def test_jacobian_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),
Category(categories),
])
model.compile(build=True)
v = model.value()
self.assertEqual(v, [max(image_data)])
p = model.predict()
self.assertEqual(p, [categories[int(np.argmax(np.array(image_data)))]])
pass
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.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
while gradient == 0 and i < n_weights:
gradient = finite_difference(model, i, epsilon=0.01)
i += 1
print(gradient)
weights = model.weights()
print(weights)
model.compile(build=True)
# ## Prepare images for training
mndata = MNISTData(path='../test/mnist')
images, labels = mndata.training()
train_images = []
train_labels = []
i = 0
train_n = len(images)
while i < len(images) and len(train_images) < train_n:
train_images.append(reduce_image(images[i]) if reduce else images[i])
train_labels.append(labels[i])
i += 1
# ## Stochastic Gradient Descent
gd = GradientDescent(model=model, learning_rate=0.1, labels=predict_labels)
gd.sgd(train_images, train_labels, iterations=20000)
# ## Test the model ###
test_images, test_labels = mndata.testing()
predictions = []
for i, (ti, tl) in enumerate(zip(test_images, test_labels)):
model.update_data_layer(reduce_image(ti) if reduce else ti)
p = model.predict()
if i % 100 == 0:
print(f'{p}, {tl}, {max(model.value()):6.4f}')
predictions.append(dict(label=tl, predicted_label=p, probability=max(model.value())))
predictions = pd.DataFrame(predictions)
predictions.to_csv(f'mnist_{datetime.datetime.now():%Y%m%dT%H%M}.csv')