def cos(tensor: Tensor) -> Tensor:
"""
Function implements the cos function in autograd
:param tensor: (Tensor) Input tensor
:return: (Tensor) Output tensor
"""
# Apply cos
output = np.cos(tensor.data)
# Check if gradient is needed
requires_grad = tensor.requires_grad
# Make backward function if needed
if requires_grad:
def grad_cos(grad: np.ndarray) -> np.ndarray:
"""
Function computes gradient of the cos function
:param grad: (Tensor) Previous gradient
:return: (Tensor) Gradient
"""
return grad * (- np.sin(tensor.data))
dependency = [Dependency(activation=tensor, grad_fn=grad_cos)]
else:
dependency = None
return Tensor(data=output, requires_grad=requires_grad, dependencies=dependency)
def log(tensor: Tensor) -> Tensor:
"""
Function implements the natural logarithm in autograd.
:param tensor: (Tensor) Input tensor
:return: (Tensor) Output tensor
"""
# Apply exp
output = np.log(tensor.data)
# Check if gradient is needed
requires_grad = tensor.requires_grad
# Make backward function if needed
if requires_grad:
def grad_log(grad: np.ndarray) -> np.ndarray:
"""
Function computes gradient of the sin function
:param grad: (Tensor) Previous gradient
:return: (Tensor) Gradient
"""
return grad * (1 / (tensor.data))
dependency = [Dependency(activation=tensor, grad_fn=grad_log)]
else:
dependency = None
return Tensor(data=output, requires_grad=requires_grad, dependencies=dependency)
def exp(tensor: Tensor) -> Tensor:
"""
Function implements the element-wise exponential function
:param tensor: (Tensor) Input tensor
:return: (Tensor) Output tensor
"""
# Apply exp
output = np.exp(tensor.data)
# Check if gradient is needed
requires_grad = tensor.requires_grad
# Make backward function if needed
if requires_grad:
def grad_exp(grad: np.ndarray) -> np.ndarray:
"""
Function computes gradient of the sin function
:param grad: (Tensor) Previous gradient
:return: (Tensor) Gradient
"""
return grad * np.exp(tensor.data)
dependency = [Dependency(activation=tensor, grad_fn=grad_exp)]
else:
dependency = None
return Tensor(data=output, requires_grad=requires_grad, dependencies=dependency)
def leaky_relu(tensor: Tensor, negative_slope: float = 0.2) -> Tensor:
"""
Function implements the leaky-relu function in autograd
:param tensor: (Tensor) Input tensor
:param negative_slope: (float) Negative slope of leaky-relu
:return: (Tensor) Output Tensor
"""
# Apply leaky-relu
output = np.maximum(tensor.data, negative_slope * tensor.data)
# Check if gradient is needed
requires_grad = tensor.requires_grad
# Make backward function if needed
if requires_grad:
def grad_relu(grad: np.ndarray) -> np.ndarray:
"""
Function computes the gradient of the leaky-relu function
:param grad: (Tensor) Previous gradient
:return: (Tensor) Gradient
"""
return grad * np.where(output > 0.0, 1.0, negative_slope)
dependency = [Dependency(activation=tensor, grad_fn=grad_relu)]
else:
dependency = None
return Tensor(data=output, requires_grad=requires_grad, dependencies=dependency)
def sigmoid(tensor: Tensor) -> Tensor:
"""
Function implements the sigmoid function in autograd
:param tensor: (Tensor) Input tensor
:return: (Tensor) Output tensor
"""
# Apply sigmoid
output = 1 / (1 + np.exp(- tensor.data))
# Check if gradient is needed
requires_grad = tensor.requires_grad
# Make backward function if needed
if requires_grad:
def grad_sigmoid(grad: np.ndarray) -> np.ndarray:
"""
Function computes gradient of the sigmoid function
:param grad: (Tensor) Previous gradient
:return: (Tensor) Gradient
"""
return grad * (output * (1 - output))
dependency = [Dependency(activation=tensor, grad_fn=grad_sigmoid)]
else:
dependency = None
return Tensor(data=output, requires_grad=requires_grad, dependencies=dependency)
def elu(tensor: Tensor, alpha: float = 1.) -> Tensor:
"""
Function implements the elu function in autograd
:param tensor: (Tensor) Input tensor
:param alpha: (float) Alpha parameter of exponential slope
:return: (Tensor) Output Tensor
"""
# Apply elu
output = np.where(tensor.data > 0.0, tensor.data, alpha * (np.exp(tensor.data) - 1))
# Check if gradient is needed
requires_grad = tensor.requires_grad
# Make backward function if needed
if requires_grad:
def grad_elu(grad: np.ndarray) -> np.ndarray:
"""
Function computes the gradient of the elu function
:param grad: (Tensor) Previous gradient
:return: (Tensor) Gradient
"""
return grad * (np.where(tensor.data > 0.0, 1.0, alpha * np.exp(tensor.data)))
dependency = [Dependency(activation=tensor, grad_fn=grad_elu)]
else:
dependency = None
return Tensor(data=output, requires_grad=requires_grad, dependencies=dependency)
def sqrt(tensor: Tensor) -> Tensor:
data = np.sqrt(tensor.data)
requires_grad = tensor.requires_grad
if requires_grad:
def grad_fn(grad: np.ndarray) -> np.ndarray:
return grad * (0.5 * (1 / (data + 1e-18)))
last_op = [Dependency(tensor, grad_fn)]
else:
last_op = []
return Tensor(data, requires_grad, last_op)
def tanh(tensor: Tensor) -> Tensor:
data = np.tanh(tensor.data)
requires_grad = tensor.requires_grad
if requires_grad:
def grad_fn(grad: np.ndarray) -> np.ndarray:
return grad * (1 - data * data)
depends_on = [Dependency(tensor, grad_fn)]
else:
depends_on = []
return Tensor(data, requires_grad, depends_on)
def relu(tensor: Tensor) -> Tensor:
data = np.maximum(0, tensor.data)
requires_grad = tensor.requires_grad
if requires_grad:
def grad_fn(grad: np.ndarray) -> np.ndarray:
return grad * np.ones_like(data)
depends_on = [Dependency(tensor, grad_fn)]
else:
depends_on = []
return Tensor(data, requires_grad, depends_on)
def sigmoid(tensor: Tensor) -> Tensor:
data = 1 / (1 + np.exp(tensor.data))
requires_grad = tensor.requires_grad
if requires_grad:
def grad_fn(grad: np.ndarray) -> np.ndarray:
return grad * (1 - data) * data
depends_on = [Dependency(tensor, grad_fn)]
else:
depends_on = []
return Tensor(data, requires_grad, depends_on)
