garage.torch.modules.cnn_module
¶
CNN Module.
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class
CNNModule
(input_var, hidden_channels, kernel_sizes, strides=1, hidden_nonlinearity=nn.ReLU, 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, layer_normalization=False, n_layers=None, is_image=True)¶ Bases:
torch.nn.Module
Convolutional neural network (CNN) model in pytorch.
Parameters: - input_var (pytorch.tensor) – Input tensor of the model. Based on ‘NCHW’ data format: [batch_size, channel, height, width].
- 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_nonlinearity (callable or torch.nn.Module) – 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]) – Amount of zero-padding added to both sides of the input of a conv layer.
- padding_mode (str) – The type of padding algorithm to use, i.e. ‘constant’, ‘reflect’, ‘replicate’ or ‘circular’ and by default is ‘zeros’.
- 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 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).
- layer_normalization (bool) – Bool for using layer normalization or not.
- n_layers (int) – number of convolutional layer.
- is_image (bool) – Whether observations are images or not.
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forward
(self, input_val)¶ Forward method.
Parameters: input_val (torch.Tensor) – Input values with (N, C, H, W) shape. Returns: Output values Return type: List[torch.Tensor]