def check_lang_id(tracker):
import models
model = models.LanguageIDModel()
dataset = backend.LanguageIDDataset(model)
detected_parameters = None
for batch_size, word_length in ((1, 1), (2, 1), (2, 6), (4, 8)):
start = dataset.dev_buckets[-1, 0]
end = start + batch_size
inp_xs, inp_y = dataset._encode(dataset.dev_x[start:end], dataset.dev_y[start:end])
inp_xs = inp_xs[:word_length]
output_node = model.run(inp_xs)
verify_node(output_node, 'node', (batch_size, len(dataset.language_names)), "LanguageIDModel.run()")
trace = trace_node(output_node)
for inp_x in inp_xs:
assert inp_x in trace, "Node returned from LanguageIDModel.run() does not depend on all of the provided inputs (xs)"
# Word length 1 does not use parameters related to transferring the
# hidden state across timesteps, so initial parameter detection is only
# run for longer words
if word_length > 1:
if detected_parameters is None:
detected_parameters = [node for node in trace if isinstance(node, nn.Parameter)]
for node in trace:
assert not isinstance(node, nn.Parameter) or node in detected_parameters, (
"Calling LanguageIDModel.run() multiple times should always re-use the same parameters, but a new nn.Parameter object was detected")
for batch_size, word_length in ((1, 1), (2, 1), (2, 6), (4, 8)):
start = dataset.dev_buckets[-1, 0]
end = start + batch_size
inp_xs, inp_y = dataset._encode(dataset.dev_x[start:end], dataset.dev_y[start:end])
inp_xs = inp_xs[:word_length]
loss_node = model.get_loss(inp_xs, inp_y)
trace = trace_node(loss_node)
for inp_x in inp_xs:
assert inp_x in trace, "Node returned from LanguageIDModel.run() does not depend on all of the provided inputs (xs)"
assert inp_y in trace, "Node returned from LanguageIDModel.get_loss() does not depend on the provided labels (y)"
for node in trace:
assert not isinstance(node, nn.Parameter) or node in detected_parameters, (
"LanguageIDModel.get_loss() should not use additional parameters not used by LanguageIDModel.run()")
tracker.add_points(2) # Partial credit for passing sanity checks
model.train(dataset)
test_predicted_probs, test_predicted, test_correct = dataset._predict('test')
test_accuracy = np.mean(test_predicted == test_correct)
accuracy_threshold = 0.81
if test_accuracy >= accuracy_threshold:
print("Your final test set accuracy is: {:%}".format(test_accuracy))
tracker.add_points(5)
else:
print("Your final test set accuracy ({:%}) must be at least {:.0%} to receive full points for this question".format(test_accuracy, accuracy_threshold))
def check_lang_id(tracker):
import models, backend
model = models.LanguageIDModel()
assert model.get_data_and_monitor == backend.get_data_and_monitor_lang_id, "LanguageIDModel.get_data_and_monitor is not set correctly"
assert model.learning_rate > 0, "LanguageIDModel.learning_rate is not set correctly"
model.train()
stats = backend.get_stats(model)
accuracy_threshold = 0.81
if stats['dev_accuracy'] >= accuracy_threshold:
tracker.add_points(2)
else:
print(
"Your final validation accuracy ({:%}) must be at least {:.0%} to receive points for this question"
.format(stats['dev_accuracy'], accuracy_threshold))
def main():
import models
model = models.PerceptronModel(3)
dataset = PerceptronDataset(model)
model.train(dataset)
model = models.RegressionModel()
dataset = RegressionDataset(model)
model.train(dataset)
model = models.DigitClassificationModel()
dataset = DigitClassificationDataset(model)
model.train(dataset)
model = models.LanguageIDModel()
dataset = LanguageIDDataset(model)
model.train(dataset)