Refactor into more traditional project structure
Splits nn.py into pygrad/nn.py and pygrad/tensor.py.
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146
nn.py
146
nn.py
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# neural networks from scratch with numpy
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import numpy as np
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def mean_absolute_error(x, y):
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return np.mean(np.abs(x - y))
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def mean_squared_error(x, y):
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return np.mean(np.power(x - y, 2))
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def cross_entropy_loss(x, y):
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return -np.log(np.exp(y) / np.sum(np.exp(x)))
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# preapre inputs and outputs
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x = np.array([[1, 0]])
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y = np.array([[1]])
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# we're doing xavier initialisation - see <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>
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w1 = np.random.randn(2, 3) / np.sqrt(2)
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w2 = np.random.randn(3, 1) / np.sqrt(3)
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def single_pass():
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# forward pass
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h = np.matmul(x, w1)
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h_hat = np.tanh(h)
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j = np.matmul(h_hat, w2)
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print("prediction {}".format(j))
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# loss calculation
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loss = cross_entropy_loss(j, y)
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print("loss {}".format(loss))
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# TODO Backward pass.
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return
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# initialise layers
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# self.lin1 = nn.Linear(2, 3)
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# self.lin2 = nn.Linear(3, 1)
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# self.loss = nn.MSELoss()
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# and then
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# x = self.lin1(x)
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# x = F.relu(x)
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# x = self.lin2(x)
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# x = F.softmax(x)
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# loss = self.loss(x, y)
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# TODO Add support for numpy arrays.
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class Tensor:
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# TODO Implement 'requires_grad' functionality.
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def __init__(self, value):
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self.value = value
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self.grad = 0
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# Required for backprop.
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self._parents = None
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self._back = None
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# uwu literally the only place where I have type annotations
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def __repr__(self) -> str:
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return f"Tensor(value={self.value}, grad={self.grad})"
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# Save values for the backward pass.
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def _save(self, *args):
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self._parents = args
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# TODO Maybe refactor the functions system? Maybe something like pytorch/tinygrad?
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def add(self, other):
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tensor = Tensor(self.value + other.value)
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tensor._save(self, other)
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def back(upstream):
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return upstream * 1, upstream * 1
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tensor._back = back
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return tensor
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def mul(self, other):
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tensor = Tensor(self.value * other.value)
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tensor._save(self, other)
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def back(upstream):
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a, b = tensor._parents
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return upstream * b.value, upstream * a.value
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tensor._back = back
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return tensor
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def expt(self, exponent):
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tensor = Tensor(self.value ** exponent)
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tensor._save(self)
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def back(upstream):
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a, = tensor._parents
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return [ upstream * exponent * (a.value ** (exponent - 1)) ]
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tensor._back = back
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return tensor
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def reciprocal(self):
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tensor = Tensor(1.0 / self.value)
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tensor._save(self)
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def back(upstream):
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a, = tensor._parents
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return [ -1.0 / (a.value ** 2) ]
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tensor._back = back
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return tensor
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def exp(self):
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tensor = Tensor(np.exp(self.value))
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tensor._save(self)
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def back(upstream):
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a, = tensor._parents
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return [ np.exp(a.value) ]
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tensor._back = back
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return tensor
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# TODO Compute gradients only for tensors that need it.
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def _backprop(tensor, upstream):
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# Backprop through the tensor iff it has any parents.
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if tensor._parents is not None:
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for node, grad in zip(tensor._parents, tensor._back(upstream)):
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# Set the node gradient to the computed gradient.
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node.grad = grad
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# Iterate through all (possible) parent nodes of this node.
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node._backprop(grad)
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def backward(self):
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# Partial of self with respect to self is ALWAYS 1.
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self.grad = 1
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Tensor._backprop(self, self.grad)
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# Small test to see if autograd works.
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def test():
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# Forward pass.
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x, y, z = Tensor(-2), Tensor(5), Tensor(-4)
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q = x.add(y)
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h = q.expt(2)
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w = h.mul(z)
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print(f"q = {q}, w = {w}")
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# Backward pass.
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w.backward()
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print(f"is: dw = {w.grad}, dz = {z.grad}, dy = {y.grad}, dx = {x.grad}")
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@ -0,0 +1,61 @@
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# Neural networks from scratch with numpy.
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import numpy as np
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import pygrad.tensor as tensor
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def mean_absolute_error(x, y):
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return np.mean(np.abs(x - y))
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def mean_squared_error(x, y):
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return np.mean(np.power(x - y, 2))
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def cross_entropy_loss(x, y):
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return -np.log(np.exp(y) / np.sum(np.exp(x)))
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# prepare inputs and outputs
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x = np.array([[1, 0]])
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y = np.array([[1]])
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# we're doing xavier initialisation - see <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>
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w1 = np.random.randn(2, 3) / np.sqrt(2)
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w2 = np.random.randn(3, 1) / np.sqrt(3)
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def single_pass():
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# forward pass
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h = np.matmul(x, w1)
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h_hat = np.tanh(h)
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j = np.matmul(h_hat, w2)
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print("prediction {}".format(j))
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# loss calculation
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loss = cross_entropy_loss(j, y)
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print("loss {}".format(loss))
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# TODO Backward pass.
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return
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# initialise layers
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# self.lin1 = nn.Linear(2, 3)
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# self.lin2 = nn.Linear(3, 1)
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# self.loss = nn.MSELoss()
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# and then
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# x = self.lin1(x)
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# x = F.relu(x)
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# x = self.lin2(x)
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# x = F.softmax(x)
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# loss = self.loss(x, y)
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# Small test to see if autograd works.
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def test():
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# Input tensors.
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x, y, z = tensor.Tensor(-2), tensor.Tensor(5), tensor.Tensor(-4)
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# Forward pass.
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q = x.add(y)
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h = q.expt(2)
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w = h.mul(z)
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print(f"q = {q}, w = {w}")
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# Backward pass.
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w.backward()
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print(f"is: dw = {w.grad}, dz = {z.grad}, dy = {y.grad}, dx = {x.grad}")
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@ -0,0 +1,93 @@
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import numpy as np
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class Tensor:
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# TODO Implement 'requires_grad' functionality.
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def __init__(self, value):
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if not isinstance(value, np.ndarray):
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print(f"{type(value)} is not compatible with {np.ndarray}")
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exit(-1)
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self.value = value
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self.grad = np.zeros_like(value)
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# Required for backprop.
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self._parents = None
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self._back = None
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# uwu literally the only place where I have type annotations
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def __repr__(self) -> str:
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return f"Tensor(value={self.value}, grad={self.grad})"
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# Save values for the backward pass.
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def _save(self, *args):
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self._parents = args
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# TODO Maybe refactor the functions system? Maybe something like pytorch/tinygrad?
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def add(self, other):
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tensor = Tensor(np.add(self.value, other.value))
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tensor._save(self, other)
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def back(upstream):
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return np.dot(np.ones_like(self.value), upstream), np.dot(np.ones_like(self.value), upstream)
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tensor._back = back
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return tensor
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def mul(self, other):
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tensor = Tensor(np.dot(self.value, other.value))
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tensor._save(self, other)
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def back(upstream):
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a, b = tensor._parents
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return np.dot(b.value, upstream), np.dot(a.value.T, upstream)
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tensor._back = back
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return tensor
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def expt(self, exponent):
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tensor = Tensor(self.value ** exponent)
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tensor._save(self)
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def back(upstream):
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a, = tensor._parents
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return [np.dot(exponent * (a.value ** (exponent - 1)), upstream)]
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tensor._back = back
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return tensor
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def reciprocal(self):
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tensor = Tensor(1.0 / self.value)
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tensor._save(self)
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def back(upstream):
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a, = tensor._parents
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return [np.dot(-1.0 / (a.value ** 2), upstream)]
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tensor._back = back
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return tensor
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def exp(self):
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tensor = Tensor(np.exp(self.value))
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tensor._save(self)
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def back(upstream):
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a, = tensor._parents
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return [np.dot(np.exp(a.value), upstream)]
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tensor._back = back
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return tensor
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# TODO Compute gradients only for tensors that need it.
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def _backprop(self, upstream):
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print(upstream)
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# Backprop through the tensor iff it has any parents.
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if self._parents is not None:
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for node, grad in zip(self._parents, self._back(upstream)):
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# Set the node gradient to the computed gradient.
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node.grad = grad
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# Iterate through all (possible) parent nodes of this node.
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node._backprop(grad)
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def backward(self):
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# Partial of self with respect to self is ALWAYS 1.
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self.grad = np.ones_like(self.value)
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self._backprop(self.grad)
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