garage.tf.regressors.continuous_mlp_regressor module¶
A regressor based on a MLP model.
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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.
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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) – Output labels.