def test_percent_error():
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)
percent_error = model.calculate_percent_error(X_test_vectorized, y_test)
np.testing.assert_almost_equal(percent_error, 0.86, decimal=2)
model = Hebbian(input_dimensions=input_dimensions,
number_of_classes=number_of_classes,
transfer_function="Linear",
seed=15)
percent_error = model.calculate_percent_error(X_test_vectorized, y_test)
np.testing.assert_almost_equal(percent_error, 0.96, decimal=2)
model = Hebbian(input_dimensions=input_dimensions,
number_of_classes=number_of_classes,
transfer_function="Hard_limit",
seed=8)
percent_error = model.calculate_percent_error(X_test_vectorized, y_test)
np.testing.assert_almost_equal(percent_error, 0.91, decimal=2)
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_predict_2():
number_of_classes = 10
number_of_training_samples_to_use = 3
(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]
input_dimensions = X_train_vectorized.shape[0]
model = Hebbian(input_dimensions=input_dimensions,
number_of_classes=number_of_classes,
transfer_function="Linear",
seed=5)
Y_hat = model.predict(X_train_vectorized)
np.testing.assert_almost_equal(Y_hat, np.array( \
[[-4101.41409432, -3820.1709349, -1235.86331202],
[854.07167203, 4061.22006877, 434.40971256],
[-552.17756811, -1791.61373625, -2591.16737069],
[-355.4367891, -3858.75847581, 1320.1141753],
[1087.86080571, -607.49532925, -460.80234811],
[-2459.84338339, -1681.30331925, -255.00327678],
[-460.58803655, -4439.85928602, -1093.82071536],
[4066.25628304, 4814.90762933, 1955.78972208],
[564.64444411, -3117.99849963, -419.49244877],
[-2374.84426405, -2878.08764629, 2979.99404738]]), decimal=2)
model = Hebbian(input_dimensions=input_dimensions,
number_of_classes=number_of_classes,
transfer_function="Hard_limit",
seed=5)
Y_hat = model.predict(X_train_vectorized)
assert (np.allclose(Y_hat, np.array( \
[[0, 0, 0],
[1, 1, 1],
[0, 0, 0],
[0, 0, 1],
[1, 0, 0],
[0, 0, 0],
[0, 0, 0],
[1, 1, 1],
[1, 0, 0],
[0, 0, 1]]),rtol=1e-3, atol=1e-3))
model = Hebbian(input_dimensions=input_dimensions,
number_of_classes=number_of_classes,
transfer_function="Hard_limit",
seed=5)
Y_hat = model.predict(X_train_vectorized)
assert np.allclose(Y_hat, np.array( \
[[0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
[1.00000000e+000, 1.00000000e+000, 1.00000000e+000],
[1.55714531e-240, 0.00000000e+000, 0.00000000e+000],
[4.32278692e-155, 0.00000000e+000, 1.00000000e+000],
[1.00000000e+000, 1.47275575e-264, 7.51766500e-201],
[0.00000000e+000, 0.00000000e+000, 1.79260263e-111],
[9.31445172e-201, 0.00000000e+000, 0.00000000e+000],
[1.00000000e+000, 1.00000000e+000, 1.00000000e+000],
[1.00000000e+000, 0.00000000e+000, 6.55759060e-183],
[0.00000000e+000, 0.00000000e+000, 1.00000000e+000]]),rtol=1e-3, atol=1e-3)
def test_weight_dimension():
input_dimensions = 4
number_of_classes = 9
model = Hebbian(input_dimensions=input_dimensions, number_of_classes=number_of_classes,
transfer_function="Hard_limit")
assert model.weights.ndim == 2 and \
model.weights.shape[0] == number_of_classes and \
model.weights.shape[1] == (input_dimensions + 1)
def test_predict():
input_dimensions = 2
number_of_classes = 2
model = Hebbian(input_dimensions=input_dimensions, number_of_classes=number_of_classes,
transfer_function="Hard_limit",seed=1)
X_train = np.array([[-1.43815556, 0.10089809, -1.25432937, 1.48410426],
[-1.81784194, 0.42935033, -1.2806198, 0.06527391]])
model.initialize_all_weights_to_zeros()
Y_hat = model.predict(X_train)
assert (np.array_equal(Y_hat, np.array([[0,0,0,0], [0,0,0,0]]))) or \
(np.array_equal(Y_hat, np.array([[1,1,1,1], [1,1,1,1]])))
def test_weight_initialization():
input_dimensions = 2
number_of_classes = 5
model = Hebbian(input_dimensions=2, number_of_classes=number_of_classes,
transfer_function="Hard_limit",seed=1)
assert model.weights.ndim == 2 and model.weights.shape[0] == number_of_classes and model.weights.shape[
1] == input_dimensions + 1
weights = np.array([[1.62434536, -0.61175641, -0.52817175],
[-1.07296862, 0.86540763, -2.3015387],
[1.74481176, -0.7612069, 0.3190391],
[-0.24937038, 1.46210794, -2.06014071],
[-0.3224172, -0.38405435, 1.13376944]])
np.testing.assert_allclose(model.weights, weights, rtol=1e-3, atol=1e-3)
model.initialize_all_weights_to_zeros()
assert np.array_equal(model.weights, np.zeros((number_of_classes, input_dimensions + 1)))
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)
"""Block 2"""
model.add(Conv2D(32, (5, 5), strides=(1, 1), padding='same'))
model.add(Activation('relu'))
model.add(
AveragePooling2D(pool_size=(3, 3), strides=(2, 2), padding='same'))
"""Block 3"""
model.add(Conv2D(64, (5, 5), strides=(1, 1), padding='same'))
model.add(Activation('relu'))
model.add(
AveragePooling2D(pool_size=(3, 3), strides=(2, 2), padding='same'))
"""Block 4"""
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(
Hebbian(model.layers[-1].output_shape[1:], lmbda, eta,
connectivity, connectivity_prob))
"""Block 5"""
model.add(Dense(10))
"""Loss Layer"""
model.add(Activation('softmax'))
"""Optimizer"""
model.compile(loss=losses.categorical_crossentropy,
optimizer='adam',
metrics=['accuracy'])
x_train = x_train.astype('float32')
x_valid = x_valid.astype('float32')
x_train /= 255
x_valid /= 255
# check model checkpointing callback which saves only the "best" network according to the 'best_criterion' optional argument (defaults to validation loss)