def pretrain_sae(X, hyper_params):
"""TODO: Docstring for pretrain_sae.
:arg1: TODO
:returns: TODO
"""
params = []
x = T.matrix()
layer_input = X
for idx, hidsize in enumerate(hyper_params['hidden_layers_sizes']):
W, b = train_autoencode(layer_input, int(np.sqrt(hidsize)),
hyper_params['iter_nums'][idx],
hyper_params['alphas'][idx],
hyper_params['decays'][idx],
hyper_params['betas'][idx],
hyper_params['rhos'][idx])[:2]
params.append((W, b))
visualize_ae_image(W, 'sae_%d.png' % (idx), int(np.sqrt(W.get_value().shape[0])),
int(np.sqrt(W.get_value().shape[1])), process_grey)
f = function(inputs=[x,], outputs=[ae_encode(x, W, b)], name='f')
layer_input = f(layer_input)[0]
return params, layer_input
def sae_predict(X, weights):
"""TODO: Docstring for sae_predict.
:returns: TODO
"""
inp = T.matrix(name='inp')
for idx, hp in enumerate(weights):
if idx == 0:
res = ae_encode(inp, hp[0], hp[1])
elif idx != len(weights)-1:
res = ae_encode(res, hp[0], hp[1])
else:
res = softmax_predict(res, hp[0], hp[1])
f = function(inputs=[inp,], outputs=[res,], name='f')
return f(X)[0]
def sae_extract(x, weights):
"""TODO: Docstring for sae_extract.
:returns: TODO
"""
pred = x
for hp in weights:
pred = ae_encode(pred, hp[0], hp[1])
return pred
