def test(model, train, validation, test_data):
loss, accuracy = model.evaluate_generator(validation, steps=math.ceil(validation.n/config.BATCH_SIZE))
train_loss, train_accuracy = model.evaluate_generator(train, steps=math.ceil(train.n/config.BATCH_SIZE))
results = evaluate.get_results(model, validation)
probabilities = list(evaluate.transform_binary_probabilities(results))
labels = list(evaluate.get_labels(validation))
test_loss = 0
test_accuracy = 0
test_probabilities = []
test_labels = []
if len(test_data.classes) != 0:
test_loss, test_accuracy = model.evaluate_generator(test_data, steps=math.ceil(test_data.n/config.BATCH_SIZE))
test_results = evaluate.get_results(model, test_data)
test_probabilities = list(evaluate.transform_binary_probabilities(test_results))
test_labels = list(evaluate.get_labels(test_data))
return {
"train_accuracy": float(train_accuracy),
"train_loss": float(train_loss),
"accuracy": float(accuracy),
"loss": float(loss),
"test_accuracy": float(test_accuracy),
"test_loss": float(test_loss),
"probabilities": probabilities,
"labels": labels,
"test_probabilities": test_probabilities,
"test_labels": test_labels,
}
def test_model(model, train, validation, test):
loss, accuracy = model.evaluate_generator(
validation, steps=math.ceil(len(validation) / config.BATCH_SIZE))
train_loss, train_accuracy = model.evaluate_generator(
train, steps=math.ceil(len(train) / config.BATCH_SIZE))
test_loss, test_accuracy = model.evaluate_generator(
test, steps=math.ceil(len(test) / config.BATCH_SIZE))
# think you mean train here
train.reset()
validation.reset()
test.reset()
results = evaluate.get_results(model, validation)
probabilities = list(evaluate.transform_binary_probabilities(results))
labels = list(evaluate.get_labels(validation))
test_results = evaluate.get_results(model, test)
test_probabilities = list(
evaluate.transform_binary_probabilities(test_results))
test_labels = list(evaluate.get_labels(test))
# think you mean train here
train.reset()
validation.reset()
test.reset()
return {
"train_accuracy": float(train_accuracy),
"train_loss": float(train_loss),
"accuracy": float(accuracy),
"loss": float(loss),
"test_accuracy": float(test_accuracy),
"test_loss": float(test_loss),
"probabilities": probabilities,
"labels": labels,
"test_probabilities": test_probabilities,
"test_labels": test_labels,
}
def test_model(model, train, validation, test): # , holdout_test):
train_loss, train_accuracy = model.evaluate_generator(
train, steps=math.ceil(len(train) / config.BATCH_SIZE))
loss, accuracy = model.evaluate_generator(
validation, steps=math.ceil(len(validation) / config.BATCH_SIZE))
test_loss, test_accuracy = model.evaluate_generator(
test, steps=math.ceil(len(test) / config.BATCH_SIZE))
# holdout_test_loss, holdout_test_accuracy = model.evaluate_generator(holdout_test, steps=math.ceil(len(holdout_test)/config.BATCH_SIZE))
train.reset()
validation.reset()
test.reset()
# holdout_test.reset()
# labels - ground truths
# results - predicted results from model
results = evaluate.get_results(model, validation)
probabilities = list(evaluate.transform_binary_probabilities(results))
labels = list(evaluate.get_labels(validation))
test_results = evaluate.get_results(model, test)
test_probabilities = list(
evaluate.transform_binary_probabilities(test_results))
test_labels = list(evaluate.get_labels(test))
# holdout_test_results = evaluate.get_results(model, holdout_test)
# holdout_test_probabilities = list(evaluate.transform_binary_probabilities(holdout_test_results))
# holdout_test_labels = list(evaluate.get_labels(holdout_test))
train.reset()
validation.reset()
test.reset()
# holdout_test.reset()
# get binary predictions
# holdout_binary_predictions = list(evaluate.transform_binary_predictions(holdout_test_results))
test_binary_predictions = list(
evaluate.transform_binary_predictions(test_results))
# get f1 score
test_f1_result = f1_score(test_labels, test_binary_predictions)
# holdout_f1_result = f1_score(holdout_test_labels, holdout_binary_predictions)
return {
"train_accuracy": float(train_accuracy),
"train_loss": float(train_loss),
"accuracy": float(accuracy),
"loss": float(loss),
"test_accuracy": float(test_accuracy),
"test_loss": float(test_loss),
# "holdout_test_accuracy": float(holdout_test_accuracy),
# "holdout_test_loss": float(holdout_test_loss),
"holdout_test_accuracy": float(0),
"holdout_test_loss": float(0),
"probabilities": probabilities,
"labels": labels,
"test_probabilities": test_probabilities,
"test_labels": test_labels,
# "holdout_test_probabilities": holdout_test_probabilities,
# "holdout_test_labels": holdout_test_labels,
"holdout_test_probabilities": 'na',
"holdout_test_labels": 'na',
"test_f1_result": test_f1_result,
# "holdout_f1_result": holdout_f1_result,
"holdout_f1_result": float(0),
}