def test_vae():
ds = odin.dataset.load_mnist()
W = T.variable(T.np_glorot_uniform(shape=(784, 512)), name='W')
WT = T.transpose(W)
encoder = odin.nnet.Dense((None, 28, 28), num_units=512, W=W, name='encoder')
# decoder = odin.nnet.Dense((None, 256), num_units=512, name='decoder1')
decoder = odin.nnet.Dense((None, 512), num_units=784, W=WT, name='decoder2')
vae = odin.nnet.VariationalEncoderDecoder(encoder=encoder, decoder=decoder,
prior_logsigma=1.7, batch_size=64)
# ====== prediction ====== #
x = ds['X_train'][:16]
f = T.function(inputs=vae.input_var, outputs=vae(training=False))
print("Predictions:", f(x)[0].shape)
f = T.function(
inputs=vae.input_var,
outputs=vae.set_reconstruction_mode(True)(training=False))
y = f(x)[0].reshape(-1, 28, 28)
print("Predictions:", y.shape)
odin.visual.plot_images(x)
odin.visual.plot_images(y)
odin.visual.plot_show()
print('Params:', [p.name for p in vae.get_params(False)])
print('Params(globals):', [p.name for p in vae.get_params(True)])
# ====== Optimizer ====== #
cost, updates = vae.get_optimization(
objective=odin.objectives.categorical_crossentropy,
optimizer=lambda x, y: odin.optimizers.sgd(x, y, learning_rate=0.01),
globals=True, training=True)
f = T.function(inputs=vae.input_var, outputs=cost, updates=updates)
cost = []
niter = ds['X_train'].iter_len() / 64
for j in xrange(2):
for i, x in enumerate(ds['X_train'].iter(64)):
if x.shape[0] != 64: continue
cost.append(f(x))
odin.logger.progress(i, niter, str(cost[-1]))
odin.visual.print_bar(cost)
# ====== reconstruc ====== #
f = T.function(
inputs=vae.input_var,
outputs=vae.set_reconstruction_mode(True)(training=False))
X_test = ds['X_test'][:16]
X_reco = f(X_test)[0].reshape(-1, 28, 28)
odin.visual.plot_images(X_test)
odin.visual.plot_images(X_reco)
odin.visual.plot_show()
def test_aED(): #AutoEncoderDecoder
Wa = T.variable(T.np_glorot_uniform(shape=(784, 256)), name='W')
Wb = T.variable(T.np_glorot_uniform(shape=(256, 128)), name='W')
d1a = odin.nnet.Dense((None, 28, 28), num_units=256, W=Wa, name='d1a',
nonlinearity=T.sigmoid)
d1b = odin.nnet.Dense(d1a, num_units=128, W=Wb, name='d1b',
nonlinearity=T.sigmoid)
# or d1b, (None, 128) as incoming
d2a = odin.nnet.Dense((None, 128), num_units=256, W=Wb.T, name='d2a',
nonlinearity=T.sigmoid)
d2b = odin.nnet.Dense(d2a, num_units=784, W=Wa.T, name='d2b',
nonlinearity=T.sigmoid)
aED = odin.nnet.AutoEncoderDecoder(d1b, d2b)
sgd = lambda x, y: odin.optimizers.sgd(x, y, learning_rate=0.01)
cost, updates = aED.get_optimization(
objective=odin.objectives.categorical_crossentropy,
optimizer=sgd,
globals=True)
f_train = T.function(
inputs=aED.input_var,
outputs=cost,
updates=updates)
cost = []
niter = ds['X_train'].iter_len() / 64
choices = None
for _ in xrange(3):
for i, x in enumerate(ds['X_train'].iter(64)):
cost.append(f_train(x))
odin.logger.progress(i, niter, title=str(cost[-1]))
print()
odin.visual.print_bar([i for i in cost if i == i], bincount=20)
W = T.get_value(aED.get_params(True)[0]).T.reshape(-1, 28, 28)
if choices is None:
choices = np.random.choice(
np.arange(W.shape[0]), size=16, replace=False)
W = W[choices]
odin.visual.plot_images(W)
plt.show(block=False)
raw_input('<enter>')
# ====== Output reconstruction ====== #
f_pred = T.function(
inputs=aED.input_var,
outputs=aED.set_reconstruction_mode(True)())
for i in xrange(3):
t = np.random.randint(ds['X_test'].shape[0] - 16)
X = ds['X_test'][t:t + 16]
X_pred = f_pred(X)[0].reshape(-1, 28, 28)
odin.visual.plot_images(X)
odin.visual.plot_images(X_pred)
odin.visual.plot_show()
# ====== OUtput hidden activation ====== #
f_pred = T.function(
inputs=aED.input_var,
outputs=aED())
X = ds['X_test'][t:t + 16]
print(f_pred(X)[0].shape)
