144 lines
4.4 KiB
Python
144 lines
4.4 KiB
Python
import numpy as np
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from collections import defaultdict, OrderedDict
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from .weight import *
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# used for numbering layers like Keras:
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_layer_counters = defaultdict(lambda: 0)
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class LayerIncompatibility(Exception):
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pass
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class Layer:
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def __init__(self):
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self.parents = []
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self.children = []
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self.weights = OrderedDict()
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self.loss = None # for activity regularizers
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self.input_shape = None
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self.output_shape = None
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kind = self.__class__.__name__
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global _layer_counters
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_layer_counters[kind] += 1
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self.name = "{}_{}".format(kind, _layer_counters[kind])
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self.unsafe = False # disables assertions for better performance
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self.shared = False # as in weight sharing
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def __str__(self):
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return self.name
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# methods we might want to override:
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def forward(self, X):
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raise NotImplementedError("unimplemented", self)
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def forward_deterministic(self, X):
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return self.forward(X)
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def backward(self, dY):
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raise NotImplementedError("unimplemented", self)
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def make_shape(self, parent):
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if self.input_shape == None:
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self.input_shape = parent.output_shape
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if self.output_shape == None:
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self.output_shape = self.input_shape
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def do_feed(self, child):
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self.children.append(child)
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def be_fed(self, parent):
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self.parents.append(parent)
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# TODO: better names for these (still)
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def _propagate(self, edges, deterministic):
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if not self.unsafe:
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assert len(edges) == 1, self
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if deterministic:
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return self.forward_deterministic(edges[0])
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else:
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return self.forward(edges[0])
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def _backpropagate(self, edges):
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if len(edges) == 1:
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return self.backward(edges[0])
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return sum((self.backward(dY) for dY in edges))
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# general utility methods:
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def is_compatible(self, parent):
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return np.all(self.input_shape == parent.output_shape)
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def feed(self, child):
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assert self.output_shape is not None, self
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child.make_shape(self)
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if not child.is_compatible(self):
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fmt = "{} is incompatible with {}: shape mismatch: {} vs. {}"
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raise LayerIncompatibility(fmt.format(self, child, self.output_shape, child.input_shape))
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self.do_feed(child)
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child.be_fed(self)
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return child
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def validate_input(self, X):
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assert X.shape[1:] == self.input_shape, (str(self), X.shape[1:], self.input_shape)
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def validate_output(self, Y):
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assert Y.shape[1:] == self.output_shape, (str(self), Y.shape[1:], self.output_shape)
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def _new_weights(self, name, **kwargs):
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w = Weights(**kwargs)
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assert name not in self.weights, name
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self.weights[name] = w
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return w
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def share(self, node):
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self.weights = node.weights # TODO: this should be all it takes.
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for k, v in self.weights.items():
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vs = getattr(node, k) # hack: key isn't necessarily attribute name!
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setattr(self, k, vs)
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self.shared = True
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def clear_grad(self):
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for name, w in self.weights.items():
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w.g[:] = 0
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@property
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def size(self):
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return sum((w.size for w in self.weights.values()))
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def init(self, allocator):
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ins, outs = self.input_shape[0], self.output_shape[0]
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for k, w in self.weights.items():
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w.allocate(ins, outs, allocator=allocator)
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def propagate(self, values, deterministic):
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if not self.unsafe:
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assert self.parents, self
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edges = []
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for parent in self.parents:
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if parent in values:
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X = values[parent]
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if not self.unsafe:
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self.validate_input(X)
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edges.append(X)
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Y = self._propagate(edges, deterministic)
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if not self.unsafe:
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self.validate_output(Y)
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return Y
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def backpropagate(self, values):
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if not self.unsafe:
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assert self.children, self
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edges = []
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for child in self.children:
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if child in values:
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dY = values[child]
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if not self.unsafe:
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self.validate_output(dY)
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edges.append(dY)
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dX = self._backpropagate(edges)
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if not self.unsafe:
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self.validate_input(dX)
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return dX
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