def predict_it(train_result, test_id, test_set):
nn, tr, train_time = result
predict_start = time.time()
prediction_result = nn.predict(test_set[0])
pred_time = time.time() - predict_start
report = NeuralNet.score_report(test_set[1], prediction_result.predicted)
print(
"[{id}][{train_time:3.1f}/{pred_time:3.1f}]:{nn}:\n{report}\n".format(
id=test_id,
train_time=train_time,
pred_time=pred_time,
nn=nn,
report=NeuralNet.format_score(report)))
return prediction_result, report, pred_time
def _start_job(self, job_id):
job = self.job_list[job_id]
job.create_neuralnet()
job.status = 'training:started'
job.neuralnet.train(
lambda neuralnet, train_data: self._train_callback(job, train_data))
job.status = 'prediction:started'
for test_set_id, test_set in job.test_sets.items():
test_set_x, test_set_y = test_set
prediction_result = job.neuralnet.predict(test_set_x)
score = NeuralNet.score_report(
test_set_y, prediction_result.predicted)
self.add_to_prediction_log(job, test_set_id, score)
# print("{0}:\n{1}\n".format(
# job.neuralnet, NeuralNet.format_score_report(score)))
job.status = 'completed'
return job
# plt.show(block=False)
classifier = NeuralNetWithAdam(train_x, train_y,
hidden_layers=(6,),
iteration_count=1000,
learning_rate=0.005,
minibatch_size=64,
epochs=3,
learning_rate_decay=0.2,
# beta1=0.8,
shuffle=True)
train_result = classifier.train(train_cb)
prediction = classifier.predict(test_x)
report = NeuralNet.score_report(test_y, prediction.predicted)
print("Classification report for {0}:\n{1}\n".format(
classifier, NeuralNet.format_score(report)))
classifier = svm.SVC(gamma=0.001)
classifier.fit(train_x, train_y)
predicted = classifier.predict(test_x)
report = NeuralNet.score_report(test_y, predicted)
print("Classification report for {0}:\n{1}\n".format(
classifier, NeuralNet.format_score(report)))
# analyse(classifier, expected, predicted)
# print("train MLp")
valid_x = valid_set[0]
valid_y = valid_set[1]
return (train_x, train_y), (test_x, test_y), (valid_x, valid_y)
(train_x, train_y), (test_x, test_y), (valid_x, valid_y) = get_datasets()
def train_callback(nn, it):
if (it.total_iteration_index % 10 == 0):
print("it:{it:>5}, cost:{cost:6.2f}".format(
it=it.total_iteration_index, cost=it.cost))
nn = NeuralNetWithAdam(train_x,
train_y,
hidden_layers=(15, ),
iteration_count=200,
learning_rate=0.01,
minibatch_size=0,
epochs=1)
print("Training ...")
train_result = nn.train(train_callback)
prediction_result = nn.predict(test_x)
report = NeuralNet.score_report(test_y, prediction_result.predicted)
print("{0}:\n{1}\n".format(nn, NeuralNet.format_score(report)))