def random(n_units, mean=None):
h_network = nnet.random_linear_then_tanh_chain(n_units[:-1])
mean_network = nnet.Linear.random(n_units[-2], n_units[-1])
if mean is not None:
mean_network.b.set_value(mean.astype(floatX))
sigma_network = nnet.NNet().add_layer(nnet.Linear.random(n_units[-2], n_units[-1])).add_layer(nnet.Exponential())
return GaussianSampler(h_network, mean_network, sigma_network)
def random(n_units, mean=None):
h_network = nnet.random_linear_then_tanh_chain(n_units[:-1])
mean_network = nnet.Linear.random(n_units[-2], n_units[-1])
if mean is not None:
mean_network.b.set_value(mean.astype(floatX))
sigma_network = nnet.NNet().add_layer(
nnet.Linear.random(n_units[-2],
n_units[-1])).add_layer(nnet.Exponential())
return GaussianSampler(h_network, mean_network, sigma_network)
def random(n_units, bias=None):
mean_network = nnet.random_linear_then_tanh_chain(n_units[:-1])
mean_network.add_layer(nnet.Linear.random(n_units[-2], n_units[-1]))
if bias is not None:
mean_network.layers[-1].b.set_value(bias.astype(theano.config.floatX))
mean_network.add_layer(nnet.Sigmoid())
return BernoulliSampler(mean_network)
def random(n_units, bias=None):
mean_network = nnet.random_linear_then_tanh_chain(n_units[:-1])
mean_network.add_layer(nnet.Linear.random(n_units[-2], n_units[-1]))
if bias is not None:
mean_network.layers[-1].b.set_value(
bias.astype(theano.config.floatX))
mean_network.add_layer(nnet.Sigmoid())
return BernoulliSampler(mean_network)
