def test_max_pooling_1d_array(self):
# arrange
input = np.array([[[1, 2, -3, 4]]])
expected = np.array([[[2, 4]]])
pooling = MaxPoolingStep(2)
# act
output = pooling.forward_propagation(input)
# assert
self.assertEqual(expected.shape, output.shape)
self.assertTrue(
all(
np.equal(expected.reshape(expected.size),
output.reshape(output.size))))
def test_3_steps_with_sigmoid_output_with_bias_2d_input(self):
# arrange
num_classes = 3
steps = [
ConvolutionalStep(filter_size=(3, 3),
padding=1,
num_of_kernels=1,
x0='ones',
stride=2),
MaxPoolingStep(2),
OutputStep(x0='ones', activation=Sigmoid)
]
network = CnnNetwork(steps)
network.fit([], list(range(num_classes)))
expected_output = np.array([1, 1, 1])
inputs = [
[1, 2, 3, 4, 5],
[5, 6, 7, 8, 5],
[9, 10, 11, 12, 5],
[1, 2, 3, 4, 5],
[5, 6, 7, 8, 5],
]
# act
output = network.forward_propagation(inputs)
# assert
self.assertEqual(expected_output.shape, output.shape)
self.assertTrue(
all(
np.equal(expected_output.reshape(expected_output.size),
output.reshape(output.size))))
def test_max_pooling_2d_array(self):
input = np.array([[[4, 5, 7, 2, 8, 5], [3, 5, 7, 82, 35, 4],
[-3, -7, 23, 6, 2, 0], [-98, 23, 59, -23, 0, 2],
[1, 2, 3, 4, 5, 6], [-7, -6, -2, 7, 0.45, 2]]])
expected = np.array([[[5, 82, 35], [23, 59, 2], [2, 7, 6]]])
pooling = MaxPoolingStep(2)
# act
output = pooling.forward_propagation(input)
# assert
self.assertEqual(expected.shape, output.shape)
self.assertTrue(
all(
np.equal(expected.reshape(expected.size),
output.reshape(output.size))))
def test_pooling_saves_z_with_0_for_unpooled(self):
input = np.array([[[4, 5, 7, 2, 8, 5], [3, 5, 7, 82, 35, 4],
[-3, -7, 23, 6, 2, 0], [-98, 23, 59, -23, 0, 2],
[1, 2, 3, 4, 5, 6], [-7, -6, -2, 7, 0.45, 2]]])
expected_z = [
0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0
]
pooling = MaxPoolingStep(2)
# act
output = pooling.forward_propagation(input)
# assert
self.assertEqual(len(expected_z), len(pooling.z))
self.assertEquals(expected_z, pooling.z)
def test_pooling_back_drop(self):
# arrange
error = 6
inputs = np.array([[[7, 8], [0, 2]]])
expected = np.array([[[0, error], [0, 0]]])
pooling = MaxPoolingStep(2)
output = pooling.forward_propagation(inputs)
# act
backdrop = pooling.back_prop(np.array([error]), None)
# assert
self.assertEqual(expected.shape, backdrop.shape)
self.assertTrue(
all(
np.equal(expected.reshape(expected.size),
output.reshape(backdrop.size))))
def fit():
X = []
y = []
get_cards(X, y)
filter_size = (6, 6)
padding = 0
stride = 1
x0 = 'random'
steps = [
ConvolutionalStep(filter_size=filter_size,
padding=padding,
stride=stride,
num_of_kernels=4,
x0=x0,
activation=Relu),
MaxPoolingStep(3),
ConvolutionalStep(filter_size=filter_size,
padding=padding,
stride=stride,
num_of_kernels=7,
x0=x0,
activation=Relu),
MaxPoolingStep(3),
Flatten(),
OutputStep(x0=x0, activation=Softmax)
]
network = CnnNetwork(steps)
network.fit(
X,
y,
CrossEntropy,
iterations=100,
batch_size=len(X),
learning_rate=1e-5,
verbose=1,
)
def test_max_pooling_3d_array(self):
input = np.array([[
[1, 2, 3],
[
4,
3,
2,
],
[7, -4, 7],
], [[0, -23, 100], [2, 5, 6], [-23.9, 4, 20.7]]])
expected = np.array([[[7]], [[100]]])
pooling = MaxPoolingStep(3)
# act
output = pooling.forward_propagation(input)
# assert
self.assertEqual(expected.shape, output.shape)
self.assertTrue(
all(
np.equal(expected.reshape(expected.size),
output.reshape(output.size))))
def test_3_steps_flow_1d_input(self):
# arrange
steps = [
ConvolutionalStep(filter_size=(3, 3), num_of_kernels=1, x0='ones'),
ReluActivation(),
MaxPoolingStep(2)
]
network = CnnNetwork(steps)
network.fit([], [])
expected_output = np.array([[[9, 5]]])
input = [1, 2, 3, 4, -2, -9]
# act
output = network.forward_propagation(input)
# assert
self.assertEqual(expected_output.shape, output.shape)
self.assertTrue(
all(
np.equal(expected_output.reshape(expected_output.size),
output.reshape(output.size))))