013bae8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Quentin=20GALLOU=C3=89DEC?= <gallouedec.quentin@gmail.com>
Date: Thu, 27 Oct 2022 16:38:21 +0200
Subject: [PATCH] Better format
---
README.md | 4 +---
1 file changed, 1 insertion(+), 3 deletions(-)
diff --git a/README.md b/README.md
index 514c68e..69ca442 100644
--- a/README.md
+++ b/README.md
@@ -177,9 +177,7 @@ We also need that the last activation layer of the network to be a softmax layer
- `labels_train` a vector of size `batch_size`, and
- `learning_rate` the learning rate,
- 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:
+ 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.
13. Write the function `train_mlp` taking as parameters:
--
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