garage.tf.regressors.gaussian_mlp_regressor module¶
A regressor based on a GaussianMLP model.
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class
GaussianMLPRegressor
(input_shape, output_dim, name='GaussianMLPRegressor', hidden_sizes=(32, 32), hidden_nonlinearity=<function tanh>, hidden_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, hidden_b_init=<tensorflow.python.ops.init_ops.Zeros object>, output_nonlinearity=None, output_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, output_b_init=<tensorflow.python.ops.init_ops.Zeros object>, 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)[source]¶ Bases:
garage.tf.regressors.base.StochasticRegressor
Fits data to a Gaussian whose parameters are estimated by an MLP.
Parameters: - input_shape (tuple[int]) – Input shape of the training data.
- output_dim (int) – Output dimension of the model.
- name (str) – Model name, also the variable scope.
- hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for mean. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
- hidden_nonlinearity (Callable) – Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation.
- hidden_w_init (Callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor.
- hidden_b_init (Callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor.
- output_nonlinearity (Callable) – Activation function for output dense layer. It should return a tf.Tensor. Set it to None to maintain a linear activation.
- output_w_init (Callable) – Initializer function for the weight of output dense layer(s). The function should return a tf.Tensor.
- output_b_init (Callable) – Initializer function for the bias of output dense layer(s). The function should return a tf.Tensor.
- optimizer (garage.tf.Optimizer) – Optimizer for minimizing the negative log-likelihood.
- optimizer_args (dict) – Arguments for the optimizer. Default is None, which means no arguments.
- use_trust_region (bool) – Whether to use trust region constraint.
- max_kl_step (float) – KL divergence constraint for each iteration.
- learn_std (bool) – Is std trainable.
- init_std (float) – Initial value for std.
- adaptive_std (bool) – Is std a neural network. If False, it will be a parameter.
- std_share_network (bool) – Boolean for whether mean and std share the same network.
- std_hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for std. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
- std_nonlinearity (Callable) – Nonlinearity for each hidden layer in the std network.
- layer_normalization (bool) – Bool for using layer normalization or not.
- normalize_inputs (bool) – Bool for normalizing inputs or not.
- normalize_outputs (bool) – Bool for normalizing outputs or not.
- subsample_factor (float) – The factor to subsample the data. By default it is 1.0, which means using all the data.
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dist_info_sym
(x_var, name=None)[source]¶ Create a symbolic graph of the distribution parameters.
Parameters: - x_var (tf.Tensor) – tf.Tensor of the input data.
- name (str) – Name of the new graph.
Returns: - Outputs of the symbolic distribution parameter
graph.
Return type: dict[tf.Tensor]
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fit
(xs, ys)[source]¶ Fit with input data xs and label ys.
Parameters: - xs (numpy.ndarray) – Input data.
- ys (numpy.ndarray) – Label of input data.
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log_likelihood_sym
(x_var, y_var, name=None)[source]¶ Create a symbolic graph of the log likelihood.
Parameters: - x_var (tf.Tensor) – Input tf.Tensor for the input data.
- y_var (tf.Tensor) – Input tf.Tensor for the label of data.
- name (str) – Name of the new graph.
Returns: Output of the symbolic log-likelihood graph.
Return type: tf.Tensor