def _make_bias(self, params):
bias = []
for i in range(params.num_layers):
bias.append(L.RNNParameterSet(
np.random.rand(params.hidden_size).astype(np.float32),
np.random.rand(params.hidden_size).astype(np.float32)))
return bias
def _make_weights(self, params, skip=False):
weights = []
for i in range(params.num_layers):
w_out = params.hidden_size
w_in = params.hidden_size if i > 0 or skip else params.data_size
weights.append(L.RNNParameterSet(
np.random.rand(w_out, w_in).astype(np.float32),
np.random.rand(w_out, w_out).astype(np.float32)))
return weights