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)
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
while gradient == 0 and i < n_weights:
gradient = finite_difference(model, i, epsilon=0.01)
i += 1
print(gradient)
weights = model.weights()
print(weights)
weight_counts = model.weight_counts()
print(weight_counts)
n_weights = model.weight_count()