From cd1f2e194517a42865485d8ad919bc936e505500 Mon Sep 17 00:00:00 2001
From: Milan <milan.cart@ecl20.ec-lyon.fr>
Date: Fri, 10 Nov 2023 13:13:33 +0100
Subject: [PATCH] com
---
mlp.py | 32 +++++++++++---------------------
1 file changed, 11 insertions(+), 21 deletions(-)
diff --git a/mlp.py b/mlp.py
index 3908f75..1d449f9 100644
--- a/mlp.py
+++ b/mlp.py
@@ -8,31 +8,21 @@ def sigmoid(x):
def learn_once_mse(w1, b1, w2, b2, data, targets, learning_rate):
- N_out = len(data) #number of training examples
- # Forward pass
- a0 = data # the data are the input of the first layer
- z1 = np.dot(a0, w1) + b1 # input of the hidden layer
- a1 = sigmoid(z1) # output of the hidden layer (sigmoid activation function)
- z2 = np.dot(a1, w2) + b2 # input of the output layer
- a2 = sigmoid(z2) # output of the output layer (sigmoid activation function)
- predictions = a2 # the predicted values are the outputs of the output layer
+ N_out = len(data)
+
+ a0 = data
+ z1 = np.dot(a0, w1) + b1
+ a1 = sigmoid(z1)
+ z2 = np.dot(a1, w2) + b2
+ a2 = sigmoid(z2)
+ predictions = a2
# Compute loss (MSE)
loss = np.mean(np.square(predictions - targets))
print(f'loss: {loss}')
- # print('shape a1', a1.shape)
- # print('shape w1', w1.shape)
- # print('shape b1', b1.shape)
-
- # print('shape a2', a2.shape)
- # print('shape w2', w2.shape)
- # print('shape b2', b2.shape)
-
- # Backpropagation
- # Backpropagation
delta_a2 = 2 / N_out * (a2 - targets)
- delta_z2 = delta_a2 * (a2 * (1 - a2)) # We divide by the sample size to have an average on the error and avoid big gradient jumps
+ delta_z2 = delta_a2 * (a2 * (1 - a2))
delta_w2 = np.dot(a1.T, delta_z2)
delta_b2 = np.sum(delta_z2, axis = 0, keepdims = True)
@@ -44,12 +34,12 @@ def learn_once_mse(w1, b1, w2, b2, data, targets, learning_rate):
return w1, b1, w2, b2, loss
def one_hot(labels):
- num_classes = int(np.max(labels) + 1) #num_classes = 10
+ num_classes = int(np.max(labels) + 1)
one_hot_matrix = np.eye(num_classes)[labels]
return one_hot_matrix
def softmax_stable(x):
- #We use this function to avoid computing big numbers
+
return(np.exp(x - np.max(x, axis=1, keepdims=True)) / np.exp(x - np.max(x, axis=1, keepdims=True)).sum())
def cross_entropy_loss(y_pred, y_true_one_hot):
--
GitLab