def main(root, pattern='*'):
bins = np.logspace(-4, 2, 127)
for t in lmj.cubes.Experiment(root).trials_matching(pattern):
stats, jac = t.jacobian(10, markers=MARKERS)
plt.colorbar(plt.imshow(jac.values[500:800].T,
vmin=-1, vmax=1, cmap='coolwarm'))
plt.show()
hist = np.zeros((len(jac), len(bins)), float)
for t, j in enumerate(jac.values):
for i in bins.searchsorted(abs(j)):
hist[t, i-1] += 1
plt.colorbar(plt.imshow(hist[500:800, :-1].T, cmap='coolwarm'))
plt.show()
def main(root, pattern='*'):
bins = np.logspace(-4, 2, 127)
for t in lmj.cubes.Experiment(root).trials_matching(pattern):
stats, jac = t.jacobian(10, markers=MARKERS)
plt.colorbar(
plt.imshow(jac.values[500:800].T, vmin=-1, vmax=1,
cmap='coolwarm'))
plt.show()
hist = np.zeros((len(jac), len(bins)), float)
for t, j in enumerate(jac.values):
for i in bins.searchsorted(abs(j)):
hist[t, i - 1] += 1
plt.colorbar(plt.imshow(hist[500:800, :-1].T, cmap='coolwarm'))
plt.show()
def main(args):
data = np.load(args.dataset, mmap_mode='r')
N = data.shape[1]
T = args.frames
K = int(N * T * args.overcomplete)
def batches():
batch = np.zeros((args.batch_size, N * T), 'f')
for b in range(args.batch_size):
o = np.random.randint(len(data) - T - 1)
batch[b] = data[o:o+T].ravel()
return [batch]
net = theanets.Autoencoder([N * T, (K, 'linear'), (N * T, 'tied')])
net.train(batches,
monitors={'hid1:out': (-0.1, -0.01, 0.01, 0.1)},
**models.kwargs_from_args(args))
D = net.find('hid1', 'w').get_value().T
R = 6
C = 18
dimg = np.zeros((R * (N + 1) - 1, C * (T + 1) - 1), float)
bimg = np.zeros((R * (N + 1) - 1, C * (T + 1) - 1), float)
idx = abs(D).max(axis=1).argsort()[::-1]
for r in range(R):
for c in range(C):
o = np.random.randint(len(data) - T - 1)
dimg[r*(N+1):r*(N+1)+N, c*(T+1):c*(T+1)+T] = data[o:o+T].T
bimg[r*(N+1):r*(N+1)+N, c*(T+1):c*(T+1)+T] = D[idx[r * C + c]].reshape((T, N)).T
_, (dax, bax) = plt.subplots(1, 2)
dax.imshow(dimg, cmap='coolwarm')
sns.despine(ax=dax, left=True, bottom=True)
dax.set_xticks([])
dax.set_yticks([])
bax.imshow(bimg, cmap='coolwarm')
sns.despine(ax=bax, left=True, bottom=True)
bax.set_xticks([])
bax.set_yticks([])
plt.show()
logging.info('%s: saving %s %s', args.codebook, D.shape, D.dtype)
np.save(args.codebook, D)
