Source code for garage.tf.models.normalized_input_mlp_model
"""NormalizedInputMLPModel."""
import numpy as np
import tensorflow as tf
from garage.tf.models import MLPModel
[docs]class NormalizedInputMLPModel(MLPModel):
"""
NormalizedInputMLPModel based on garage.tf.models.Model class.
This class normalized the inputs and pass the normalized input to a
MLP model, which can be used to perform linear regression to the outputs.
Args:
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.
layer_normalization (bool): Bool for using layer normalization or not.
"""
def __init__(self,
input_shape,
output_dim,
name='NormalizedInputMLPModel',
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.relu,
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(),
layer_normalization=False):
super().__init__(
output_dim=output_dim,
name=name,
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._input_shape = input_shape
[docs] def network_output_spec(self):
"""Network output spec."""
return ['y_hat', 'x_mean', 'x_std']
def _build(self, state_input, name=None):
with tf.compat.v1.variable_scope('normalized_vars'):
x_mean_var = tf.compat.v1.get_variable(
name='x_mean',
shape=(1, ) + self._input_shape,
dtype=np.float32,
initializer=tf.zeros_initializer(),
trainable=False)
x_std_var = tf.compat.v1.get_variable(
name='x_std_var',
shape=(1, ) + self._input_shape,
dtype=np.float32,
initializer=tf.ones_initializer(),
trainable=False)
normalized_xs_var = (state_input - x_mean_var) / x_std_var
y_hat = super()._build(normalized_xs_var)
return y_hat, x_mean_var, x_std_var