"""CNN Model."""
import tensorflow as tf
from garage.tf.models.base import Model
from garage.tf.models.cnn import cnn_with_max_pooling
[docs]class CNNModelWithMaxPooling(Model):
"""CNN Model with max pooling.
Args:
filter_dims (tuple[int]): Dimension of the filters. For example,
(3, 5) means there are two convolutional layers. The filter for
first layer is of dimension (3 x 3) and the second one is of
dimension (5 x 5).
num_filters (tuple[int]): Number of filters. For example, (3, 32) means
there are two convolutional layers. The filter for the first layer
has 3 channels and the second one with 32 channels.
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,
filter_dims,
num_filters,
strides,
name=None,
padding='SAME',
pool_strides=(2, 2),
pool_shapes=(2, 2),
hidden_nonlinearity=tf.nn.relu,
hidden_w_init=tf.glorot_uniform_initializer(),
hidden_b_init=tf.zeros_initializer()):
super().__init__(name)
self._filter_dims = filter_dims
self._num_filters = num_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
def _build(self, state_input, name=None):
return cnn_with_max_pooling(
input_var=state_input,
filter_dims=self._filter_dims,
hidden_nonlinearity=self._hidden_nonlinearity,
hidden_w_init=self._hidden_w_init,
hidden_b_init=self._hidden_b_init,
num_filters=self._num_filters,
strides=self._strides,
padding=self._padding,
pool_shapes=self._pool_shapes,
pool_strides=self._pool_strides,
name='cnn')