_weights = [[glumpy.Image(w, vmin=-m, vmax=m) for w in ws]
for ws in weights]
W = 100 * (opts.cols + 1) + 4
H = 100 * opts.rows + 4
win = glumpy.Window(W, H)
loaded = False
updates = -1
batches = 0.
recent = collections.deque(maxlen=20)
errors = [collections.deque(maxlen=20) for _ in range(10)]
testset = [None] * 10
trainset = dict((i, []) for i in range(10))
loader = idx_reader.iterimages(opts.labels, opts.images, False)
rbm = opts.model and pickle.load(open(opts.model, 'rb')) or rbm.RBM(
28 * 28, opts.rows * opts.cols, opts.binary)
trainer = rbm.Trainer(
rbm,
momentum=opts.momentum,
target_sparsity=opts.sparsity,
)
def get_pixels():
global loaded
if not loaded and numpy.all([len(trainset[t]) > 10
for t in range(10)]):
loaded = True
visible_frames = [
fig.add_figure(args.n + 1, args.n,
position=(args.n, r)).add_frame(aspect=1)
for r in range(args.n)
]
weight_frames = [
fig.add_figure(args.n + 1, args.n, position=(c, r)).add_frame(aspect=1)
for r in range(args.n) for c in range(args.n)
]
loaded = False
recent = collections.deque(maxlen=20)
errors = [collections.deque(maxlen=20) for _ in range(10)]
trainset = dict((i, []) for i in range(10))
loader = idx_reader.iterimages(args.images, args.labels, True)
Model = lmj.rbm.Convolutional if args.conv else lmj.rbm.RBM
rbm = args.model and pickle.load(open(args.model, 'rb')) or Model(
28 * 28, args.n * args.n, not args.gaussian)
Trainer = lmj.rbm.ConvolutionalTrainer if args.conv else lmj.rbm.Trainer
trainer = Trainer(rbm,
l2=args.l2,
momentum=args.momentum,
target_sparsity=args.sparsity)
def get_pixels():
global loaded
if not loaded and all(len(v) > 50 for v in trainset.itervalues()):
loaded = True
hiddens = glumpy.image.Image(_hiddens)
weights = [glumpy.image.Image(w) for w in _weights]
visible_frames = [
fig.add_figure(args.n + 1, args.n, position=(args.n, r)).add_frame(aspect=1)
for r in range(args.n)]
weight_frames = [
fig.add_figure(args.n + 1, args.n, position=(c, r)).add_frame(aspect=1)
for r in range(args.n) for c in range(args.n)]
loaded = False
recent = collections.deque(maxlen=20)
errors = [collections.deque(maxlen=20) for _ in range(10)]
trainset = dict((i, []) for i in range(10))
loader = idx_reader.iterimages(args.images, args.labels, True)
Model = lmj.rbm.Convolutional if args.conv else lmj.rbm.RBM
rbm = args.model and pickle.load(open(args.model, 'rb')) or Model(
28 * 28, args.n * args.n, not args.gaussian)
Trainer = lmj.rbm.ConvolutionalTrainer if args.conv else lmj.rbm.Trainer
trainer = Trainer(rbm, l2=args.l2, momentum=args.momentum, target_sparsity=args.sparsity)
def get_pixels():
global loaded
if not loaded and all(len(v) > 50 for v in trainset.itervalues()):
loaded = True
if loaded:
t = rng.randint(10)
_hiddens = [glumpy.Image(h) for h in hiddens]
_weights = [[glumpy.Image(w, vmin=-m, vmax=m) for w in ws] for ws in weights]
W = 100 * (opts.cols + 1) + 4
H = 100 * opts.rows + 4
win = glumpy.Window(W, H)
loaded = False
updates = -1
batches = 0.0
recent = collections.deque(maxlen=20)
errors = [collections.deque(maxlen=20) for _ in range(10)]
testset = [None] * 10
trainset = dict((i, []) for i in range(10))
loader = idx_reader.iterimages(opts.labels, opts.images, False)
rbm = opts.model and pickle.load(open(opts.model, "rb")) or rbm.RBM(28 * 28, opts.rows * opts.cols, opts.binary)
trainer = rbm.Trainer(rbm, momentum=opts.momentum, target_sparsity=opts.sparsity)
def get_pixels():
global loaded
if not loaded and numpy.all([len(trainset[t]) > 10 for t in range(10)]):
loaded = True
if loaded and rng.random() < 0.99:
t = rng.randint(10)
pixels = trainset[t][rng.randint(len(trainset[t]))]
else:
t, pixels = loader.next()