From 91871cd9188aa61d2a4ebb563c0b5a1e40010f77 Mon Sep 17 00:00:00 2001
From: "Jangberry (Nomad-Debian)" <matt2001@hotmail.fr>
Date: Fri, 10 Nov 2023 19:57:03 +0100
Subject: [PATCH] Some more tests
---
mlp.py | 4 ++--
tests/test_mlp.py | 38 ++++++++++++++++++++++++++++++++++----
2 files changed, 36 insertions(+), 6 deletions(-)
diff --git a/mlp.py b/mlp.py
index 086121d..e8d0765 100644
--- a/mlp.py
+++ b/mlp.py
@@ -168,11 +168,11 @@ def train_mlp(w1, b1, w2, b2, data_train, labels_train, learning_rate, num_epoch
for i in range(num_epochs):
w1, b1, w2, b2, loss = learn_once_cross_entropy(
w1, b1, w2, b2, data_train, labels_train, learning_rate)
- acc.append(test_mlp(w1, b1, w2, b2, data_train, labels_train))
+ acc.append(mlp_test(w1, b1, w2, b2, data_train, labels_train))
return w1, b1, w2, b2, acc
-def test_mlp(w1, b1, w2, b2, data_test, labels_test):
+def mlp_test(w1, b1, w2, b2, data_test, labels_test):
"""
Tests the MLP
diff --git a/tests/test_mlp.py b/tests/test_mlp.py
index 662faf0..0a55684 100644
--- a/tests/test_mlp.py
+++ b/tests/test_mlp.py
@@ -20,16 +20,46 @@ def test_learn_once_mse():
targets = np.random.rand(N, d_out) # create a random targets
w1, b1, w2, b2, loss = learn_once_mse(
- w1, b1, w2, b2, data, targets, 0.1)
-
+ w1, b1, w2, b2, data, targets, 0.1)
+
w1, b1, w2, b2, loss2 = learn_once_mse(
- w1, b1, w2, b2, data, targets, 0.1)
+ w1, b1, w2, b2, data, targets, 0.1)
assert loss2 < loss
+
def test_one_hot():
indices = np.array([2, 0, 1])
result = one_hot(indices)
expected = np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
-
+
np.testing.assert_array_equal(result, expected)
+
+
+def test_learn_once_cross_entropy():
+ N = 30 # number of input data
+ d_in = 3 # input dimension
+ d_h = 3 # number of neurons in the hidden layer
+ d_out = 5 # output dimension (number of neurons of the output layer)
+
+ w1 = 2 * np.random.rand(d_in, d_h) - 1 # first layer weights
+ b1 = np.zeros((1, d_h)) # first layer biaises
+ w2 = 2 * np.random.rand(d_h, d_out) - 1 # second layer weights
+ b2 = np.zeros((1, d_out)) # second layer biaises
+
+ data = np.random.rand(N, d_in) # create a random data
+ targets = np.random.randint(1, d_out, N) # create a random targets
+
+ w1, b1, w2, b2, loss = learn_once_cross_entropy(
+ w1, b1, w2, b2, data, targets, 0.1)
+ w1, b1, w2, b2, loss2 = learn_once_cross_entropy(
+ w1, b1, w2, b2, data, targets, 0.1)
+
+ assert loss2 < loss
+
+def test_run_mlp_training():
+ data, labels = read_cifar.read_cifar("data/cifar-10-batches-py/")
+ data_train, labels_train, data_test, labels_test = read_cifar.split_dataset(
+ data, labels, 0.8)
+ w1, b1, w2, b2, acc = run_mlp_training(
+ data_train, labels_train, data_test, labels_test, 0.1, 1)
\ No newline at end of file
--
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