def test_model(self):
# create the batched examples of data
print("Entered testing phase")
result_dict = {}
self.dump_dict = {}
self.demark_testing()
with torch.no_grad():
for keys in (self.testing_dict.keys()):
self.dump_dict[keys] = []
accuracy = 0
total_example = 0
for x_test_minibatch, y_test_minibatch in (
self.testing_dict[keys]):
total_example += x_test_minibatch.shape[0]
predicted = self.model.predict(
x_test_minibatch.to(torch.long))
accuracy += np.sum(y_test_minibatch.numpy() ==
predicted.squeeze().numpy())
self.update_dump_dict(keys, x_test_minibatch,
y_test_minibatch, predicted)
result_dict[keys] = (accuracy / total_example, total_example)
dump_dict_to_csv(self.dump_dict)
self.log(str(result_dict))
return result_dict
def test_model(self):
# create the batched examples of data
print("Entered testing phase")
result_dict = {}
self.dump_dict = {}
if not hasattr(self, "testing_dict"):
self.demark_testing()
with torch.no_grad():
for keys in (self.testing_dict.keys()):
self.dump_dict[keys] = []
accuracy = 0
total_example = 0
for x_test, y_test in self.testing_dict[keys]:
total_example += 1
y_test = np.asarray(y_test)
x_test = torch.tensor(x_test, dtype=torch.long)
pred, _, _ = self.model(x_test)
if (pred[0][0] > pred[0][1]):
predicted = 0
else:
predicted = 1
if (predicted == (y_test)):
accuracy += 1
self.update_dump_dict(keys, x_test, y_test, predicted)
result_dict[keys] = (accuracy / total_example, total_example)
dump_dict_to_csv(self.dump_dict)
self.log(str(result_dict))
return result_dict
def test_model(self):
# we will do this in batched fashion
print("Entered testing phase")
result_dict = {}
self.dump_dict = {}
if not hasattr(self, "testing_dict"):
self.demark_testing()
with torch.no_grad():
for keys in (self.testing_dict.keys()):
x_, y_ = zip(*self.testing_dict[keys])
x_ = np.asarray(list(x_))
y_ = np.asarray(list(y_))
batch_generator = BatchedDataset(x_, y_)
DataGenerator = DataLoader(
batch_generator,
batch_size=self.batch_size,
drop_last=False
) # we should not drop the last ones, its bad
self.dump_dict[keys] = []
accuracy = 0
total_example = 0
for x_test, y_test in DataGenerator:
x_test = torch.tensor(x_test, dtype=torch.long).to(device)
y_test = torch.tensor(y_test, dtype=torch.long).to(device)
m = x_test.shape[0]
total_example += m
assert m == y_test.shape[0]
x_test = x_test.view(m, -1)
y_test = y_test.view(m, )
pred, _, _ = self.model(x_test)
y_hat = torch.argmax(pred, dim=1)
y_hat = y_hat.view(m, )
accuracy += torch.sum(y_hat == y_test).item()
self.update_dump_dict(keys, x_test, y_test, predicted)
result_dict[keys] = (accuracy / total_example, total_example)
dump_dict_to_csv(self.dump_dict)
self.log(str(result_dict))
return result_dict