adgm.py 文件源码

def __init__(self, n_dim, n_out, n_chan=1, n_batch=128, n_superbatch=12800, model='bernoulli',
                opt_alg='adam', opt_params={'lr' : 1e-3, 'b1': 0.9, 'b2': 0.99}):
    # save model that wil be created
    self.model = model
    self.n_sample = 1 # adjustable parameter, though 1 works best in practice

    self.n_batch = n_batch
    self.n_lat = 200
    self.n_dim = n_dim
    self.n_chan = n_chan
    self.n_batch = n_batch

    Model.__init__(self, n_dim, n_chan, n_out, n_superbatch, opt_alg, opt_params)

    # sample generation
    Z = T.matrix(dtype=theano.config.floatX) # noise matrix
    l_px_mu, l_px_logsigma, l_pa_mu, l_pa_logsigma, \
        l_qz_mu, l_qz_logsigma, l_qa_mu, l_qa_logsigma, \
        l_qa, l_qz  = self.network
    sample = lasagne.layers.get_output(l_px_mu,  {l_qz : Z}, deterministic=True)
    self.sample = theano.function([Z], sample, on_unused_input='warn')