792 lines
23 KiB
Python
792 lines
23 KiB
Python
#!/usr/bin/env python3
|
|
|
|
import numpy as np
|
|
nf = np.float32
|
|
nfa = lambda x: np.array(x, dtype=nf)
|
|
ni = np.int
|
|
nia = lambda x: np.array(x, dtype=ni)
|
|
|
|
from scipy.special import expit as sigmoid
|
|
|
|
from collections import defaultdict
|
|
|
|
# Initializations
|
|
|
|
# note: these are currently only implemented for 2D shapes.
|
|
|
|
def init_he_normal(size, ins, outs):
|
|
s = np.sqrt(2 / ins)
|
|
return np.random.normal(0, s, size=size)
|
|
|
|
def init_he_uniform(size, ins, outs):
|
|
s = np.sqrt(6 / ins)
|
|
return np.random.uniform(-s, s, size=size)
|
|
|
|
# Loss functions
|
|
|
|
class Loss:
|
|
def mean(self, r):
|
|
return np.average(self.f(r))
|
|
|
|
def dmean(self, r):
|
|
d = self.df(r)
|
|
return d / len(d)
|
|
|
|
class Squared(Loss):
|
|
def f(self, r):
|
|
return np.square(r)
|
|
|
|
def df(self, r):
|
|
return 2 * r
|
|
|
|
class SquaredHalved(Loss):
|
|
def f(self, r):
|
|
return np.square(r) / 2
|
|
|
|
def df(self, r):
|
|
return r
|
|
|
|
class SomethingElse(Loss):
|
|
# generalizes Absolute and SquaredHalved
|
|
# plot: https://www.desmos.com/calculator/fagjg9vuz7
|
|
def __init__(self, a=4/3):
|
|
assert 1 <= a <= 2, "parameter out of range"
|
|
self.a = nf(a / 2)
|
|
self.b = nf(2 / a)
|
|
self.c = nf(2 / a - 1)
|
|
|
|
def f(self, r):
|
|
return self.a * np.abs(r)**self.b
|
|
|
|
def df(self, r):
|
|
return np.sign(r) * np.abs(r)**self.c
|
|
|
|
# Optimizers
|
|
|
|
class Optimizer:
|
|
def __init__(self, alpha=0.1):
|
|
self.alpha = nf(alpha)
|
|
self.reset()
|
|
|
|
def reset(self):
|
|
pass
|
|
|
|
def compute(self, dW, W):
|
|
return -self.alpha * dW
|
|
|
|
def update(self, dW, W):
|
|
W += self.compute(dW, W)
|
|
|
|
# the following optimizers are blatantly lifted from tiny-dnn:
|
|
# https://github.com/tiny-dnn/tiny-dnn/blob/master/tiny_dnn/optimizers/optimizer.h
|
|
|
|
class Momentum(Optimizer):
|
|
def __init__(self, alpha=0.01, lamb=0, mu=0.9, nesterov=False):
|
|
self.alpha = np.asfarray(alpha) # learning rate
|
|
self.lamb = np.asfarray(lamb) # weight decay
|
|
self.mu = np.asfarray(mu) # momentum
|
|
self.nesterov = bool(nesterov)
|
|
|
|
self.reset()
|
|
|
|
def reset(self):
|
|
self.dWprev = None
|
|
|
|
def compute(self, dW, W):
|
|
if self.dWprev is None:
|
|
#self.dWprev = np.zeros_like(dW)
|
|
self.dWprev = np.copy(dW)
|
|
|
|
V = self.mu * self.dWprev - self.alpha * (dW + W * self.lamb)
|
|
self.dWprev[:] = V
|
|
if self.nesterov:
|
|
return self.mu * V - self.alpha * (dW + W * self.lamb)
|
|
else:
|
|
return V
|
|
|
|
class Adam(Optimizer):
|
|
def __init__(self, alpha=0.001, b1=0.9, b2=0.999, b1_t=0.9, b2_t=0.999, eps=1e-8):
|
|
self.alpha = nf(alpha) # learning rate
|
|
self.b1 = nf(b1) # decay term
|
|
self.b2 = nf(b2) # decay term
|
|
self.b1_t_default = nf(b1_t) # decay term power t
|
|
self.b2_t_default = nf(b2_t) # decay term power t
|
|
self.eps = nf(eps)
|
|
|
|
self.reset()
|
|
|
|
def reset(self):
|
|
self.mt = None
|
|
self.vt = None
|
|
self.b1_t = self.b1_t_default
|
|
self.b2_t = self.b2_t_default
|
|
|
|
def compute(self, dW, W):
|
|
if self.mt is None:
|
|
self.mt = np.zeros_like(W)
|
|
if self.vt is None:
|
|
self.vt = np.zeros_like(W)
|
|
|
|
# decay
|
|
self.b1_t *= self.b1
|
|
self.b2_t *= self.b2
|
|
|
|
self.mt[:] = self.b1 * self.mt + (1 - self.b1) * dW
|
|
self.vt[:] = self.b2 * self.vt + (1 - self.b2) * dW * dW
|
|
|
|
return -self.alpha * (self.mt / (1 - self.b1_t)) \
|
|
/ np.sqrt((self.vt / (1 - self.b2_t)) + self.eps)
|
|
|
|
# Abstract Layers
|
|
|
|
_layer_counters = defaultdict(lambda: 0)
|
|
|
|
class Layer:
|
|
def __init__(self):
|
|
self.parents = []
|
|
self.children = []
|
|
self.input_shape = None
|
|
self.output_shape = None
|
|
kind = self.__class__.__name__
|
|
global _layer_counters
|
|
_layer_counters[kind] += 1
|
|
self.name = "{}_{}".format(kind, _layer_counters[kind])
|
|
self.size = None # total weight count (if any)
|
|
self.unsafe = False # disables assertions for better performance
|
|
|
|
def __str__(self):
|
|
return self.name
|
|
|
|
# methods we might want to override:
|
|
|
|
def F(self, X):
|
|
raise NotImplementedError("unimplemented", self)
|
|
|
|
def dF(self, dY):
|
|
raise NotImplementedError("unimplemented", self)
|
|
|
|
def do_feed(self, child):
|
|
self.children.append(child)
|
|
|
|
def be_fed(self, parent):
|
|
self.parents.append(parent)
|
|
|
|
def make_shape(self, shape):
|
|
if not self.unsafe:
|
|
assert shape is not None
|
|
if self.output_shape is None:
|
|
self.output_shape = shape
|
|
return shape
|
|
|
|
# TODO: rename this multi and B crap to something actually relevant.
|
|
|
|
def multi(self, B):
|
|
if not self.unsafe:
|
|
assert len(B) == 1, self
|
|
return self.F(B[0])
|
|
|
|
def dmulti(self, dB):
|
|
if len(dB) == 1:
|
|
return self.dF(dB[0])
|
|
else:
|
|
dX = None
|
|
for dY in dB:
|
|
if dX is None:
|
|
dX = self.dF(dY)
|
|
else:
|
|
dX += self.dF(dY)
|
|
return dX
|
|
|
|
# general utility methods:
|
|
|
|
def compatible(self, parent):
|
|
if self.input_shape is None:
|
|
# inherit shape from output
|
|
shape = self.make_shape(parent.output_shape)
|
|
if shape is None:
|
|
return False
|
|
self.input_shape = shape
|
|
if np.all(self.input_shape == parent.output_shape):
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
def feed(self, child):
|
|
if not child.compatible(self):
|
|
fmt = "{} is incompatible with {}: shape mismatch: {} vs. {}"
|
|
raise Exception(fmt.format(self, child, self.output_shape, child.input_shape))
|
|
self.do_feed(child)
|
|
child.be_fed(self)
|
|
return child
|
|
|
|
def validate_input(self, X):
|
|
assert X.shape[1:] == self.input_shape, (str(self), X.shape[1:], self.input_shape)
|
|
|
|
def validate_output(self, Y):
|
|
assert Y.shape[1:] == self.output_shape, (str(self), Y.shape[1:], self.output_shape)
|
|
|
|
def forward(self, lut):
|
|
if not self.unsafe:
|
|
assert len(self.parents) > 0, self
|
|
B = []
|
|
for parent in self.parents:
|
|
# TODO: skip over irrelevant nodes (if any)
|
|
X = lut[parent]
|
|
if not self.unsafe:
|
|
self.validate_input(X)
|
|
B.append(X)
|
|
Y = self.multi(B)
|
|
if not self.unsafe:
|
|
self.validate_output(Y)
|
|
return Y
|
|
|
|
def backward(self, lut):
|
|
if not self.unsafe:
|
|
assert len(self.children) > 0, self
|
|
dB = []
|
|
for child in self.children:
|
|
# TODO: skip over irrelevant nodes (if any)
|
|
dY = lut[child]
|
|
if not self.unsafe:
|
|
self.validate_output(dY)
|
|
dB.append(dY)
|
|
dX = self.dmulti(dB)
|
|
if not self.unsafe:
|
|
self.validate_input(dX)
|
|
return dX
|
|
|
|
# Final Layers
|
|
|
|
class Sum(Layer):
|
|
def multi(self, B):
|
|
return np.sum(B, axis=0)
|
|
|
|
def dmulti(self, dB):
|
|
#assert len(dB) == 1, "unimplemented"
|
|
return dB[0] # TODO: does this always work?
|
|
|
|
class Input(Layer):
|
|
def __init__(self, shape):
|
|
assert shape is not None
|
|
super().__init__()
|
|
self.shape = tuple(shape)
|
|
self.input_shape = self.shape
|
|
self.output_shape = self.shape
|
|
|
|
def F(self, X):
|
|
return X
|
|
|
|
def dF(self, dY):
|
|
#self.dY = dY
|
|
return np.zeros_like(dY)
|
|
|
|
class Affine(Layer):
|
|
def __init__(self, a=1, b=0):
|
|
super().__init__()
|
|
self.a = nf(a)
|
|
self.b = nf(b)
|
|
|
|
def F(self, X):
|
|
return self.a * X + self.b
|
|
|
|
def dF(self, dY):
|
|
return dY * self.a
|
|
|
|
class Sigmoid(Layer): # aka Logistic
|
|
def F(self, X):
|
|
from scipy.special import expit as sigmoid
|
|
self.sig = sigmoid(X)
|
|
return X * self.sig
|
|
|
|
def dF(self, dY):
|
|
return dY * self.sig * (1 - self.sig)
|
|
|
|
class Tanh(Layer):
|
|
def F(self, X):
|
|
self.sig = np.tanh(X)
|
|
return X * self.sig
|
|
|
|
def dF(self, dY):
|
|
return dY * (1 - self.sig * self.sig)
|
|
|
|
class Relu(Layer):
|
|
def F(self, X):
|
|
self.cond = X >= 0
|
|
return np.where(self.cond, X, 0)
|
|
|
|
def dF(self, dY):
|
|
return np.where(self.cond, dY, 0)
|
|
|
|
class Elu(Layer):
|
|
# paper: https://arxiv.org/abs/1511.07289
|
|
def __init__(self, alpha=1):
|
|
super().__init__()
|
|
self.alpha = nf(alpha)
|
|
|
|
def F(self, X):
|
|
self.cond = X >= 0
|
|
self.neg = np.exp(X) - 1
|
|
return np.where(self.cond, X, self.neg)
|
|
|
|
def dF(self, dY):
|
|
return dY * np.where(self.cond, 1, self.neg + 1)
|
|
|
|
class GeluApprox(Layer):
|
|
# paper: https://arxiv.org/abs/1606.08415
|
|
# plot: https://www.desmos.com/calculator/ydzgtccsld
|
|
def F(self, X):
|
|
self.a = 1.704 * X
|
|
self.sig = sigmoid(self.a)
|
|
return X * self.sig
|
|
|
|
def dF(self, dY):
|
|
return dY * self.sig * (1 + self.a * (1 - self.sig))
|
|
|
|
class Dense(Layer):
|
|
def __init__(self, dim, init=init_he_uniform):
|
|
super().__init__()
|
|
self.dim = ni(dim)
|
|
self.output_shape = (dim,)
|
|
self.size = None
|
|
self.weight_init = init
|
|
|
|
def init(self, W, dW):
|
|
ins, outs = self.input_shape[0], self.output_shape[0]
|
|
|
|
self.W = W
|
|
self.dW = dW
|
|
self.coeffs = self.W[:self.nW].reshape(ins, outs)
|
|
self.biases = self.W[self.nW:].reshape(1, outs)
|
|
self.dcoeffs = self.dW[:self.nW].reshape(ins, outs)
|
|
self.dbiases = self.dW[self.nW:].reshape(1, outs)
|
|
|
|
self.coeffs.flat = self.weight_init(self.nW, ins, outs)
|
|
self.biases.flat = 0
|
|
|
|
def make_shape(self, shape):
|
|
super().make_shape(shape)
|
|
if len(shape) != 1:
|
|
return False
|
|
self.nW = self.dim * shape[0]
|
|
self.nb = self.dim
|
|
self.size = self.nW + self.nb
|
|
return shape
|
|
|
|
def F(self, X):
|
|
self.X = X
|
|
Y = X.dot(self.coeffs) \
|
|
+ self.biases
|
|
return Y
|
|
|
|
def dF(self, dY):
|
|
dX = dY.dot(self.coeffs.T)
|
|
self.dcoeffs[:] = self.X.T.dot(dY)
|
|
self.dbiases[:] = np.sum(dY, axis=0, keepdims=True)
|
|
return dX
|
|
|
|
# Model
|
|
|
|
class Model:
|
|
def __init__(self, x, y, unsafe=False):
|
|
assert isinstance(x, Layer), x
|
|
assert isinstance(y, Layer), y
|
|
self.x = x
|
|
self.y = y
|
|
self.ordered_nodes = self.traverse([], self.y)
|
|
self.make_weights()
|
|
for node in self.ordered_nodes:
|
|
node.unsafe = unsafe
|
|
|
|
def make_weights(self):
|
|
self.param_count = 0
|
|
for node in self.ordered_nodes:
|
|
if node.size is not None:
|
|
self.param_count += node.size
|
|
self.W = np.zeros(self.param_count, dtype=nf)
|
|
self.dW = np.zeros(self.param_count, dtype=nf)
|
|
|
|
offset = 0
|
|
for node in self.ordered_nodes:
|
|
if node.size is not None:
|
|
end = offset + node.size
|
|
node.init(self.W[offset:end], self.dW[offset:end])
|
|
offset += node.size
|
|
|
|
def traverse(self, nodes, node):
|
|
if node == self.x:
|
|
return [node]
|
|
for parent in node.parents:
|
|
if parent not in nodes:
|
|
new_nodes = self.traverse(nodes, parent)
|
|
for new_node in new_nodes:
|
|
if new_node not in nodes:
|
|
nodes.append(new_node)
|
|
if nodes:
|
|
nodes.append(node)
|
|
return nodes
|
|
|
|
def forward(self, X):
|
|
lut = dict()
|
|
input_node = self.ordered_nodes[0]
|
|
output_node = self.ordered_nodes[-1]
|
|
lut[input_node] = input_node.multi(np.expand_dims(X, 0))
|
|
for node in self.ordered_nodes[1:]:
|
|
lut[node] = node.forward(lut)
|
|
return lut[output_node]
|
|
|
|
def backward(self, error):
|
|
lut = dict()
|
|
input_node = self.ordered_nodes[0]
|
|
output_node = self.ordered_nodes[-1]
|
|
lut[output_node] = output_node.dmulti(np.expand_dims(error, 0))
|
|
for node in reversed(self.ordered_nodes[:-1]):
|
|
lut[node] = node.backward(lut)
|
|
#return lut[input_node] # meaningless value
|
|
return self.dW
|
|
|
|
def load_weights(self, fn):
|
|
# seemingly compatible with keras' Dense layers.
|
|
# ignores any non-Dense layer types.
|
|
# TODO: assert file actually exists
|
|
import h5py
|
|
f = h5py.File(fn)
|
|
weights = {}
|
|
def visitor(name, obj):
|
|
if isinstance(obj, h5py.Dataset):
|
|
weights[name.split('/')[-1]] = nfa(obj[:])
|
|
f.visititems(visitor)
|
|
f.close()
|
|
|
|
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
|
|
denses[a].coeffs[:] = weights[b_name+'_W']
|
|
denses[a].biases[:] = np.expand_dims(weights[b_name+'_b'], 0)
|
|
|
|
def save_weights(self, fn, overwrite=False):
|
|
import h5py
|
|
f = h5py.File(fn, 'w')
|
|
|
|
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:
|
|
def __init__(self,
|
|
optim=None,
|
|
learn_rate=1e-3, learn_anneal=1, learn_advance=0,
|
|
loss=None, mloss=None):
|
|
self.optim = optim if optim is not None else SGD()
|
|
self.loss = loss if loss is not None else Squared()
|
|
self.mloss = mloss if mloss is not None else loss
|
|
self.learn_rate = nf(learn_rate)
|
|
self.learn_anneal = nf(learn_anneal)
|
|
self.learn_advance = nf(learn_advance)
|
|
|
|
def measure(self, residual):
|
|
return self.mloss.mean(residual)
|
|
|
|
def derive(self, residual):
|
|
return self.loss.dmean(residual)
|
|
|
|
def update(self, dW, W):
|
|
self.optim.update(dW, W)
|
|
|
|
def prepare(self, epoch):
|
|
self.optim.alpha = self.learn_rate * self.learn_anneal**epoch
|
|
|
|
def restart(self, optim=False):
|
|
self.learn_rate *= self.learn_anneal**self.learn_advance
|
|
if optim:
|
|
self.optim.reset()
|
|
|
|
def train_batched(self, model, 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]
|
|
|
|
predicted = model.forward(batch_inputs)
|
|
residual = predicted - batch_outputs
|
|
|
|
model.backward(self.derive(residual))
|
|
self.update(model.dW, model.W)
|
|
|
|
batch_loss = self.measure(residual)
|
|
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 multiresnet(x, width, depth, block=2, multi=1,
|
|
activation=Relu, style='batchless',
|
|
init=init_he_normal):
|
|
y = x
|
|
last_size = x.output_shape[0]
|
|
|
|
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(Dense(size, init))
|
|
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(Dense(size, init))
|
|
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 = 49,
|
|
res_depth = 1,
|
|
res_block = 4, # normally 2 for plain resnet
|
|
res_multi = 1, # 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: SGD with restarts (kinda)
|
|
learn = 1e-2,
|
|
epochs = 24,
|
|
learn_halve_every = 16,
|
|
restarts = 2,
|
|
learn_restart_advance = 16,
|
|
|
|
# misc
|
|
batch_size = 64,
|
|
init = 'he_normal',
|
|
loss = SomethingElse(4/3),
|
|
mloss = 'mse',
|
|
restart_optim = True, # restarts also reset internal state of optimizer
|
|
unsafe = True, # aka gotta go fast mode
|
|
train_compare = None,
|
|
#valid_compare = 0.0007159,
|
|
valid_compare = 0.0000946,
|
|
)
|
|
|
|
config.pprint()
|
|
|
|
# toy CIE-2000 data
|
|
from ml.cie_mlp_data import rgbcompare, input_samples, output_samples, x_scale, y_scale
|
|
|
|
def read_data(fn):
|
|
data = np.load(fn)
|
|
try:
|
|
inputs, outputs = data['inputs'], data['outputs']
|
|
except KeyError:
|
|
# because i'm bad at video games.
|
|
inputs, outputs = data['arr_0'], data['arr_1']
|
|
return inputs, outputs
|
|
|
|
inputs, outputs = read_data("ml/cie_mlp_data.npz")
|
|
valid_inputs, valid_outputs = read_data("ml/cie_mlp_vdata.npz")
|
|
|
|
# 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)
|
|
if y.output_shape[0] != output_samples:
|
|
y = y.feed(Dense(output_samples, init))
|
|
|
|
model = Model(x, y, unsafe=config.unsafe)
|
|
|
|
node_names = ' '.join([str(node) for node in model.ordered_nodes])
|
|
log('{} nodes'.format(len(model.ordered_nodes)), node_names)
|
|
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 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()
|
|
raise Exception('unknown objective', maybe_name)
|
|
|
|
loss = lookup_loss(config.loss)
|
|
mloss = lookup_loss(config.mloss) if config.mloss else loss
|
|
|
|
anneal = 0.5**(1/config.learn_halve_every)
|
|
ritual = Ritual(optim=optim,
|
|
learn_rate=config.learn, learn_anneal=anneal,
|
|
learn_advance=config.learn_restart_advance,
|
|
loss=loss, mloss=mloss)
|
|
|
|
learn_end = config.learn * (anneal**config.learn_restart_advance)**config.restarts * anneal**(config.epochs - 1)
|
|
log("final learning rate", "{:10.8f}".format(learn_end))
|
|
|
|
# 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)
|
|
|
|
for i in range(config.restarts + 1):
|
|
measure_error()
|
|
|
|
if i > 0:
|
|
log("restarting", i)
|
|
ritual.restart(optim=config.restart_optim)
|
|
|
|
assert inputs.shape[0] % config.batch_size == 0, \
|
|
"inputs is not evenly divisible by batch_size" # TODO: lift this restriction
|
|
for e in range(config.epochs):
|
|
indices = np.arange(inputs.shape[0])
|
|
np.random.shuffle(indices)
|
|
shuffled_inputs = inputs[indices] / x_scale
|
|
shuffled_outputs = outputs[indices] / y_scale
|
|
|
|
ritual.prepare(e)
|
|
#log("learning rate", "{:10.8f}".format(ritual.optim.alpha))
|
|
|
|
avg_loss, losses = ritual.train_batched(model,
|
|
shuffled_inputs, shuffled_outputs,
|
|
config.batch_size,
|
|
return_losses=True)
|
|
log("average loss", "{:11.7f}".format(avg_loss))
|
|
batch_losses += losses
|
|
|
|
measure_error()
|
|
|
|
if config.fn_save is not None:
|
|
log('saving weights', config.fn_save)
|
|
model.save_weights(config.fn_save, overwrite=True)
|
|
|
|
# Evaluation
|
|
|
|
a = (192, 128, 64)
|
|
b = (64, 128, 192)
|
|
X = np.expand_dims(np.hstack((a, b)), 0) / x_scale
|
|
P = model.forward(X) * y_scale
|
|
log("truth", rgbcompare(a, b))
|
|
log("network", np.squeeze(P))
|
|
|
|
if config.log_fn is not None:
|
|
np.savez_compressed(config.log_fn,
|
|
batch_losses=nfa(batch_losses),
|
|
train_losses=nfa(train_losses),
|
|
valid_losses=nfa(valid_losses))
|
|
|
|
return 0
|
|
|
|
if __name__ == '__main__':
|
|
import sys
|
|
sys.exit(run(sys.argv[0], sys.argv[1:]))
|