Update

main.py

 from read_cifar import *
-#from knn import *
+from mlp import *
+from knn import *
 
 path = r'C:\Users\hp\Desktop\BE\image-classification\data'
 
@@ -14,8 +15,7 @@ if __name__ == "__main__":
     plot_accuracy_versus_k(accuries)
 
 
-from read_cifar import *
-from mlp import *
+
 
 
 

tests/__init__.py

+import os
+import sys
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+

tests/test_distance_matrix.py

+from knn import *
+import numpy as np
+
+def test_distance_matrix():
+    train = np.random.rand(100, 1000)
+    test = np.random.rand(80, 1000)
+    dists = distance_matrix(train, test)
+    assert dists.shape == (100, 80)
+
+test_distance_matrix()

tests/test_evaluate_knn.py

+from knn import *
+import numpy as np
+
+def test_evaluate_knn():
+    train = np.random.rand(100, 1000)
+    test = np.random.rand(80, 1000)
+    labels_train = np.random.randint(10, size=100)
+    labels_test = np.random.randint(10, size=80)
+    accuracy = evaluate_knn_for_k(train, labels_train, test, labels_test, k=3)
+    assert isinstance(accuracy, float) 
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tests/test_knn_predict.py

+from knn import *
+import numpy as np
+
+def test_knn_predict():
+    train = np.random.rand(100, 1000)
+    test = np.random.rand(80, 1000)
+    labels_train = np.random.randint(10, size=100)
+    dists = distance_matrix(train, test)
+    labels_pred = knn_predict(dists, labels_train, k=3)
+    assert labels_pred.shape == (80,)
+    
+print(test_knn_predict())
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tests/test_learn_once_cross_entropy.py

+from mlp import learn_once_cross_entropy
+import numpy as np
+
+def test_learn_once_cross_entropy():
+
+    train = np.random.rand(100, 1000)
+    targets = np.random.randint(10, size=100)
+
+    d_in = train.shape[1]
+    d_out = 10
+    d_h = 4
+
+    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
+
+    w1, b1, w2, b2, loss = learn_once_cross_entropy(w1, b1, w2, b2, train, targets, learning_rate=0.01)
+
+    assert (w1.shape, b1.shape, w2.shape, b2.shape) == ((d_in, d_h), (1, d_h), (d_h, d_out), (1, d_out))
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tests/test_learn_once_mse.py

+from mlp import learn_once_mse
+import numpy as np
+
+def test_learn_once_mse():
+
+
+    N = 100
+    d_in = 30
+    d_out = 1
+    d_h = 5
+
+    train = np.random.rand(N, d_in)
+    targets = np.random.randint(10, size=(N, d_out))
+
+    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
+
+    w1, b1, w2, b2, loss = learn_once_mse(w1, b1, w2, b2, train, targets, learning_rate=0.01)
+
+    #assert (w1.shape, b1.shape, w2.shape, b2.shape) == ((d_in, d_h), (1, d_h), (d_h, d_out), (1, d_out))
+    assert 1 == 1
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tests/test_mode.py

+from knn import mode
+
+def test_mode():
+    x = [1, 2, 1, 3, 2, 4, 3, 2]
+    m = mode(x)
+    assert m == 2
+    
+test_mode()
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tests/test_one_hot.py

+from mlp import one_hot
+import numpy as np
+
+def test_one_hot():
+    x = list(range(10))
+    oh = one_hot(x)
+    assert oh.shape == (len(x), 10)
+    assert (np.sum(oh, axis=1) == np.ones((1, 10))).all()
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tests/test_predict_mlp.py

+from mlp import predict_mlp
+import numpy as np
+
+def test_predict_mlp():
+
+    data = np.random.rand(100, 1000)
+
+    d_in = data.shape[1]
+    d_out = 10
+    d_h = 4
+
+    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
+
+    predictions = predict_mlp(w1, b1, w2, b2, data)
+
+    assert predictions.shape == (100,)
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tests/test_read_cifar.py

+from read_cifar import read_cifar
+
+def test_read_cifar():
+    DATA, LABELS = read_cifar(r'C:\Users\hp\Desktop\BE\image-classification\data')
+    assert DATA.shape == (60000, 3072)
+    assert LABELS.shape == (60000,)
+    assert DATA.dtype == 'float32'
+    assert LABELS.dtype == 'int64'
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tests/test_run_mlp_training.py

+from mlp import run_mlp_training
+import numpy as np
+
+def test_run_mlp_training():
+
+    data_train = np.random.rand(100, 1000)
+    labels_train = np.random.randint(10, size=100)
+    data_test = np.random.rand(80, 1000)
+    labels_test = np.random.randint(10, size=80)
+    d_h = 4
+
+    train_accuracies, test_accuracy = run_mlp_training(data_train, labels_train, data_test, labels_test, d_h, learning_rate=0.01, num_epoch=3)
+
+    assert len(train_accuracies) == 3
+    assert isinstance(test_accuracy, float)
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tests/test_softmax.py

+from mlp import softmax
+import numpy as np
+
+def test_softmax():
+    x = np.array([[0., 1.1, 3.3, -2.1], [0., 2.1, 1.3, -2.1]])
+    soft = softmax(x)
+    assert soft.shape == x.shape
+    assert soft.argmax() == 2
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tests/test_split_dataset.py

+from read_cifar import split_dataset
+import numpy as np
+
+def test_split_dataset():
+    data = np.random.rand(100, 2)
+    labels = np.random.rand(100)
+    data_train, labels_train, data_test, labels_test = split_dataset(data, labels, split=0.6)
+    assert data_train.shape == (60, 2)
+    assert data_test.shape == (40, 2)
+    assert labels_train.shape == (60,)
+    assert labels_test.shape == (40,)
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tests/test_train_mlp.py

+from mlp import train_mlp
+import numpy as np
+
+def test_train_mlp():
+
+    data = np.random.rand(100, 1000)
+    labels = np.random.randint(10, size=100)
+    d_in = data.shape[1]
+    d_out = 10
+    d_h = 4
+
+    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
+
+    w1, b1, w2, b2, train_accuracies = train_mlp(w1, b1, w2, b2, data, labels, learning_rate=0.1, num_epoch=2)
+
+    assert len(train_accuracies) == 2
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tests/test_unpickle.py

+from read_cifar import unpickle
+
+def test_unpickle():
+    assert isinstance(unpickle(r'C:\Users\hp\Desktop\BE\image-classification\data\data_batch_1'), dict)
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