garage.tf.regressors.continuous_mlp_regressor module

A regressor based on a MLP model.

class ContinuousMLPRegressor(input_shape, output_dim, name='ContinuousMLPRegressor', 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, normalize_inputs=True)[source]

Bases: garage.tf.regressors.base.Regressor

Fits continuously-valued data to an MLP model.

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.
  • normalize_inputs (bool) – Bool for normalizing inputs or not.
fit(xs, ys)[source]

Fit with input data xs and label ys.

Parameters:
  • xs (numpy.ndarray) – Input data.
  • ys (numpy.ndarray) – Output labels.
get_params_internal(**args)[source]

Get the params, which are the trainable variables.

predict(xs)[source]

Predict y based on input xs.

Parameters:xs (numpy.ndarray) – Input data.
Returns:The predicted ys.
Return type:numpy.ndarray
predict_sym(xs, name=None)[source]

Build a symbolic graph of the model prediction.

Parameters:
  • xs (tf.Tensor) – Input tf.Tensor for the input data.
  • name (str) – Name of the new graph.
Returns:

Output of the symbolic prediction graph.

Return type:

tf.Tensor