def test_predict(self):
model = SingleInputModel()
trainer = Trainer(model,
nn.CrossEntropyLoss(),
Adam(model.parameters()),
metrics=[Accuracy()])
trainer.predict(train_x)
def test_evaluate(self):
model = SingleInputModel()
trainer = Trainer(model,
nn.CrossEntropyLoss(),
Adam(model.parameters()),
metrics=[Accuracy()])
trainer.evaluate(train_x, train_y)
def test_train(self):
model = SingleInputModel()
trainer = Trainer(model,
nn.CrossEntropyLoss(),
Adam(model.parameters()),
metrics=[Accuracy()])
history = trainer.train(train_x, train_y, epochs=2)
train_logs = history.train_logs
assert train_logs['Loss'][0] > train_logs['Loss'][1]
def test_train_with_validation_split(self):
model = SingleInputModel()
trainer = Trainer(model,
nn.CrossEntropyLoss(),
Adam(model.parameters()),
metrics=[Accuracy()])
history = trainer.train(train_x,
train_y,
epochs=2,
validation_split=0.2)
train_logs, test_logs = history.train_logs, history.test_logs
assert train_logs['Loss'][0] > train_logs['Loss'][1]
assert test_logs['Loss'][0] > test_logs['Loss'][1]
def test_train_generator(self):
model = SingleInputModel()
trainer = Trainer(model,
nn.CrossEntropyLoss(),
Adam(model.parameters()),
metrics=[Accuracy()])
return F.log_softmax(x, dim=1)
train_data = np.load(os.path.abspath("./data/mnist_train.npz"))
train_x, train_y = train_data['X_train'], train_data['Y_train']
train_x = np.expand_dims(train_x, 1).astype('float32')
train_y = train_y.reshape(-1)
test_data = np.load(os.path.abspath("./data/mnist_test.npz"))
test_x, test_y = test_data['X_test'], test_data['Y_test']
test_x = np.expand_dims(test_x, 1).astype('float32')
test_y = test_y.reshape(-1)
train_x /= 255.0
test_x /= 255.0
model = Net()
trainer = Trainer(model,
F.nll_loss,
Adam(model.parameters()),
metrics=[Accuracy()])
history = trainer.train(train_x,
train_y,
batch_size=64,
epochs=2,
validation_split=0.2)
print(history.train_logs)
print(history.test_logs)