1046 lines
32 KiB
Python
1046 lines
32 KiB
Python
#!/usr/bin/env python3
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import numpy as np
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# ugly shorthand:
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nf = np.float32
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nfa = lambda x: np.array(x, dtype=nf)
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ni = np.int
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nia = lambda x: np.array(x, dtype=ni)
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# just for speed, not strictly essential:
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from scipy.special import expit as sigmoid
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# used for numbering layers like Keras:
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from collections import defaultdict
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# Initializations
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# note: these are currently only implemented for 2D shapes.
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def init_he_normal(size, ins, outs):
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s = np.sqrt(2 / ins)
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return np.random.normal(0, s, size=size)
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def init_he_uniform(size, ins, outs):
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s = np.sqrt(6 / ins)
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return np.random.uniform(-s, s, size=size)
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# Loss functions
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class Loss:
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def mean(self, r):
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return np.average(self.f(r))
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def dmean(self, r):
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d = self.df(r)
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return d / len(d)
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class Squared(Loss):
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def f(self, r):
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return np.square(r)
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def df(self, r):
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return 2 * r
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class SquaredHalved(Loss):
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def f(self, r):
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return np.square(r) / 2
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def df(self, r):
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return r
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class SomethingElse(Loss):
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# generalizes Absolute and SquaredHalved (|dx| = 1)
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# plot: https://www.desmos.com/calculator/fagjg9vuz7
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def __init__(self, a=4/3):
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assert 1 <= a <= 2, "parameter out of range"
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self.a = nf(a / 2)
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self.b = nf(2 / a)
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self.c = nf(2 / a - 1)
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def f(self, r):
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return self.a * np.abs(r)**self.b
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def df(self, r):
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return np.sign(r) * np.abs(r)**self.c
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# Optimizers
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class Optimizer:
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def __init__(self, alpha=0.1):
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self.alpha = nf(alpha)
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self.reset()
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def reset(self):
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pass
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def compute(self, dW, W):
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return -self.alpha * dW
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def update(self, dW, W):
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W += self.compute(dW, W)
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# the following optimizers are blatantly lifted from tiny-dnn:
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# https://github.com/tiny-dnn/tiny-dnn/blob/master/tiny_dnn/optimizers/optimizer.h
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class Momentum(Optimizer):
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def __init__(self, alpha=0.01, lamb=0, mu=0.9, nesterov=False):
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self.alpha = np.asfarray(alpha) # learning rate
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self.lamb = np.asfarray(lamb) # weight decay
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self.mu = np.asfarray(mu) # momentum
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self.nesterov = bool(nesterov)
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self.reset()
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def reset(self):
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self.dWprev = None
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def compute(self, dW, W):
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if self.dWprev is None:
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#self.dWprev = np.zeros_like(dW)
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self.dWprev = np.copy(dW)
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V = self.mu * self.dWprev - self.alpha * (dW + W * self.lamb)
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self.dWprev[:] = V
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if self.nesterov: # TODO: is this correct? looks weird
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return self.mu * V - self.alpha * (dW + W * self.lamb)
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else:
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return V
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class Adam(Optimizer):
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def __init__(self, alpha=0.001, b1=0.9, b2=0.999, b1_t=0.9, b2_t=0.999, eps=1e-8):
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self.alpha = nf(alpha) # learning rate
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self.b1 = nf(b1) # decay term
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self.b2 = nf(b2) # decay term
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self.b1_t_default = nf(b1_t) # decay term power t
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self.b2_t_default = nf(b2_t) # decay term power t
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self.eps = nf(eps)
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self.reset()
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def reset(self):
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self.mt = None
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self.vt = None
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self.b1_t = self.b1_t_default
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self.b2_t = self.b2_t_default
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def compute(self, dW, W):
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if self.mt is None:
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self.mt = np.zeros_like(W)
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if self.vt is None:
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self.vt = np.zeros_like(W)
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# decay
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self.b1_t *= self.b1
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self.b2_t *= self.b2
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self.mt[:] = self.b1 * self.mt + (1 - self.b1) * dW
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self.vt[:] = self.b2 * self.vt + (1 - self.b2) * dW * dW
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return -self.alpha * (self.mt / (1 - self.b1_t)) \
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/ np.sqrt((self.vt / (1 - self.b2_t)) + self.eps)
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# Abstract Layers
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_layer_counters = defaultdict(lambda: 0)
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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.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.size = None # total weight count (if any)
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self.unsafe = False # disables assertions for better performance
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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 F(self, X):
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raise NotImplementedError("unimplemented", self)
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def dF(self, dY):
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raise NotImplementedError("unimplemented", self)
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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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def make_shape(self, shape):
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if not self.unsafe:
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assert shape is not None
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if self.output_shape is None:
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self.output_shape = shape
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return shape
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# TODO: rename this multi and B crap to something actually relevant.
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def multi(self, B):
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if not self.unsafe:
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assert len(B) == 1, self
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return self.F(B[0])
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def dmulti(self, dB):
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if len(dB) == 1:
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return self.dF(dB[0])
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else:
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dX = None
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for dY in dB:
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if dX is None:
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dX = self.dF(dY)
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else:
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dX += self.dF(dY)
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return dX
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# general utility methods:
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def compatible(self, parent):
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if self.input_shape is None:
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# inherit shape from output
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shape = self.make_shape(parent.output_shape)
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if shape is None:
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return False
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self.input_shape = shape
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if np.all(self.input_shape == parent.output_shape):
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return True
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else:
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return False
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def feed(self, child):
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if not child.compatible(self):
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fmt = "{} is incompatible with {}: shape mismatch: {} vs. {}"
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raise Exception(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 forward(self, lut):
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if not self.unsafe:
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assert len(self.parents) > 0, self
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B = []
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for parent in self.parents:
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# TODO: skip over irrelevant nodes (if any)
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X = lut[parent]
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if not self.unsafe:
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self.validate_input(X)
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B.append(X)
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Y = self.multi(B)
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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 backward(self, lut):
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if not self.unsafe:
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assert len(self.children) > 0, self
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dB = []
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for child in self.children:
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# TODO: skip over irrelevant nodes (if any)
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dY = lut[child]
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if not self.unsafe:
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self.validate_output(dY)
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dB.append(dY)
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dX = self.dmulti(dB)
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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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# Final Layers
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class Sum(Layer):
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def multi(self, B):
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return np.sum(B, axis=0)
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def dmulti(self, dB):
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#assert len(dB) == 1, "unimplemented"
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return dB[0] # TODO: does this always work?
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class Input(Layer):
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def __init__(self, shape):
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assert shape is not None
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super().__init__()
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self.shape = tuple(shape)
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self.input_shape = self.shape
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self.output_shape = self.shape
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def F(self, X):
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return X
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def dF(self, dY):
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#self.dY = dY
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return np.zeros_like(dY)
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class Affine(Layer):
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def __init__(self, a=1, b=0):
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super().__init__()
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self.a = nf(a)
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self.b = nf(b)
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def F(self, X):
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return self.a * X + self.b
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def dF(self, dY):
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return dY * self.a
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class Sigmoid(Layer): # aka Logistic
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def F(self, X):
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from scipy.special import expit as sigmoid
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self.sig = sigmoid(X)
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return X * self.sig
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def dF(self, dY):
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return dY * self.sig * (1 - self.sig)
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class Tanh(Layer):
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def F(self, X):
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self.sig = np.tanh(X)
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return X * self.sig
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def dF(self, dY):
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return dY * (1 - self.sig * self.sig)
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class Relu(Layer):
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def F(self, X):
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self.cond = X >= 0
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return np.where(self.cond, X, 0)
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def dF(self, dY):
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return np.where(self.cond, dY, 0)
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class Elu(Layer):
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# paper: https://arxiv.org/abs/1511.07289
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def __init__(self, alpha=1):
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super().__init__()
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self.alpha = nf(alpha)
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def F(self, X):
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self.cond = X >= 0
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self.neg = np.exp(X) - 1
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return np.where(self.cond, X, self.neg)
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def dF(self, dY):
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return dY * np.where(self.cond, 1, self.neg + 1)
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class GeluApprox(Layer):
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# paper: https://arxiv.org/abs/1606.08415
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# plot: https://www.desmos.com/calculator/ydzgtccsld
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def F(self, X):
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self.a = 1.704 * X
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self.sig = sigmoid(self.a)
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return X * self.sig
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def dF(self, dY):
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return dY * self.sig * (1 + self.a * (1 - self.sig))
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class Dense(Layer):
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def __init__(self, dim, init=init_he_uniform):
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super().__init__()
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self.dim = ni(dim)
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self.output_shape = (dim,)
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self.weight_init = init
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self.size = None
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def make_shape(self, shape):
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super().make_shape(shape)
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if len(shape) != 1:
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return False
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self.nW = self.dim * shape[0]
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self.nb = self.dim
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self.size = self.nW + self.nb
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return shape
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def init(self, W, dW):
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ins, outs = self.input_shape[0], self.output_shape[0]
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self.W = W
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self.dW = dW
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self.coeffs = self.W[:self.nW].reshape(ins, outs)
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self.biases = self.W[self.nW:].reshape(1, outs)
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self.dcoeffs = self.dW[:self.nW].reshape(ins, outs)
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self.dbiases = self.dW[self.nW:].reshape(1, outs)
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self.coeffs.flat = self.weight_init(self.nW, ins, outs)
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self.biases.flat = 0
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self.std = np.std(self.W)
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def F(self, X):
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self.X = X
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Y = X.dot(self.coeffs) + self.biases
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return Y
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def dF(self, dY):
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dX = dY.dot(self.coeffs.T)
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self.dcoeffs[:] = self.X.T.dot(dY)
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self.dbiases[:] = dY.sum(0, keepdims=True)
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return dX
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class DenseOneLess(Dense):
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def init(self, W, dW):
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super().init(W, dW)
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ins, outs = self.input_shape[0], self.output_shape[0]
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assert ins == outs, (ins, outs)
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def F(self, X):
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np.fill_diagonal(self.coeffs, 0)
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self.X = X
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Y = X.dot(self.coeffs) + self.biases
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return Y
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def dF(self, dY):
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dX = dY.dot(self.coeffs.T)
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self.dcoeffs[:] = self.X.T.dot(dY)
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self.dbiases[:] = dY.sum(0, keepdims=True)
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np.fill_diagonal(self.dcoeffs, 0)
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return dX
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class LayerNorm(Layer): # TODO: inherit Affine instead?
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def __init__(self, eps=1e-3, axis=-1):
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super().__init__()
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self.eps = nf(eps)
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self.axis = int(axis)
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def F(self, X):
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self.center = X - np.mean(X, axis=self.axis, keepdims=True)
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#self.var = np.var(X, axis=self.axis, keepdims=True) + self.eps
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self.var = np.mean(np.square(self.center), axis=self.axis, keepdims=True) + self.eps
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self.std = np.sqrt(self.var) + self.eps
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Y = self.center / self.std
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return Y
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def dF(self, dY):
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length = self.input_shape[self.axis]
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dstd = dY * (-self.center / self.var)
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dvar = dstd * (0.5 / self.std)
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dcenter2 = dvar * (1 / length)
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dcenter = dY * (1 / self.std)
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dcenter += dcenter2 * (2 * self.center)
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dX = dcenter - dcenter / length
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return dX
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# Model
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class Model:
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def __init__(self, x, y, unsafe=False):
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assert isinstance(x, Layer), x
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assert isinstance(y, Layer), y
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self.x = x
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self.y = y
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self.ordered_nodes = self.traverse([], self.y)
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self.make_weights()
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for node in self.ordered_nodes:
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node.unsafe = unsafe
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def make_weights(self):
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self.param_count = 0
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for node in self.ordered_nodes:
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if node.size is not None:
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self.param_count += node.size
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self.W = np.zeros(self.param_count, dtype=nf)
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self.dW = np.zeros(self.param_count, dtype=nf)
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offset = 0
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for node in self.ordered_nodes:
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if node.size is not None:
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end = offset + node.size
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node.init(self.W[offset:end], self.dW[offset:end])
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offset += node.size
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def traverse(self, nodes, node):
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if node == self.x:
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return [node]
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for parent in node.parents:
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if parent not in nodes:
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new_nodes = self.traverse(nodes, parent)
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for new_node in new_nodes:
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if new_node not in nodes:
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nodes.append(new_node)
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if nodes:
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nodes.append(node)
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return nodes
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def forward(self, X):
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lut = dict()
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input_node = self.ordered_nodes[0]
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output_node = self.ordered_nodes[-1]
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lut[input_node] = input_node.multi(np.expand_dims(X, 0))
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for node in self.ordered_nodes[1:]:
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lut[node] = node.forward(lut)
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return lut[output_node]
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def backward(self, error):
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lut = dict()
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input_node = self.ordered_nodes[0]
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output_node = self.ordered_nodes[-1]
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lut[output_node] = output_node.dmulti(np.expand_dims(error, 0))
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for node in reversed(self.ordered_nodes[:-1]):
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lut[node] = node.backward(lut)
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#return lut[input_node] # meaningless value
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return self.dW
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def load_weights(self, fn):
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# seemingly compatible with keras' Dense layers.
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# ignores any non-Dense layer types.
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# TODO: assert file actually exists
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import h5py
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f = h5py.File(fn)
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weights = {}
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def visitor(name, obj):
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if isinstance(obj, h5py.Dataset):
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weights[name.split('/')[-1]] = nfa(obj[:])
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f.visititems(visitor)
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f.close()
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denses = [node for node in self.ordered_nodes if isinstance(node, Dense)]
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for i in range(len(denses)):
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a, b = i, i + 1
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b_name = "dense_{}".format(b)
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# TODO: write a Dense method instead of assigning directly
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denses[a].coeffs[:] = weights[b_name+'_W']
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denses[a].biases[:] = np.expand_dims(weights[b_name+'_b'], 0)
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def save_weights(self, fn, overwrite=False):
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import h5py
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f = h5py.File(fn, 'w')
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|
|
|
denses = [node for node in self.ordered_nodes if isinstance(node, Dense)]
|
|
for i in range(len(denses)):
|
|
a, b = i, i + 1
|
|
b_name = "dense_{}".format(b)
|
|
# TODO: write a Dense method instead of assigning directly
|
|
grp = f.create_group(b_name)
|
|
data = grp.create_dataset(b_name+'_W', denses[a].coeffs.shape, dtype=nf)
|
|
data[:] = denses[a].coeffs
|
|
data = grp.create_dataset(b_name+'_b', denses[a].biases.shape, dtype=nf)
|
|
data[:] = denses[a].biases
|
|
|
|
f.close()
|
|
|
|
class Ritual: # i'm just making up names at this point
|
|
def __init__(self, learner=None, loss=None, mloss=None):
|
|
self.learner = learner if learner is not None else Learner(Optimizer())
|
|
self.loss = loss if loss is not None else Squared()
|
|
self.mloss = mloss if mloss is not None else loss
|
|
|
|
def reset(self):
|
|
self.learner.reset(optim=True)
|
|
|
|
def measure(self, residual):
|
|
return self.mloss.mean(residual)
|
|
|
|
def derive(self, residual):
|
|
return self.loss.dmean(residual)
|
|
|
|
def learn(self, inputs, outputs):
|
|
predicted = self.model.forward(inputs)
|
|
residual = predicted - outputs
|
|
self.model.backward(self.derive(residual))
|
|
return residual
|
|
|
|
def update(self):
|
|
self.learner.optim.update(self.model.dW, self.model.W)
|
|
|
|
def prepare(self, model):
|
|
self.model = model
|
|
|
|
def train_batched(self, inputs, outputs, batch_size, return_losses=False):
|
|
cumsum_loss = 0
|
|
batch_count = inputs.shape[0] // batch_size
|
|
losses = []
|
|
for b in range(batch_count):
|
|
bi = b * batch_size
|
|
batch_inputs = inputs[ bi:bi+batch_size]
|
|
batch_outputs = outputs[bi:bi+batch_size]
|
|
|
|
if self.learner.per_batch:
|
|
self.learner.batch(b / batch_count)
|
|
|
|
residual = self.learn(batch_inputs, batch_outputs)
|
|
self.update()
|
|
|
|
batch_loss = self.measure(residual)
|
|
if np.isnan(batch_loss):
|
|
raise Exception("nan")
|
|
cumsum_loss += batch_loss
|
|
if return_losses:
|
|
losses.append(batch_loss)
|
|
avg_loss = cumsum_loss / batch_count
|
|
if return_losses:
|
|
return avg_loss, losses
|
|
else:
|
|
return avg_loss
|
|
|
|
def stochastic_multiply(W, gamma=0.5, allow_negation=True):
|
|
# paper: https://arxiv.org/abs/1606.01981
|
|
assert W.ndim == 1, W.ndim
|
|
assert 0 < gamma < 1, gamma
|
|
size = len(W)
|
|
alpha = np.max(np.abs(W))
|
|
# NOTE: numpy gives [low, high) but the paper advocates [low, high]
|
|
mult = np.random.uniform(gamma, 1/gamma, size=size)
|
|
if allow_negation: # TODO: verify this is correct. seems to wreak havok.
|
|
prob = (W / alpha + 1) / 2
|
|
samples = np.random.random_sample(size=size)
|
|
mult *= np.where(samples < prob, 1, -1)
|
|
np.multiply(W, mult, out=W)
|
|
|
|
class StochMRitual(Ritual):
|
|
# paper: https://arxiv.org/abs/1606.01981
|
|
# this probably doesn't make sense for regression problems,
|
|
# let alone small models, but here it is anyway!
|
|
|
|
def __init__(self, learner=None, loss=None, mloss=None, gamma=0.5):
|
|
super().__init__(learner, loss, mloss)
|
|
self.gamma = nf(gamma)
|
|
|
|
def prepare(self, model):
|
|
self.W = np.copy(model.W)
|
|
super().prepare(model)
|
|
|
|
def learn(self, inputs, outputs):
|
|
# an experiment:
|
|
#assert self.learner.rate < 10, self.learner.rate
|
|
#self.gamma = 1 - 1/2**(1 - np.log10(self.learner.rate))
|
|
|
|
self.W[:] = self.model.W
|
|
for layer in self.model.ordered_nodes:
|
|
if isinstance(layer, Dense):
|
|
stochastic_multiply(layer.coeffs.ravel(), gamma=self.gamma,
|
|
allow_negation=True)
|
|
residual = super().learn(inputs, outputs)
|
|
self.model.W[:] = self.W
|
|
return residual
|
|
|
|
def update(self):
|
|
super().update()
|
|
f = 0.5
|
|
for layer in self.model.ordered_nodes:
|
|
if isinstance(layer, Dense):
|
|
np.clip(layer.W, -layer.std * f, layer.std * f, out=layer.W)
|
|
# np.clip(layer.W, -1, 1, out=layer.W)
|
|
|
|
class Learner:
|
|
per_batch = False
|
|
|
|
def __init__(self, optim, epochs=100, rate=None):
|
|
assert isinstance(optim, Optimizer)
|
|
self.optim = optim
|
|
self.start_rate = optim.alpha if rate is None else float(rate)
|
|
self.epochs = int(epochs)
|
|
self.reset()
|
|
|
|
def reset(self, optim=False):
|
|
self.started = False
|
|
self.epoch = 0
|
|
if optim:
|
|
self.optim.reset()
|
|
|
|
@property
|
|
def epoch(self):
|
|
return self._epoch
|
|
|
|
@epoch.setter
|
|
def epoch(self, new_epoch):
|
|
self._epoch = int(new_epoch)
|
|
self.rate = self.rate_at(self._epoch)
|
|
|
|
@property
|
|
def rate(self):
|
|
return self.optim.alpha
|
|
|
|
@rate.setter
|
|
def rate(self, new_rate):
|
|
self.optim.alpha = new_rate
|
|
|
|
def rate_at(self, epoch):
|
|
return self.start_rate
|
|
|
|
def next(self):
|
|
# prepares the next epoch. returns whether or not to continue training.
|
|
if self.epoch + 1 >= self.epochs:
|
|
return False
|
|
if self.started:
|
|
self.epoch += 1
|
|
else:
|
|
self.started = True
|
|
self.epoch = self.epoch # poke property setter just in case
|
|
return True
|
|
|
|
def batch(self, progress): # TODO: rename
|
|
# interpolates rates between epochs.
|
|
# unlike epochs, we do not store batch number as a state.
|
|
# i.e. calling next() will not respect progress.
|
|
assert 0 <= progress <= 1
|
|
self.rate = self.rate_at(self._epoch + progress)
|
|
|
|
@property
|
|
def final_rate(self):
|
|
return self.rate_at(self.epochs - 1)
|
|
|
|
class AnnealingLearner(Learner):
|
|
def __init__(self, optim, epochs=100, rate=None, halve_every=10):
|
|
self.halve_every = float(halve_every)
|
|
self.anneal = 0.5**(1/self.halve_every)
|
|
super().__init__(optim, epochs, rate)
|
|
|
|
def rate_at(self, epoch):
|
|
return self.start_rate * self.anneal**epoch
|
|
|
|
class DumbLearner(AnnealingLearner):
|
|
# this is my own awful contraption. it's not really "SGD with restarts".
|
|
def __init__(self, optim, epochs=100, rate=None, halve_every=10, restarts=0, restart_advance=20, callback=None):
|
|
self.restart_epochs = int(epochs)
|
|
self.restarts = int(restarts)
|
|
self.restart_advance = float(restart_advance)
|
|
self.restart_callback = callback
|
|
epochs = self.restart_epochs * (self.restarts + 1)
|
|
super().__init__(optim, epochs, rate, halve_every)
|
|
|
|
def rate_at(self, epoch):
|
|
sub_epoch = epoch % self.restart_epochs
|
|
restart = epoch // self.restart_epochs
|
|
return super().rate_at(sub_epoch) * (self.anneal**self.restart_advance)**restart
|
|
|
|
def next(self):
|
|
if not super().next():
|
|
return False
|
|
sub_epoch = self.epoch % self.restart_epochs
|
|
restart = self.epoch // self.restart_epochs
|
|
if restart > 0 and sub_epoch == 0:
|
|
if self.restart_callback is not None:
|
|
self.restart_callback(restart)
|
|
return True
|
|
|
|
def cosmod(x):
|
|
# plot: https://www.desmos.com/calculator/hlgqmyswy2
|
|
return (1 + np.cos((x % 1) * np.pi)) / 2
|
|
|
|
class SGDR(Learner):
|
|
# Stochastic Gradient Descent with Restarts
|
|
# paper: https://arxiv.org/abs/1608.03983
|
|
# NOTE: this is not a complete implementation.
|
|
per_batch = True
|
|
|
|
def __init__(self, optim, epochs=100, rate=None, restarts=0, restart_decay=0.5, callback=None):
|
|
self.restart_epochs = int(epochs)
|
|
self.decay = float(restart_decay)
|
|
self.restarts = int(restarts)
|
|
self.restart_callback = callback
|
|
epochs = self.restart_epochs * (self.restarts + 1)
|
|
super().__init__(optim, epochs, rate)
|
|
|
|
def rate_at(self, epoch):
|
|
sub_epoch = epoch % self.restart_epochs
|
|
x = sub_epoch / self.restart_epochs
|
|
restart = epoch // self.restart_epochs
|
|
return self.start_rate * self.decay**restart * cosmod(x)
|
|
|
|
def next(self):
|
|
if not super().next():
|
|
return False
|
|
sub_epoch = self.epoch % self.restart_epochs
|
|
restart = self.epoch // self.restart_epochs
|
|
if restart > 0 and sub_epoch == 0:
|
|
if self.restart_callback is not None:
|
|
self.restart_callback(restart)
|
|
return True
|
|
|
|
def multiresnet(x, width, depth, block=2, multi=1,
|
|
activation=Relu, style='batchless',
|
|
init=init_he_normal):
|
|
y = x
|
|
last_size = x.output_shape[0]
|
|
|
|
FC = lambda size: Dense(size, init)
|
|
#FC = lambda size: DenseOneLess(size, init)
|
|
|
|
for d in range(depth):
|
|
size = width
|
|
|
|
if last_size != size:
|
|
y = y.feed(Dense(size, init))
|
|
|
|
if style == 'batchless':
|
|
skip = y
|
|
merger = Sum()
|
|
skip.feed(merger)
|
|
z_start = skip.feed(activation())
|
|
for i in range(multi):
|
|
z = z_start
|
|
for i in range(block):
|
|
if i > 0:
|
|
z = z.feed(activation())
|
|
z = z.feed(FC(size))
|
|
z.feed(merger)
|
|
y = merger
|
|
elif style == 'onelesssum':
|
|
is_last = d + 1 == depth
|
|
needs_sum = not is_last or multi > 1
|
|
skip = y
|
|
if needs_sum:
|
|
merger = Sum()
|
|
if not is_last:
|
|
skip.feed(merger)
|
|
z_start = skip.feed(activation())
|
|
for i in range(multi):
|
|
z = z_start
|
|
for i in range(block):
|
|
if i > 0:
|
|
z = z.feed(activation())
|
|
z = z.feed(FC(size))
|
|
if needs_sum:
|
|
z.feed(merger)
|
|
if needs_sum:
|
|
y = merger
|
|
else:
|
|
y = z
|
|
else:
|
|
raise Exception('unknown resnet style', style)
|
|
|
|
last_size = size
|
|
|
|
return y
|
|
|
|
inits = dict(he_normal=init_he_normal, he_uniform=init_he_uniform)
|
|
activations = dict(sigmoid=Sigmoid, tanh=Tanh, relu=Relu, elu=Elu, gelu=GeluApprox)
|
|
|
|
def run(program, args=[]):
|
|
import sys
|
|
lament = lambda *args, **kwargs: print(*args, file=sys.stderr, **kwargs)
|
|
def log(left, right):
|
|
lament("{:>20}: {}".format(left, right))
|
|
|
|
# Config
|
|
|
|
from dotmap import DotMap
|
|
config = DotMap(
|
|
fn_load = None,
|
|
fn_save = 'optim_nn.h5',
|
|
log_fn = 'losses.npz',
|
|
|
|
# multi-residual network parameters
|
|
res_width = 28,
|
|
res_depth = 2,
|
|
res_block = 3, # normally 2 for plain resnet
|
|
res_multi = 2, # normally 1 for plain resnet
|
|
|
|
# style of resnet (order of layers, which layers, etc.)
|
|
parallel_style = 'onelesssum',
|
|
activation = 'gelu',
|
|
|
|
optim = 'adam',
|
|
nesterov = False, # only used with SGD or Adam
|
|
momentum = 0.33, # only used with SGD
|
|
|
|
# learning parameters
|
|
learner = 'SGDR',
|
|
learn = 1e-2,
|
|
epochs = 24,
|
|
restarts = 2,
|
|
learn_decay = 0.25, # only used with SGDR
|
|
learn_halve_every = 16, # unused with SGDR
|
|
learn_restart_advance = 16, # unused with SGDR
|
|
|
|
# misc
|
|
batch_size = 64,
|
|
init = 'he_normal',
|
|
loss = 'msee',
|
|
mloss = 'mse',
|
|
restart_optim = False, # restarts also reset internal state of optimizer
|
|
unsafe = True, # aka gotta go fast mode
|
|
train_compare = 0.0000508,
|
|
valid_compare = 0.0000678,
|
|
|
|
ritual = None,
|
|
)
|
|
|
|
config.pprint()
|
|
|
|
# toy CIE-2000 data
|
|
from ml.cie_mlp_data import rgbcompare, input_samples, output_samples, \
|
|
inputs, outputs, valid_inputs, valid_outputs, \
|
|
x_scale, y_scale
|
|
|
|
# Our Test Model
|
|
|
|
init = inits[config.init]
|
|
activation = activations[config.activation]
|
|
|
|
x = Input(shape=(input_samples,))
|
|
y = x
|
|
y = multiresnet(y,
|
|
config.res_width, config.res_depth,
|
|
config.res_block, config.res_multi,
|
|
activation=activation, init=init,
|
|
style=config.parallel_style)
|
|
if y.output_shape[0] != output_samples:
|
|
y = y.feed(Dense(output_samples, init))
|
|
|
|
model = Model(x, y, unsafe=config.unsafe)
|
|
|
|
if 0:
|
|
node_names = ' '.join([str(node) for node in model.ordered_nodes])
|
|
log('{} nodes'.format(len(model.ordered_nodes)), node_names)
|
|
else:
|
|
for node in model.ordered_nodes:
|
|
children = [str(n) for n in node.children]
|
|
if len(children) > 0:
|
|
sep = '->'
|
|
print(str(node)+sep+('\n'+str(node)+sep).join(children))
|
|
log('parameters', model.param_count)
|
|
|
|
#
|
|
|
|
training = config.epochs > 0 and config.restarts >= 0
|
|
|
|
if config.fn_load is not None:
|
|
log('loading weights', config.fn_load)
|
|
model.load_weights(config.fn_load)
|
|
|
|
#
|
|
|
|
if config.optim == 'adam':
|
|
assert not config.nesterov, "unimplemented"
|
|
optim = Adam()
|
|
elif config.optim == 'sgd':
|
|
if config.momentum != 0:
|
|
optim = Momentum(mu=config.momentum, nesterov=config.nesterov)
|
|
else:
|
|
optim = Optimizer()
|
|
else:
|
|
raise Exception('unknown optimizer', config.optim)
|
|
|
|
def rscb(restart):
|
|
measure_error() # declared later...
|
|
log("restarting", restart)
|
|
if config.restart_optim:
|
|
optim.reset()
|
|
|
|
#
|
|
|
|
if config.learner == 'SGDR':
|
|
learner = SGDR(optim, epochs=config.epochs, rate=config.learn,
|
|
restart_decay=config.learn_decay, restarts=config.restarts,
|
|
callback=rscb)
|
|
# final learning rate isn't of interest here; it's gonna be close to 0.
|
|
else:
|
|
learner = DumbLearner(optim, epochs=config.epochs, rate=config.learn,
|
|
halve_every=config.learn_halve_every,
|
|
restarts=config.restarts, restart_advance=config.learn_restart_advance,
|
|
callback=rscb)
|
|
log("final learning rate", "{:10.8f}".format(learner.final_rate))
|
|
|
|
#
|
|
|
|
def lookup_loss(maybe_name):
|
|
if isinstance(maybe_name, Loss):
|
|
return maybe_name
|
|
elif maybe_name == 'mse':
|
|
return Squared()
|
|
elif maybe_name == 'mshe': # mushy
|
|
return SquaredHalved()
|
|
elif maybe_name == 'msee':
|
|
return SomethingElse()
|
|
raise Exception('unknown objective', maybe_name)
|
|
|
|
loss = lookup_loss(config.loss)
|
|
mloss = lookup_loss(config.mloss) if config.mloss else loss
|
|
|
|
if config.ritual == None:
|
|
ritual = Ritual(learner=learner, loss=loss, mloss=mloss)
|
|
elif config.ritual == 'stochm':
|
|
ritual = StochMRitual(learner=learner, loss=loss, mloss=mloss)
|
|
else:
|
|
raise Exception('unknown ritual', config.ritual)
|
|
|
|
# Training
|
|
|
|
batch_losses = []
|
|
train_losses = []
|
|
valid_losses = []
|
|
|
|
def measure_error():
|
|
def print_error(name, inputs, outputs, comparison=None):
|
|
predicted = model.forward(inputs)
|
|
residual = predicted - outputs
|
|
err = ritual.measure(residual)
|
|
log(name + " loss", "{:11.7f}".format(err))
|
|
if comparison:
|
|
log("improvement", "{:+7.2f}%".format((comparison / err - 1) * 100))
|
|
return err
|
|
|
|
train_err = print_error("train",
|
|
inputs / x_scale, outputs / y_scale,
|
|
config.train_compare)
|
|
valid_err = print_error("valid",
|
|
valid_inputs / x_scale, valid_outputs / y_scale,
|
|
config.valid_compare)
|
|
train_losses.append(train_err)
|
|
valid_losses.append(valid_err)
|
|
|
|
measure_error()
|
|
|
|
assert inputs.shape[0] % config.batch_size == 0, \
|
|
"inputs is not evenly divisible by batch_size" # TODO: lift this restriction
|
|
ritual.prepare(model)
|
|
while learner.next():
|
|
indices = np.arange(inputs.shape[0])
|
|
np.random.shuffle(indices)
|
|
shuffled_inputs = inputs[indices] / x_scale
|
|
shuffled_outputs = outputs[indices] / y_scale
|
|
|
|
avg_loss, losses = ritual.train_batched(
|
|
shuffled_inputs, shuffled_outputs,
|
|
config.batch_size,
|
|
return_losses=True)
|
|
batch_losses += losses
|
|
|
|
#log("learning rate", "{:10.8f}".format(learner.rate))
|
|
#log("average loss", "{:11.7f}".format(avg_loss))
|
|
fmt = "epoch {:4.0f}, rate {:10.8f}, loss {:11.7f}"
|
|
log("info", fmt.format(learner.epoch + 1, learner.rate, avg_loss))
|
|
|
|
measure_error()
|
|
|
|
if config.fn_save is not None:
|
|
log('saving weights', config.fn_save)
|
|
model.save_weights(config.fn_save, overwrite=True)
|
|
|
|
# Evaluation
|
|
|
|
# this is just an example/test of how to predict a single output;
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# it doesn't measure the quality of the network or anything.
|
|
a = (192, 128, 64)
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|
b = (64, 128, 192)
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|
X = np.expand_dims(np.hstack((a, b)), 0) / x_scale
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P = model.forward(X) * y_scale
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|
log("truth", rgbcompare(a, b))
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|
log("network", np.squeeze(P))
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|
|
|
if config.log_fn is not None:
|
|
np.savez_compressed(config.log_fn,
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|
batch_losses=nfa(batch_losses),
|
|
train_losses=nfa(train_losses),
|
|
valid_losses=nfa(valid_losses))
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|
|
|
return 0
|
|
|
|
if __name__ == '__main__':
|
|
import sys
|
|
sys.exit(run(sys.argv[0], sys.argv[1:]))
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