def __init__(self, input_shape, output_dim, name='CategoricalMLPRegressor', hidden_sizes=(32, 32), hidden_nonlinearity=tf.nn.tanh, hidden_w_init=tf.initializers.glorot_uniform(), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=tf.nn.softmax, output_w_init=tf.initializers.glorot_uniform(), output_b_init=tf.zeros_initializer(), optimizer=None, optimizer_args=None, tr_optimizer=None, tr_optimizer_args=None, use_trust_region=True, max_kl_step=0.01, normalize_inputs=True, layer_normalization=False): super().__init__(input_shape, output_dim, name) self._use_trust_region = use_trust_region self._max_kl_step = max_kl_step self._normalize_inputs = normalize_inputs with tf.compat.v1.variable_scope(self._name, reuse=False) as vs: self._variable_scope = vs if optimizer_args is None: optimizer_args = dict() if tr_optimizer_args is None: tr_optimizer_args = dict() if optimizer is None: self._optimizer = make_optimizer(LbfgsOptimizer, **optimizer_args) else: self._optimizer = make_optimizer(optimizer, **optimizer_args) if tr_optimizer is None: self._tr_optimizer = make_optimizer(ConjugateGradientOptimizer, **tr_optimizer_args) else: self._tr_optimizer = make_optimizer(tr_optimizer, **tr_optimizer_args) self._first_optimized = False self.model = NormalizedInputMLPModel( input_shape, output_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, hidden_w_init=hidden_w_init, hidden_b_init=hidden_b_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, output_b_init=output_b_init, layer_normalization=layer_normalization) self._initialize()
def __init__(self, input_shape, output_dim, name='ContinuousMLPRegressor', hidden_sizes=(32, 32), hidden_nonlinearity=tf.nn.tanh, hidden_w_init=tf.glorot_uniform_initializer(), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=None, output_w_init=tf.glorot_uniform_initializer(), output_b_init=tf.zeros_initializer(), optimizer=None, optimizer_args=None, normalize_inputs=True): super().__init__(input_shape, output_dim, name) self._normalize_inputs = normalize_inputs with tf.compat.v1.variable_scope(self._name, reuse=False) as vs: self._variable_scope = vs if optimizer_args is None: optimizer_args = dict() if optimizer is None: optimizer = LbfgsOptimizer(**optimizer_args) else: optimizer = optimizer(**optimizer_args) self._optimizer = optimizer self.model = NormalizedInputMLPModel( input_shape=input_shape, output_dim=output_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, hidden_w_init=hidden_w_init, hidden_b_init=hidden_b_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, output_b_init=output_b_init) self._initialize()