def test_mnist_image(self):
image_data = [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 44.25, 122.75, 126.5, 70.5,
0.0, 0.0, 0.0, 0.0, 0.0, 6.5, 1.5, 0.0, 16.75, 203.75, 249.5,
252.0, 252.0, 246.5, 214.75, 37.5, 0.0, 0.0, 0.0, 25.25, 6.0, 0.0,
7.0, 123.5, 166.75, 162.25, 201.25, 252.5, 181.5, 9.25, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 30.0, 167.5, 248.75, 240.5, 129.0, 27.5,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 212.75, 252.5, 208.75,
26.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 34.25,
71.5, 214.75, 202.0, 31.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 13.25, 169.5, 167.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 148.25, 190.5, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 12.75, 210.0, 159.5, 65.75, 167.5, 209.75, 252.5, 78.25,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.25, 168.0, 252.5, 251.25, 238.25,
163.75, 58.75, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 15.75,
106.5, 60.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
]
model = Model(layers=[
Image(image_data=image_data, maximum=256),
CNN(reLU(), (3, 3)),
Dense(reLU(), (3, 1)),
Category([1, 2, 3]),
])
model.compile(build=True)
model.jacobian()
weights = model.weights()
derivatives = [w.derivative() for w in weights]
pass
def test_jacobian_dense_11_cnn_11(self):
image = [random.randint(0, 255) for _ in range(1)]
small_image = Image(image_data=image)
dense = Dense(reLU(), (1, 1))
cnn = CNN(reLU(), (1, 1), (0, 0))
model = Model(layers=[small_image, cnn, dense])
self._run_derivatives_test(model)
def test_jacobian_cnn_31_dense_11(self):
random.seed(44009)
image = [random.randint(0, 255) for _ in range(3)]
model = Model(layers=[
Image(image_data=image, shape=(3, 1)),
CNN(reLU(), (3, 1), name='a'),
Dense(reLU(), (1, 1), name='d'),
])
self._run_derivatives_test(model)
def test_jacobian_dense_12_11(self):
image = Image(image_data=[random.randint(0, 255) for _ in range(2)],
shape=(1, 2))
dense_a = Dense(reLU(), (1, 2))
dense_b = Dense(reLU(), (1, 1))
model = Model(layers=[
image,
dense_a,
dense_b,
])
self._run_derivatives_test(model)
def test_jacobian_cnn_11_11(self):
image = [random.randint(0, 255) for _ in range(2)]
image = Image(image_data=image, shape=(1, 2))
cnn_a = CNN(reLU(), (1, 1), (0, 0), name='a')
cnn_b = CNN(reLU(), (1, 1), (0, 0), name='b')
model = Model(layers=[
image,
cnn_a,
cnn_b,
])
self._run_derivatives_test(model)
def test_jacobian_dense_101_51_softmax_5(self):
random.seed(42)
image = Image(image_data=[random.randint(0, 255) for _ in range(2)],
shape=(1, 2))
dense_a = Dense(reLU(), (10, 1), name='a')
dense_b = Dense(reLU(), (5, 1), name='b')
softmax = SoftMax([1, 2, 3, 4, 5], name='c')
model = Model(layers=[
image,
dense_a,
dense_b,
softmax,
])
self._run_derivatives_test(model)
def test_jacobian_dense_12_12_11(self):
random.seed(42)
image = Image(image_data=[random.randint(0, 255) for _ in range(2)],
shape=(1, 2))
dense_a = Dense(reLU(), (1, 2), name='a')
dense_b = Dense(reLU(), (1, 2), name='b')
dense_c = Dense(reLU(), (1, 1), name='c')
model = Model(layers=[
image,
dense_a,
dense_b,
dense_c,
])
self._run_derivatives_test(model)
def test_jacobian_cnn_33_33_33_dense_11(self):
image = [random.randint(0, 255) for _ in range(49)]
image = Image(image_data=image)
cnn_a = CNN(reLU(), (3, 3), name='a')
cnn_b = CNN(reLU(), (3, 3), name='b')
cnn_c = CNN(reLU(), (3, 3), name='c')
dense = Dense(reLU(), (1, 1), name='d')
model = Model(layers=[
image,
cnn_a,
cnn_b,
cnn_c,
dense,
])
self._run_derivatives_test(model)
def test_jacobian_cnn_31(self):
image = [random.randint(0, 255) for _ in range(3)]
model = Model(layers=[
Image(image_data=image, shape=(3, 1)),
CNN(reLU(), (3, 1), (0, 0), name='a'),
])
weights = self._run_derivatives_test(model)
self.assertEqual(len(weights), 7)
def test_softmax(self):
image_data = [0.2, 0.5, 0.3]
categories = [1, 2, 4]
model = Model(layers=[
Image(image_data=image_data, shape=(3, 1), maximum=1),
Dense(reLU(), (3, 1)),
SoftMax(categories),
])
self._run_derivatives_test(model)
def test_jacobian(self):
mndata = MNISTData(path='../mnist')
images, labels = mndata.training()
r = 49 # a nice number three
image_layer = Image(image_data=images[r], label=labels[r])
dense = Dense(reLU(), (1, 1))
model = Model(layers=[image_layer, dense])
model.compile(build=True)
weight_count = model.weight_count()
# bump and grind
gradients = finite_differences(model, False)
jacobian = list(flatten(dense.jacobian()))
mod_jacb = list(flatten(model.jacobian()))
assert len(gradients) == len(jacobian)
for g, j, m in zip(gradients.values(), jacobian, mod_jacb):
print(f'{g[0]:8.6f} {j:8.6f} {m:8.6f}')
for g, j, m in zip(gradients.values(), jacobian, mod_jacb):
self.assertAlmostEqual(g[0], j, delta=0.001)
self.assertAlmostEqual(j, m, delta=0.000001)
def test_jacobian_dense_31_category_3(self):
image_data = [0.2, 0.5, 0.3]
categories = [1, 2, 4]
model = Model(layers=[
Image(image_data=image_data, shape=(3, 1), maximum=1),
Dense(reLU(), (3, 1)),
Category(categories),
])
self._run_derivatives_test(model)
v = model.value()
# compute value manually for this image_data:
manual_dense_layer = []
for i in range(3):
weights = model._layers[1]._nodes[i]._weights
manual_dense_layer.append(
sum([w.weight() * d for w, d in zip(weights, image_data)]))
max_value = max(manual_dense_layer)
imax_value = int(np.argmax(np.array(manual_dense_layer)))
self.assertEqual(v, [max_value])
p = model.predict()
self.assertEqual(p, [categories[imax_value]])
pass
from j2learn.data.mnist_images import MNISTData
from j2learn.function.function import reLU
from j2learn.layer.category import Category
from j2learn.layer.cnn import CNN
from j2learn.layer.dense import Dense
from j2learn.layer.image import Image
mndata = MNISTData(path='../mnist')
images, labels = mndata.training()
r = 49 # a nice number three
image_layer = Image(image_data=images[r], label=labels[r])
print(image_layer.display())
### test a non-convoluted CNN. Need the same output
identity_cnn = CNN(reLU(), (1, 1), (0, 0), image_layer, weight=1)
for i in range(identity_cnn.shape()[0] * identity_cnn.shape()[1]):
assert identity_cnn.node(i).value() == identity_cnn._underlying_layer.node(
i).value()
print(identity_cnn.display())
### test convolution
first_cnn = CNN(reLU(), (3, 3), (0, 0), identity_cnn)
print(first_cnn.display(threshold=0.5))
second_cnn = CNN(reLU(), (3, 3), (0, 0), first_cnn)
print(second_cnn.display(threshold=0.5))
### more layers
dense = Dense(reLU(), (10, 1), second_cnn)
category = Category([i for i in range(10)], dense)
print(category.node(0).value())
def test_jacobian_dense_21_cnn_12(self):
image = Image(image_data=[random.randint(0, 255) for _ in range(4)])
dense = Dense(reLU(), (2, 1))
cnn = CNN(reLU(), (1, 2), (0, 0))
model = Model(layers=[image, cnn, dense])
self._run_derivatives_test(model)
def test_jacobian_cnn_11(self):
image = [random.randint(0, 255) for _ in range(2)]
image = Image(image_data=image, shape=(2, 1))
cnn = CNN(reLU(), (1, 1), (0, 0))
model = Model(layers=[image, cnn])
self._run_derivatives_test(model)
import random
from j2learn.etc.tools import finite_differences
from j2learn.function.function import reLU
from j2learn.layer.cnn import CNN
from j2learn.layer.dense import Dense
from j2learn.layer.category import Category
from j2learn.layer.image import Image
from j2learn.model.model import Model
pixels = 81
image = Image(image_data=[random.randint(59, 59) for _ in range(pixels)])
cnn_a = CNN(reLU(), (3, 3), name='a')
cnn_b = CNN(reLU(), (3, 3), name='b')
cnn_c = CNN(reLU(), (3, 3), name='c')
dense = Dense(reLU(), (10, 1), name='d')
category = Category([i for i in range(10)])
model = Model(layers=[
image,
cnn_a,
cnn_b,
cnn_c,
dense,
category,
])
model.compile(build=True)
v = model.value()
# v = model.probability()
analytic_jacobian = model.jacobian()
from j2learn.data.mnist_images import MNISTData from j2learn.function.function import reLU from j2learn.layer.category import Category from j2learn.layer.cnn import CNN from j2learn.layer.dense import Dense from j2learn.layer.image import Image from j2learn.model.model import Model from j2learn.etc.tools import finite_difference mndata = MNISTData(path='../mnist') images, labels = mndata.training() r = 49 # a nice number three image_layer = Image(image_data=images[r], label=labels[r]) cnn = CNN(reLU(), (3, 3), (0, 0)) dense = Dense(reLU(), (10, 1)) category = Category([i for i in range(10)]) model = Model(layers=[image_layer, cnn, dense, category]) model.compile(build=True) cat = model.predict() print(cat) prob = model.probability() print(prob) n_weights = model.weight_count() gradient = 0 i = 0
from j2learn.etc.tools import reduce as reduce_image
from j2learn.function.function import reLU
from j2learn.layer.dense import Dense
from j2learn.layer.image import Image
from j2learn.layer.softmax import SoftMax
from j2learn.model.model import Model
from j2learn.regression.gradient_descent import GradientDescent
# ## Use reduced image size?
reduce = True
if reduce:
nx = ny = 14
else:
nx = ny = 28
activation = reLU()
# ## Define model
predict_labels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
model = Model(layers=[
Image(shape=(nx, ny)),
Dense(activation, (100, 1), name='d1'),
SoftMax(predict_labels, name='s1'),
])
model.compile(build=True)
# ## Prepare images for training
mndata = MNISTData(path='../test/mnist')
images, labels = mndata.training()
train_images = []
train_labels = []