garage.torch.modules.categorical_cnn_module
¶
Categorical CNN Module.
A model represented by a categorical distribution which is parameterized by a convolutional neural network (CNN) followed a multilayer perceptron (MLP).
-
class
CategoricalCNNModule
(input_var, output_dim, kernel_sizes, hidden_channels, strides=1, hidden_sizes=(32, 32), hidden_nonlinearity=torch.tanh, hidden_w_init=nn.init.xavier_uniform_, hidden_b_init=nn.init.zeros_, paddings=0, padding_mode='zeros', max_pool=False, pool_shape=None, pool_stride=1, output_nonlinearity=None, output_w_init=nn.init.xavier_uniform_, output_b_init=nn.init.zeros_, layer_normalization=False, is_image=True)¶ Bases:
torch.nn.Module
Categorical CNN Model.
A model represented by a Categorical distribution which is parameterized by a convolutional neural network (CNN) followed by a fully-connected layer.
Parameters: - input_var (torch.tensor) – Input tensor of the model.
- output_dim (int) – Output dimension of the model.
- kernel_sizes (tuple[int]) – Dimension of the conv 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).
- 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.
- hidden_channels (tuple[int]) – Number of output channels for CNN. For example, (3, 32) means there are two convolutional layers. The filter for the first conv layer outputs 3 channels and the second one outputs 32 channels.
- 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 torch.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 torch.Tensor.
- hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a torch.Tensor.
- paddings (tuple[int]) – Zero-padding added to both sides of the input
- padding_mode (str) – The type of padding algorithm to use, either ‘SAME’ or ‘VALID’.
- max_pool (bool) – Bool for using max-pooling or not.
- pool_shape (tuple[int]) – Dimension of the pooling layer(s). For example, (2, 2) means that all the pooling layers are of the same shape (2, 2).
- pool_stride (tuple[int]) – The strides of the pooling layer(s). For example, (2, 2) means that all the pooling layers have strides (2, 2).
- output_nonlinearity (callable) – Activation function for output dense layer. It should return a torch.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 torch.Tensor.
- output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a torch.Tensor.
- layer_normalization (bool) – Bool for using layer normalization or not.
- is_image (bool) – Whether observations are images or not.
-
forward
(self, *inputs)¶ Forward method.
Parameters: *inputs – Input to the module. Returns: Policy distribution. Return type: torch.distributions.Categorical