def step2(x1, x2, x3, x4, h, mem):
h = h.dimshuffle('x', 0) # 1x20
h1 = T.dot(h, W1) # 1x10
h2 = T.dot(h, W2)
h3 = T.dot(h, W3)
h4 = T.dot(h, W4)
test = (T.dot(x1, T.transpose(h1)) +
T.dot(x2, T.transpose(h2)) +
T.dot(x3, T.transpose(h3)) +
T.dot(x4, T.transpose(h4)))
return test + mem
def step3(x, h, mem):
x1 = x[:, 0:10] # seq_lenx10
x2 = x[:, 10:20]
x3 = x[:, 20:30]
x4 = x[:, 30:40]
h = h.dimshuffle('x', 0) # 1x20
h1 = T.dot(h, W1) # 1x10
h2 = T.dot(h, W2)
h3 = T.dot(h, W3)
h4 = T.dot(h, W4)
test = (T.dot(x1, T.transpose(h1)) +
T.dot(x2, T.transpose(h2)) +
T.dot(x3, T.transpose(h3)) +
T.dot(x4, T.transpose(h4)))
return test + mem
def step1(x, h, mem):
x1 = x[:, 0:10] # seq_lenx10
x2 = x[:, 10:20]
x3 = x[:, 20:30]
x4 = x[:, 30:40]
h = T.dot(h.dimshuffle('x', 0), W)
h1 = h[:, 0:10] # 1x10
h2 = h[:, 10:20]
h3 = h[:, 20:30]
h4 = h[:, 30:40]
test = (T.dot(x1, T.transpose(h1)) +
T.dot(x2, T.transpose(h2)) +
T.dot(x3, T.transpose(h3)) +
T.dot(x4, T.transpose(h4)))
return test + mem
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()
