"""CNN Model."""
import tensorflow as tf
from garage.tf.models.cnn import cnn_with_max_pooling
from garage.tf.models.model import Model
[docs]class CNNModelWithMaxPooling(Model):
"""CNN Model with max pooling.
Args:
filters (Tuple[Tuple[int, Tuple[int, int]], ...]): Number and dimension
of filters. For example, ((3, (3, 5)), (32, (3, 3))) means there
are two convolutional layers. The filter for the first layer have 3
channels and its shape is (3 x 5), while the filter for the second
layer have 32 channels and its shape is (3 x 3).
strides (tuple[int]): The stride of the sliding window. For example,
(1, 2) means there are two convolutional layers. The stride of the
filter for first layer is 1 and that of the second layer is 2.
name (str): Model name, also the variable scope of the cnn.
padding (str): The type of padding algorithm to use,
either 'SAME' or 'VALID'.
pool_strides (tuple[int]): The strides of the pooling layer(s). For
example, (2, 2) means that all the pooling layers have
strides (2, 2).
pool_shapes (tuple[int]): Dimension of the pooling layer(s). For
example, (2, 2) means that all the pooling layers have
shape (2, 2).
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.
"""
def __init__(self,
filters,
strides,
name=None,
padding='SAME',
pool_strides=(2, 2),
pool_shapes=(2, 2),
hidden_nonlinearity=tf.nn.relu,
hidden_w_init=tf.initializers.glorot_uniform(),
hidden_b_init=tf.zeros_initializer()):
super().__init__(name)
self._filters = filters
self._strides = strides
self._padding = padding
self._pool_strides = pool_strides
self._pool_shapes = pool_shapes
self._hidden_nonlinearity = hidden_nonlinearity
self._hidden_w_init = hidden_w_init
self._hidden_b_init = hidden_b_init
# pylint: disable=arguments-differ
def _build(self, state_input, name=None):
"""Build model given input placeholder(s).
Args:
state_input (tf.Tensor): Tensor input for state.
name (str): Inner model name, also the variable scope of the
inner model, if exist. One example is
garage.tf.models.Sequential.
Return:
tf.Tensor: Tensor output of the model.
"""
del name
return cnn_with_max_pooling(
input_var=state_input,
filters=self._filters,
hidden_nonlinearity=self._hidden_nonlinearity,
hidden_w_init=self._hidden_w_init,
hidden_b_init=self._hidden_b_init,
strides=self._strides,
padding=self._padding,
pool_shapes=self._pool_shapes,
pool_strides=self._pool_strides,
name='cnn')