def __init__(self, input_shape, output_dim, name='BernoulliMLPRegressorWithModel', hidden_sizes=(32, 32), hidden_nonlinearity=tf.nn.relu, hidden_w_init=tf.glorot_uniform_initializer(), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=tf.nn.sigmoid, output_w_init=tf.glorot_uniform_initializer(), 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.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: optimizer = LbfgsOptimizer(**optimizer_args) else: optimizer = optimizer(**optimizer_args) if tr_optimizer is None: tr_optimizer = ConjugateGradientOptimizer(**tr_optimizer_args) else: tr_optimizer = tr_optimizer(**tr_optimizer_args) self._optimizer = optimizer self._tr_optimizer = tr_optimizer 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._dist = Bernoulli(output_dim) self._initialize()
def __init__(self, input_shape, output_dim, name='GaussianMLPRegressorWithModel', hidden_sizes=(32, 32), hidden_nonlinearity=tf.nn.tanh, optimizer=None, optimizer_args=None, use_trust_region=True, max_kl_step=0.01, learn_std=True, init_std=1.0, adaptive_std=False, std_share_network=False, std_hidden_sizes=(32, 32), std_nonlinearity=None, layer_normalization=False, normalize_inputs=True, normalize_outputs=True, subsample_factor=1.0): super().__init__(input_shape, output_dim, name) self._use_trust_region = use_trust_region self._subsample_factor = subsample_factor self._max_kl_step = max_kl_step self._normalize_inputs = normalize_inputs self._normalize_outputs = normalize_outputs with tf.variable_scope(self._name, reuse=False) as self._variable_scope: if optimizer_args is None: optimizer_args = dict() if optimizer is None: if use_trust_region: optimizer = PenaltyLbfgsOptimizer(**optimizer_args) else: optimizer = LbfgsOptimizer(**optimizer_args) else: optimizer = optimizer(**optimizer_args) self._optimizer = optimizer self.model = GaussianMLPRegressorModel( input_shape=input_shape, output_dim=self._output_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, output_nonlinearity=None, learn_std=learn_std, adaptive_std=adaptive_std, std_share_network=std_share_network, init_std=init_std, min_std=None, max_std=None, std_hidden_sizes=std_hidden_sizes, std_hidden_nonlinearity=std_nonlinearity, std_output_nonlinearity=None, std_parameterization='exp', 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.initializers.glorot_uniform(), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=None, output_w_init=tf.initializers.glorot_uniform(), 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()
def __init__( self, input_shape, output_dim, name="BernoulliMLPRegressor", hidden_sizes=(32, 32), hidden_nonlinearity=tf.nn.relu, optimizer=None, tr_optimizer=None, use_trust_region=True, step_size=0.01, normalize_inputs=True, no_initial_trust_region=True, ): """ :param input_shape: Shape of the input data. :param output_dim: Dimension of output. :param hidden_sizes: Number of hidden units of each layer of the mean network. :param hidden_nonlinearity: Non-linearity used for each layer of the mean network. :param optimizer: Optimizer for minimizing the negative log-likelihood. :param use_trust_region: Whether to use trust region constraint. :param step_size: KL divergence constraint for each iteration """ Serializable.quick_init(self, locals()) with tf.variable_scope(name): if optimizer is None: optimizer = LbfgsOptimizer(name="optimizer") if tr_optimizer is None: tr_optimizer = ConjugateGradientOptimizer() self.output_dim = output_dim self.optimizer = optimizer self.tr_optimizer = tr_optimizer p_network = MLP(input_shape=input_shape, output_dim=output_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, output_nonlinearity=tf.nn.sigmoid, name="p_network") l_p = p_network.output_layer LayersPowered.__init__(self, [l_p]) xs_var = p_network.input_layer.input_var ys_var = tf.placeholder(dtype=tf.float32, shape=(None, output_dim), name="ys") old_p_var = tf.placeholder(dtype=tf.float32, shape=(None, output_dim), name="old_p") x_mean_var = tf.get_variable(name="x_mean", initializer=tf.zeros_initializer(), shape=(1, ) + input_shape) x_std_var = tf.get_variable(name="x_std", initializer=tf.ones_initializer(), shape=(1, ) + input_shape) normalized_xs_var = (xs_var - x_mean_var) / x_std_var p_var = L.get_output(l_p, {p_network.input_layer: normalized_xs_var}) old_info_vars = dict(p=old_p_var) info_vars = dict(p=p_var) dist = self._dist = Bernoulli(output_dim) mean_kl = tf.reduce_mean(dist.kl_sym(old_info_vars, info_vars)) loss = -tf.reduce_mean(dist.log_likelihood_sym(ys_var, info_vars)) predicted = p_var >= 0.5 self.f_predict = tensor_utils.compile_function([xs_var], predicted) self.f_p = tensor_utils.compile_function([xs_var], p_var) self.l_p = l_p self.optimizer.update_opt(loss=loss, target=self, network_outputs=[p_var], inputs=[xs_var, ys_var]) self.tr_optimizer.update_opt(loss=loss, target=self, network_outputs=[p_var], inputs=[xs_var, ys_var, old_p_var], leq_constraint=(mean_kl, step_size)) self.use_trust_region = use_trust_region self.name = name self.normalize_inputs = normalize_inputs self.x_mean_var = x_mean_var self.x_std_var = x_std_var self.first_optimized = not no_initial_trust_region
def __init__(self, input_shape, output_dim, name="GaussianMLPRegressor", mean_network=None, hidden_sizes=(32, 32), hidden_nonlinearity=tf.nn.tanh, optimizer=None, optimizer_args=None, use_trust_region=True, max_kl_step=0.01, learn_std=True, init_std=1.0, adaptive_std=False, std_share_network=False, std_hidden_sizes=(32, 32), std_nonlinearity=None, normalize_inputs=True, normalize_outputs=True, subsample_factor=1.0): """ :param input_shape: Shape of the input data. :param output_dim: Dimension of output. :param hidden_sizes: Number of hidden units of each layer of the mean network. :param hidden_nonlinearity: Non-linearity used for each layer of the mean network. :param optimizer: Optimizer for minimizing the negative log-likelihood. :param use_trust_region: Whether to use trust region constraint. :param max_kl_step: KL divergence constraint for each iteration :param learn_std: Whether to learn the standard deviations. Only effective if adaptive_std is False. If adaptive_std is True, this parameter is ignored, and the weights for the std network are always earned. :param adaptive_std: Whether to make the std a function of the states. :param std_share_network: Whether to use the same network as the mean. :param std_hidden_sizes: Number of hidden units of each layer of the std network. Only used if `std_share_network` is False. It defaults to the same architecture as the mean. :param std_nonlinearity: Non-linearity used for each layer of the std network. Only used if `std_share_network` is False. It defaults to the same non-linearity as the mean. """ Parameterized.__init__(self) Serializable.quick_init(self, locals()) self._mean_network_name = "mean_network" self._std_network_name = "std_network" with tf.variable_scope(name): if optimizer_args is None: optimizer_args = dict() if optimizer is None: if use_trust_region: optimizer = PenaltyLbfgsOptimizer(**optimizer_args) else: optimizer = LbfgsOptimizer(**optimizer_args) else: optimizer = optimizer(**optimizer_args) self._optimizer = optimizer self._subsample_factor = subsample_factor if mean_network is None: if std_share_network: mean_network = MLP( name="mean_network", input_shape=input_shape, output_dim=2 * output_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, output_nonlinearity=None, ) l_mean = L.SliceLayer( mean_network.output_layer, slice(output_dim), name="mean_slice", ) else: mean_network = MLP( name="mean_network", input_shape=input_shape, output_dim=output_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, output_nonlinearity=None, ) l_mean = mean_network.output_layer if adaptive_std: l_log_std = MLP( name="log_std_network", input_shape=input_shape, input_var=mean_network.input_layer.input_var, output_dim=output_dim, hidden_sizes=std_hidden_sizes, hidden_nonlinearity=std_nonlinearity, output_nonlinearity=None, ).output_layer elif std_share_network: l_log_std = L.SliceLayer( mean_network.output_layer, slice(output_dim, 2 * output_dim), name="log_std_slice", ) else: l_log_std = L.ParamLayer( mean_network.input_layer, num_units=output_dim, param=tf.constant_initializer(np.log(init_std)), name="output_log_std", trainable=learn_std, ) LayersPowered.__init__(self, [l_mean, l_log_std]) xs_var = mean_network.input_layer.input_var ys_var = tf.placeholder(dtype=tf.float32, name="ys", shape=(None, output_dim)) old_means_var = tf.placeholder(dtype=tf.float32, name="ys", shape=(None, output_dim)) old_log_stds_var = tf.placeholder(dtype=tf.float32, name="old_log_stds", shape=(None, output_dim)) x_mean_var = tf.Variable( np.zeros((1, ) + input_shape, dtype=np.float32), name="x_mean", ) x_std_var = tf.Variable( np.ones((1, ) + input_shape, dtype=np.float32), name="x_std", ) y_mean_var = tf.Variable( np.zeros((1, output_dim), dtype=np.float32), name="y_mean", ) y_std_var = tf.Variable( np.ones((1, output_dim), dtype=np.float32), name="y_std", ) normalized_xs_var = (xs_var - x_mean_var) / x_std_var normalized_ys_var = (ys_var - y_mean_var) / y_std_var with tf.name_scope(self._mean_network_name, values=[normalized_xs_var]): normalized_means_var = L.get_output( l_mean, {mean_network.input_layer: normalized_xs_var}) with tf.name_scope(self._std_network_name, values=[normalized_xs_var]): normalized_log_stds_var = L.get_output( l_log_std, {mean_network.input_layer: normalized_xs_var}) means_var = normalized_means_var * y_std_var + y_mean_var log_stds_var = normalized_log_stds_var + tf.log(y_std_var) normalized_old_means_var = (old_means_var - y_mean_var) / y_std_var normalized_old_log_stds_var = old_log_stds_var - tf.log(y_std_var) dist = self._dist = DiagonalGaussian(output_dim) normalized_dist_info_vars = dict(mean=normalized_means_var, log_std=normalized_log_stds_var) mean_kl = tf.reduce_mean( dist.kl_sym( dict(mean=normalized_old_means_var, log_std=normalized_old_log_stds_var), normalized_dist_info_vars, )) loss = -tf.reduce_mean( dist.log_likelihood_sym(normalized_ys_var, normalized_dist_info_vars)) self._f_predict = tensor_utils.compile_function([xs_var], means_var) self._f_pdists = tensor_utils.compile_function( [xs_var], [means_var, log_stds_var]) self._l_mean = l_mean self._l_log_std = l_log_std optimizer_args = dict( loss=loss, target=self, network_outputs=[ normalized_means_var, normalized_log_stds_var ], ) if use_trust_region: optimizer_args["leq_constraint"] = (mean_kl, max_kl_step) optimizer_args["inputs"] = [ xs_var, ys_var, old_means_var, old_log_stds_var ] else: optimizer_args["inputs"] = [xs_var, ys_var] self._optimizer.update_opt(**optimizer_args) self._use_trust_region = use_trust_region self._name = name self._normalize_inputs = normalize_inputs self._normalize_outputs = normalize_outputs self._mean_network = mean_network self._x_mean_var = x_mean_var self._x_std_var = x_std_var self._y_mean_var = y_mean_var self._y_std_var = y_std_var # Optionally create assign operations for normalization if self._normalize_inputs: self._x_mean_var_ph = tf.placeholder( shape=(1, ) + input_shape, dtype=tf.float32, ) self._x_std_var_ph = tf.placeholder( shape=(1, ) + input_shape, dtype=tf.float32, ) self._assign_x_mean = tf.assign(self._x_mean_var, self._x_mean_var_ph) self._assign_x_std = tf.assign(self._x_std_var, self._x_std_var_ph) if self._normalize_outputs: self._y_mean_var_ph = tf.placeholder( shape=(1, output_dim), dtype=tf.float32, ) self._y_std_var_ph = tf.placeholder( shape=(1, output_dim), dtype=tf.float32, ) self._assign_y_mean = tf.assign(self._y_mean_var, self._y_mean_var_ph) self._assign_y_std = tf.assign(self._y_std_var, self._y_std_var_ph)
def __init__( self, input_shape, output_dim, name='CategoricalMLPRegressor', prob_network=None, hidden_sizes=(32, 32), hidden_nonlinearity=tf.nn.tanh, optimizer=None, tr_optimizer=None, use_trust_region=True, max_kl_step=0.01, normalize_inputs=True, no_initial_trust_region=True, ): """ :param input_shape: Shape of the input data. :param output_dim: Dimension of output. :param hidden_sizes: Number of hidden units of each layer of the mean network. :param hidden_nonlinearity: Non-linearity used for each layer of the mean network. :param optimizer: Optimizer for minimizing the negative log-likelihood. :param use_trust_region: Whether to use trust region constraint. :param max_kl_step: KL divergence constraint for each iteration """ Parameterized.__init__(self) Serializable.quick_init(self, locals()) with tf.compat.v1.variable_scope(name, 'CategoricalMLPRegressor'): if optimizer is None: optimizer = LbfgsOptimizer() if tr_optimizer is None: tr_optimizer = ConjugateGradientOptimizer() self.output_dim = output_dim self.optimizer = optimizer self.tr_optimizer = tr_optimizer self._prob_network_name = 'prob_network' if prob_network is None: prob_network = MLP(input_shape=input_shape, output_dim=output_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, output_nonlinearity=tf.nn.softmax, name=self._prob_network_name) l_prob = prob_network.output_layer LayersPowered.__init__(self, [l_prob]) xs_var = prob_network.input_layer.input_var ys_var = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, output_dim], name='ys') old_prob_var = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, output_dim], name='old_prob') x_mean_var = tf.compat.v1.get_variable( name='x_mean', shape=(1, ) + input_shape, initializer=tf.constant_initializer(0., dtype=tf.float32)) x_std_var = tf.compat.v1.get_variable( name='x_std', shape=(1, ) + input_shape, initializer=tf.constant_initializer(1., dtype=tf.float32)) normalized_xs_var = (xs_var - x_mean_var) / x_std_var with tf.name_scope(self._prob_network_name, values=[normalized_xs_var]): prob_var = L.get_output( l_prob, {prob_network.input_layer: normalized_xs_var}) old_info_vars = dict(prob=old_prob_var) info_vars = dict(prob=prob_var) dist = self._dist = Categorical(output_dim) mean_kl = tf.reduce_mean(dist.kl_sym(old_info_vars, info_vars)) loss = -tf.reduce_mean(dist.log_likelihood_sym(ys_var, info_vars)) predicted = tf.one_hot(tf.argmax(prob_var, axis=1), depth=output_dim) self.prob_network = prob_network self.f_predict = tensor_utils.compile_function([xs_var], predicted) self.f_prob = tensor_utils.compile_function([xs_var], prob_var) self.l_prob = l_prob self.optimizer.update_opt(loss=loss, target=self, network_outputs=[prob_var], inputs=[xs_var, ys_var]) self.tr_optimizer.update_opt(loss=loss, target=self, network_outputs=[prob_var], inputs=[xs_var, ys_var, old_prob_var], leq_constraint=(mean_kl, max_kl_step)) self.use_trust_region = use_trust_region self.name = name self.normalize_inputs = normalize_inputs self.x_mean_var = x_mean_var self.x_std_var = x_std_var self.first_optimized = not no_initial_trust_region
def __init__( self, input_shape, output_dim, name="DeterministicMLPRegressor", network=None, hidden_sizes=(32, 32), hidden_nonlinearity=tf.nn.tanh, output_nonlinearity=None, optimizer=None, optimizer_args=None, normalize_inputs=True, ): """ :param input_shape: Shape of the input data. :param output_dim: Dimension of output. :param hidden_sizes: Number of hidden units of each layer of the mean network. :param hidden_nonlinearity: Non-linearity used for each layer of the mean network. :param optimizer: Optimizer for minimizing the negative log-likelihood. """ Parameterized.__init__(self) Serializable.quick_init(self, locals()) with tf.variable_scope(name, "DeterministicMLPRegressor"): if optimizer_args is None: optimizer_args = dict() if optimizer is None: optimizer = LbfgsOptimizer(**optimizer_args) else: optimizer = optimizer(**optimizer_args) self.output_dim = output_dim self.optimizer = optimizer self._network_name = "network" if network is None: network = MLP(input_shape=input_shape, output_dim=output_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, output_nonlinearity=output_nonlinearity, name=self._network_name) l_out = network.output_layer LayersPowered.__init__(self, [l_out]) xs_var = network.input_layer.input_var ys_var = tf.placeholder(dtype=tf.float32, shape=[None, output_dim], name="ys") x_mean_var = tf.get_variable(name="x_mean", shape=(1, ) + input_shape, initializer=tf.constant_initializer( 0., dtype=tf.float32)) x_std_var = tf.get_variable(name="x_std", shape=(1, ) + input_shape, initializer=tf.constant_initializer( 1., dtype=tf.float32)) normalized_xs_var = (xs_var - x_mean_var) / x_std_var with tf.name_scope(self._network_name, values=[normalized_xs_var]): fit_ys_var = L.get_output( l_out, {network.input_layer: normalized_xs_var}) loss = -tf.reduce_mean(tf.square(fit_ys_var - ys_var)) self.f_predict = tensor_utils.compile_function([xs_var], fit_ys_var) optimizer_args = dict( loss=loss, target=self, network_outputs=[fit_ys_var], ) optimizer_args["inputs"] = [xs_var, ys_var] self.optimizer.update_opt(**optimizer_args) self.name = name self.l_out = l_out self.normalize_inputs = normalize_inputs self.x_mean_var = x_mean_var self.x_std_var = x_std_var
def __init__(self, input_shape, output_dim, filter_dims, num_filters, strides, padding, hidden_sizes, 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(), name='GaussianCNNRegressorWithModel', learn_std=True, init_std=1.0, adaptive_std=False, std_share_network=False, std_filter_dims=[], std_num_filters=[], std_strides=[], std_padding='SAME', std_hidden_sizes=[], std_hidden_nonlinearity=None, std_output_nonlinearity=None, layer_normalization=False, normalize_inputs=True, normalize_outputs=True, subsample_factor=1., optimizer=None, optimizer_args=dict(), use_trust_region=True, max_kl_step=0.01): super().__init__(input_shape, output_dim, name) self._use_trust_region = use_trust_region self._subsample_factor = subsample_factor self._max_kl_step = max_kl_step self._normalize_inputs = normalize_inputs self._normalize_outputs = normalize_outputs 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: if use_trust_region: optimizer = PenaltyLbfgsOptimizer(**optimizer_args) else: optimizer = LbfgsOptimizer(**optimizer_args) else: optimizer = optimizer(**optimizer_args) self._optimizer = optimizer self.model = GaussianCNNRegressorModel( input_shape=input_shape, output_dim=output_dim, num_filters=num_filters, filter_dims=filter_dims, strides=strides, padding=padding, 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, learn_std=learn_std, adaptive_std=adaptive_std, std_share_network=std_share_network, init_std=init_std, min_std=None, max_std=None, std_num_filters=std_num_filters, std_filter_dims=std_filter_dims, std_strides=std_strides, std_padding=std_padding, std_hidden_sizes=std_hidden_sizes, std_hidden_nonlinearity=std_hidden_nonlinearity, std_output_nonlinearity=std_output_nonlinearity, std_parameterization='exp', layer_normalization=layer_normalization) self._initialize()
def __init__(self, input_shape, output_dim, conv_filters, conv_filter_sizes, conv_strides, conv_pads, hidden_sizes, hidden_nonlinearity=tf.nn.tanh, output_nonlinearity=None, name='GaussianConvRegressor', mean_network=None, learn_std=True, init_std=1.0, adaptive_std=False, std_share_network=False, std_conv_filters=[], std_conv_filter_sizes=[], std_conv_strides=[], std_conv_pads=[], std_hidden_sizes=[], std_hidden_nonlinearity=None, std_output_nonlinearity=None, normalize_inputs=True, normalize_outputs=True, subsample_factor=1., optimizer=None, optimizer_args=dict(), use_trust_region=True, max_kl_step=0.01): Parameterized.__init__(self) Serializable.quick_init(self, locals()) self._mean_network_name = 'mean_network' self._std_network_name = 'std_network' with tf.compat.v1.variable_scope(name): if optimizer is None: if use_trust_region: optimizer = PenaltyLbfgsOptimizer(**optimizer_args) else: optimizer = LbfgsOptimizer(**optimizer_args) else: optimizer = optimizer(**optimizer_args) self._optimizer = optimizer self._subsample_factor = subsample_factor if mean_network is None: if std_share_network: b = np.concatenate( [ np.zeros(output_dim), np.full(output_dim, np.log(init_std)) ], axis=0) # yapf: disable b = tf.constant_initializer(b) mean_network = ConvNetwork( name=self._mean_network_name, input_shape=input_shape, output_dim=2 * output_dim, conv_filters=conv_filters, conv_filter_sizes=conv_filter_sizes, conv_strides=conv_strides, conv_pads=conv_pads, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, output_nonlinearity=output_nonlinearity, output_b_init=b) l_mean = layers.SliceLayer( mean_network.output_layer, slice(output_dim), name='mean_slice', ) else: mean_network = ConvNetwork( name=self._mean_network_name, input_shape=input_shape, output_dim=output_dim, conv_filters=conv_filters, conv_filter_sizes=conv_filter_sizes, conv_strides=conv_strides, conv_pads=conv_pads, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, output_nonlinearity=output_nonlinearity) l_mean = mean_network.output_layer if adaptive_std: l_log_std = ConvNetwork( name=self._std_network_name, input_shape=input_shape, output_dim=output_dim, conv_filters=std_conv_filters, conv_filter_sizes=std_conv_filter_sizes, conv_strides=std_conv_strides, conv_pads=std_conv_pads, hidden_sizes=std_hidden_sizes, hidden_nonlinearity=std_hidden_nonlinearity, output_nonlinearity=std_output_nonlinearity, output_b_init=tf.constant_initializer(np.log(init_std)), ).output_layer elif std_share_network: l_log_std = layers.SliceLayer( mean_network.output_layer, slice(output_dim, 2 * output_dim), name='log_std_slice', ) else: l_log_std = layers.ParamLayer( mean_network.input_layer, num_units=output_dim, param=tf.constant_initializer(np.log(init_std)), trainable=learn_std, name=self._std_network_name, ) LayersPowered.__init__(self, [l_mean, l_log_std]) xs_var = mean_network.input_layer.input_var ys_var = tf.compat.v1.placeholder( dtype=tf.float32, name='ys', shape=(None, output_dim)) old_means_var = tf.compat.v1.placeholder( dtype=tf.float32, name='ys', shape=(None, output_dim)) old_log_stds_var = tf.compat.v1.placeholder( dtype=tf.float32, name='old_log_stds', shape=(None, output_dim)) x_mean_var = tf.Variable( np.zeros((1, np.prod(input_shape)), dtype=np.float32), name='x_mean', ) x_std_var = tf.Variable( np.ones((1, np.prod(input_shape)), dtype=np.float32), name='x_std', ) y_mean_var = tf.Variable( np.zeros((1, output_dim), dtype=np.float32), name='y_mean', ) y_std_var = tf.Variable( np.ones((1, output_dim), dtype=np.float32), name='y_std', ) normalized_xs_var = (xs_var - x_mean_var) / x_std_var normalized_ys_var = (ys_var - y_mean_var) / y_std_var with tf.name_scope( self._mean_network_name, values=[normalized_xs_var]): normalized_means_var = layers.get_output( l_mean, {mean_network.input_layer: normalized_xs_var}) with tf.name_scope( self._std_network_name, values=[normalized_xs_var]): normalized_log_stds_var = layers.get_output( l_log_std, {mean_network.input_layer: normalized_xs_var}) means_var = normalized_means_var * y_std_var + y_mean_var log_stds_var = normalized_log_stds_var + tf.math.log(y_std_var) normalized_old_means_var = (old_means_var - y_mean_var) / y_std_var normalized_old_log_stds_var = ( old_log_stds_var - tf.math.log(y_std_var)) dist = self._dist = DiagonalGaussian(output_dim) normalized_dist_info_vars = dict( mean=normalized_means_var, log_std=normalized_log_stds_var) mean_kl = tf.reduce_mean( dist.kl_sym( dict( mean=normalized_old_means_var, log_std=normalized_old_log_stds_var), normalized_dist_info_vars, )) loss = -tf.reduce_mean( dist.log_likelihood_sym(normalized_ys_var, normalized_dist_info_vars)) self._f_predict = tensor_utils.compile_function([xs_var], means_var) self._f_pdists = tensor_utils.compile_function( [xs_var], [means_var, log_stds_var]) self._l_mean = l_mean self._l_log_std = l_log_std optimizer_args = dict( loss=loss, target=self, network_outputs=[ normalized_means_var, normalized_log_stds_var ], ) if use_trust_region: optimizer_args['leq_constraint'] = (mean_kl, max_kl_step) optimizer_args['inputs'] = [ xs_var, ys_var, old_means_var, old_log_stds_var ] else: optimizer_args['inputs'] = [xs_var, ys_var] self._optimizer.update_opt(**optimizer_args) self._use_trust_region = use_trust_region self._name = name self._normalize_inputs = normalize_inputs self._normalize_outputs = normalize_outputs self._mean_network = mean_network self._x_mean_var = x_mean_var self._x_std_var = x_std_var self._y_mean_var = y_mean_var self._y_std_var = y_std_var