def test_confusion_matrix():
# Read mnist data
number_of_classes = 10
number_of_test_samples_to_use = 100
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()
X_test_vectorized = ((X_test.reshape(
X_test.shape[0], -1)).T)[:, 0:number_of_test_samples_to_use]
y_test = y_test[0:number_of_test_samples_to_use]
input_dimensions = X_test_vectorized.shape[0]
model = Hebbian(input_dimensions=input_dimensions,
number_of_classes=number_of_classes,
transfer_function="Sigmoid",
seed=5)
confusion_matrix = model.calculate_confusion_matrix(
X_test_vectorized, y_test)
assert np.array_equal(confusion_matrix, np.array(\
[ [0.,6.,2.,0.,0.,0.,0.,0.,0.,0.],
[1., 10.,0.,3.,0.,0.,0.,0.,0.,0.],
[0.,6.,1.,1.,0.,0.,0.,0.,0.,0.],
[0.,8.,0.,3.,0.,0.,0.,0.,0.,0.],
[1.,11.,1.,1.,0.,0.,0.,0.,0.,0.],
[1.,5.,1.,0.,0.,0.,0.,0.,0.,0.],
[0.,9.,0.,1.,0.,0.,0.,0.,0.,0.],
[0.,7.,4.,1.,3.,0.,0.,0.,0.,0.],
[0.,2.,0.,0.,0.,0.,0.,0.,0.,0.],
[1.,7.,2.,1.,0.,0.,0.,0.,0.,0.]]))
model = Hebbian(input_dimensions=input_dimensions,
number_of_classes=number_of_classes,
transfer_function="Linear",
seed=5)
confusion_matrix = model.calculate_confusion_matrix(
X_test_vectorized, y_test)
assert np.array_equal(confusion_matrix, np.array( \
[[0., 1., 1., 0., 5., 0., 0., 1., 0., 0.],
[0., 1., 0., 0., 2., 0., 0., 11., 0., 0.],
[0., 1., 0., 1., 4., 0., 1., 1., 0., 0.],
[0., 0., 0., 3., 3., 0., 1., 4., 0., 0.],
[0., 4., 0., 0., 6., 0., 0., 4., 0., 0.],
[0., 1., 1., 0., 2., 0., 0., 2., 0., 1.],
[0., 1., 0., 0., 3., 0., 0., 6., 0., 0.],
[0., 0., 0., 0., 8., 0., 0., 4., 3., 0.],
[0., 0., 1., 0., 1., 0., 0., 0., 0., 0.],
[0., 2., 0., 1., 1., 0., 0., 7., 0., 0.]]))
model = Hebbian(input_dimensions=input_dimensions,
number_of_classes=number_of_classes,
transfer_function="Hard_limit",
seed=5)
confusion_matrix = model.calculate_confusion_matrix(
X_test_vectorized, y_test)
assert np.array_equal(confusion_matrix, np.array( \
[[0., 6., 2., 0., 0., 0., 0., 0., 0., 0.],
[1., 10., 0., 3., 0., 0., 0., 0., 0., 0.],
[0., 6., 1., 1., 0., 0., 0., 0., 0., 0.],
[0., 8., 0., 3., 0., 0., 0., 0., 0., 0.],
[1., 12., 0., 1., 0., 0., 0., 0., 0., 0.],
[1., 5., 1., 0., 0., 0., 0., 0., 0., 0.],
[0., 9., 0., 1., 0., 0., 0., 0., 0., 0.],
[0., 7., 4., 1., 3., 0., 0., 0., 0., 0.],
[0., 2., 0., 0., 0., 0., 0., 0., 0., 0.],
[1., 7., 2., 1., 0., 0., 0., 0., 0., 0.]]))
def test_training():
number_of_classes = 10
number_of_training_samples_to_use = 1000
number_of_test_samples_to_use = 100
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()
X_train_vectorized = ((X_train.reshape(
X_train.shape[0], -1)).T)[:, 0:number_of_training_samples_to_use]
y_train = y_train[0:number_of_training_samples_to_use]
X_test_vectorized = ((X_test.reshape(
X_test.shape[0], -1)).T)[:, 0:number_of_test_samples_to_use]
y_test = y_test[0:number_of_test_samples_to_use]
input_dimensions = X_test_vectorized.shape[0]
model = Hebbian(input_dimensions=input_dimensions,
number_of_classes=number_of_classes,
transfer_function="Hard_limit",
seed=5)
model.initialize_all_weights_to_zeros()
percent_error = []
for k in range(10):
model.train(X_train_vectorized,
y_train,
batch_size=300,
num_epochs=2,
alpha=0.1,
gamma=0.1,
learning="Delta")
percent_error.append(
model.calculate_percent_error(X_test_vectorized, y_test))
confusion_matrix = model.calculate_confusion_matrix(
X_test_vectorized, y_test)
assert (np.array_equal(confusion_matrix, np.array( \
[[8., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[1., 13., 0., 0., 0., 0., 0., 0., 0., 0.],
[1., 0., 7., 0., 0., 0., 0., 0., 0., 0.],
[2., 0., 1., 8., 0., 0., 0., 0., 0., 0.],
[1., 0., 0., 1., 12., 0., 0., 0., 0., 0.],
[4., 0., 1., 0., 0., 2., 0., 0., 0., 0.],
[3., 0., 2., 0., 0., 0., 5., 0., 0., 0.],
[1., 0., 0., 2., 0., 0., 0., 11., 0., 1.],
[2., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[1., 0., 0., 0., 0., 0., 0., 1., 0., 9.]]))) or \
(np.array_equal(confusion_matrix, np.array( \
[[8., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[1., 13., 0., 0., 0., 0., 0., 0., 0., 0.],
[1., 0., 6., 0., 0., 0., 1., 0., 0., 0.],
[2., 0., 1., 8., 0., 0., 0., 0., 0., 0.],
[2., 0., 0., 1., 11., 0., 0., 0., 0., 0.],
[4., 0., 1., 0., 0., 2., 0., 0., 0., 0.],
[4., 0., 1., 0., 0., 0., 5., 0., 0., 0.],
[2., 0., 0., 1., 0., 0., 0., 12., 0., 0.],
[1., 0., 0., 0., 0., 0., 0., 0., 1., 0.],
[3., 0., 0., 0., 0., 0., 0., 3., 0., 5.]])))
assert np.allclose(percent_error,
np.array([0.74, 0.35, 0.32, 0.3, 0.28, 0.32, 0.25, 0.26, 0.3, 0.25]),rtol=1e-3, atol=1e-3) or \
np.allclose(percent_error,
np.array([0.8 ,0.37,0.36,0.32,0.31,0.31,0.29,0.29,0.24,0.29]), rtol=1e-3, atol=1e-3)
model = Hebbian(input_dimensions=input_dimensions,
number_of_classes=number_of_classes,
transfer_function="Linear",
seed=5)
percent_error = []
for k in range(10):
model.train(X_train_vectorized,
y_train,
batch_size=300,
num_epochs=2,
alpha=0.1,
gamma=0.1,
learning="Filtered")
percent_error.append(
model.calculate_percent_error(X_test_vectorized, y_test))
confusion_matrix = model.calculate_confusion_matrix(
X_test_vectorized, y_test)
assert np.array_equal(confusion_matrix, np.array( \
[[8., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 11., 0., 1., 0., 0., 0., 0., 2., 0.],
[2., 0., 4., 1., 0., 0., 0., 1., 0., 0.],
[2., 0., 1., 8., 0., 0., 0., 0., 0., 0.],
[1., 0., 0., 0., 11., 0., 0., 1., 0., 1.],
[5., 0., 0., 1., 0., 0., 0., 1., 0., 0.],
[3., 0., 1., 0., 0., 0., 6., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 15., 0., 0.],
[0., 0., 1., 0., 0., 0., 0., 0., 1., 0.],
[0., 0., 0., 0., 0., 0., 0., 8., 1., 2.]]))
np.testing.assert_almost_equal(
percent_error,
[0.34, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34],
decimal=2)