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(self):
image_layer = Image(image_data=[0.1, 0.3, 0.7], shape=(3, 1))
softmax = SoftMax([1, 2])
model = Model(layers=[image_layer, softmax])
model.compile(build=True)
# bump and grind
gradients = finite_differences(model, False)
gradients = list(flatten(gradients.values()))
jacobian = list(flatten(softmax.jacobian(cache={})))
mod_jacb = list(flatten(model.jacobian()))
assert len(gradients) == len(jacobian)
for g, j, m in zip(gradients, jacobian, mod_jacb):
print(f'{g:8.6f} {j:8.6f} {m:8.6f}')
for g, j, m in zip(gradients, jacobian, mod_jacb):
self.assertAlmostEqual(g, j, delta=0.001)
self.assertAlmostEqual(j, m, delta=0.000001)
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)
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()
# finite_differences(model, False)
print(v)
model.update_data_layer([random.randint(199, 199) for _ in range(pixels)])
v = model.value()
# analytic_jacobian = model.jacobian()
finite_differences(model, False, nmax=500)
print(v)
pass