def test_train_step():
data = Tensor.from_builtin([[1, 2, 3], [4, 5, 0]])
targets = Tensor.from_builtin([[1, 0], [0, 1]])
weights = Tensor.from_builtin([[2, 0], [1, 1], [0, 3]])
biases = Tensor.from_builtin([-2, -7])
def compute_loss():
logits = matrix_multiply(data, weights) + biases
probabilities = logits.softmax(-1)
return -(probabilities.log() * targets).sum()
loss = compute_loss()
gradients = Gradients.trace(loss)
weights -= Tensor.from_numpy(gradients[weights])
biases -= Tensor.from_numpy(gradients[biases])
assert compute_loss().data < loss.data
def make_normalizer(train_features: np.ndarray):
mean = np.mean(train_features)
std = np.std(train_features)
return lambda features: Tensor.from_numpy((features - mean) / std)
def test_unequal_array():
assert Tensor.from_numpy(np.array([1, 2, 4.5])) != Tensor.from_numpy(
np.array([1, 2, 5.5]))
def test_convert():
tensor = Tensor.convert([1, 2])
assert tensor == Tensor.from_numpy(np.array([1, 2]))
assert tensor == Tensor.from_builtin([1, 2])
assert tensor is Tensor.convert(tensor)
def test_equal_array():
assert Tensor.from_numpy(np.array([1, 2, 4.5])) == Tensor.from_numpy(
np.array([1, 2, 4.5]))
