def create_mlp_model(hyper_params):
n_in=hyper_params['n_in']
n_hidden=hyper_params['n_hidden']
n_out=hyper_params['n_out']
rand=deep.RandomNum()
hidden_shape=(n_in,n_hidden)
hidden=deep.create_layer(hidden_shape,rand,"_hidden")
vis_shape=(n_hidden,n_out)
logistic=deep.create_layer(vis_shape,rand,"_vis")
return MlpModel(hidden,logistic)
def create_sda_model(hyper_params):
n_in=hyper_params['n_in']
n_ae=hyper_params['n_ae']
n_hidden=hyper_params['n_hidden']
n_out=hyper_params['n_out']
ae_path=hyper_params['ae_path']
ae_pretrain=autoencoder.read_autoencoder(ae_path)
rand=deep.RandomNum()
ae_layer=deep.create_layer((n_in,n_ae),rand,"_ae")
hidden=deep.create_layer((n_ae,n_hidden),rand,"_hidden")
logistic=deep.create_layer((n_hidden,n_out),rand,"_vis")
W_init,b_init=ae_pretrain.get_numpy()
ae_layer.init_params(W_init,b_init)
return SdaModel(ae_layer,hidden,logistic)
