garage.tf.regressors.categorical_mlp_regressor module¶
A regressor based on MLP with Normalized Inputs.
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class
CategoricalMLPRegressor(input_shape, output_dim, name='CategoricalMLPRegressor', hidden_sizes=(32, 32), hidden_nonlinearity=<function tanh>, hidden_w_init=<tensorflow.python.ops.init_ops_v2.GlorotUniform object>, hidden_b_init=<tensorflow.python.ops.init_ops_v2.Zeros object>, output_nonlinearity=<function softmax_v2>, output_w_init=<tensorflow.python.ops.init_ops_v2.GlorotUniform object>, output_b_init=<tensorflow.python.ops.init_ops_v2.Zeros object>, 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)[source]¶ Bases:
garage.tf.regressors.regressor.StochasticRegressorFits data to a Categorical with parameters are the output of an MLP.
A class for performing regression (or classification, really) by fitting a Categorical distribution to the outputs. Assumes that the output will always be a one hot vector
Parameters: - input_shape (tuple[int]) – Input shape of the training data. Since an MLP model is used, implementation assumes flattened inputs. The input shape of each data point should thus be of shape (x, ).
- 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 the network. 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 tanh activation.
- hidden_w_init (Callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor. Default is Glorot uniform initializer.
- hidden_b_init (Callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor. Default is zero initializer.
- output_nonlinearity (Callable) – Activation function for output dense layer. It should return a tf.Tensor. Set it to None to maintain a softmax activation.
- output_w_init (Callable) – Initializer function for the weight of output dense layer(s). The function should return a tf.Tensor. Default is Glorot uniform initializer.
- output_b_init (Callable) – Initializer function for the bias of output dense layer(s). The function should return a tf.Tensor. Default is zero initializer.
- optimizer (garage.tf.Optimizer) – Optimizer for minimizing the negative log-likelihood. Defaults to LbsgsOptimizer
- optimizer_args (dict) – Arguments for the optimizer. Default is None, which means no arguments.
- tr_optimizer (garage.tf.Optimizer) – Optimizer for trust region approximation. Defaults to ConjugateGradientOptimizer.
- tr_optimizer_args (dict) – Arguments for the trust region 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.
- normalize_inputs (bool) – Bool for normalizing inputs or not.
- layer_normalization (bool) – Bool for using layer normalization or not.
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distribution¶ Distribution.
Type: garage.tf.distributions.DiagonalGaussian
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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.