def __call__(self):
"""Train the model and visualize progress."""
print('Start training')
repeats = repeated(self.problem.dataset.training, self.problem.epochs)
batches = batched(repeats, self.problem.batch_size)
if self.visual:
self.window.start(functools.partial(self._train_visual, batches))
else:
self._train(batches)
def __call__(self):
"""Train the model and visualize progress."""
print('Start training')
repeats = repeated(self.problem.dataset.training, self.problem.epochs)
batches = batched(repeats, self.problem.batch_size)
if self.visual:
self.window.start(functools.partial(self._train_visual, batches))
else:
self._train(batches)
def test_generator(self):
iterable = MockGenerator([1, 2, 3])
repeats = repeated(iterable, 3)
assert iterable.evaluated == 0
list(repeats)
assert iterable.evaluated == 3 * 3
def test_result(self):
iterable = range(14)
repeats = repeated(iterable, 3)
assert list(repeats) == list(iterable) * 3
def test_generator(self):
iterable = MockGenerator([1, 2, 3])
repeats = repeated(iterable, 3)
assert iterable.evaluated == 0
list(repeats)
assert iterable.evaluated == 3 * 3
def test_result(self):
iterable = range(14)
repeats = repeated(iterable, 3)
assert list(repeats) == list(iterable) * 3
# Define model and initialize weights
network = Network([
Layer(len(problem.dataset.training[0].data), Linear),
Layer(700, Relu),
Layer(500, Relu),
Layer(300, Relu),
Layer(len(problem.dataset.training[0].target), Sigmoid)
])
weights = Matrices(network.shapes)
weights.flat = np.random.normal(0, problem.weight_scale, len(weights.flat))
# Classes needed during training
backprop = ParallelBackprop(network, problem.cost)
momentum = Momentum()
decent = GradientDecent()
decay = WeightDecay()
plot = Plot()
# Train the model
repeats = repeated(problem.dataset.training, problem.training_rounds)
batches = batched(repeats, problem.batch_size)
for index, batch in enumerate(batches):
gradient = backprop(weights, batch)
gradient = momentum(gradient, problem.momentum)
weights = decent(weights, gradient, problem.learning_rate)
weights = decay(weights, problem.weight_decay)
# Show progress
plot(compute_costs(network, weights, problem.cost, batch))
every(problem.evaluate_every // problem.batch_size, index, evaluate,
index, network, weights, problem.dataset.testing)
