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Other activations ignore this parameter.īeta – float The beta parameter that is used by some parametric activations (SELU, SOFTPLUS, CLIP, HARD_SIGMOID, SCALED_TANH). Type – ActivationType The type of activation to be performed.Īlpha – float The alpha parameter that is used by some parametric activations (LEAKY_RELU, ELU, SELU, SOFTPLUS, CLIP, HARD_SIGMOID, SCALED_TANH). The output has the same shape as the input. This layer applies a per-element activation function to its input. IActivationLayer ¶Īn Activation layer in an INetworkDefinition. LEAKY_RELU : Leaky Relu activation: f(x) = x if x >= 0, f(x) = alpha * x if x = 0, f(x) = alpha * (exp(x) - 1) if x 0, f(x) = beta * (alpha * exp(x) - alpha) if x alpha, f(x) = 0 if x <= alpha class tensorrt. To omit bias, set this to an empty Weights object. Kernel – Weights The kernel weights, given as a KxC matrix in row-major order.īias – Weights The bias weights. Num_output_channels – int The number of output channels K from the fully connected layer. The input is automatically reshaped into an MxV tensor X, where V is a product of the last three dimensions and M is a product of the remaining dimensions (where the product over 0 dimensions is defined as 1). This layer expects an input tensor of three or more non-batch dimensions. IFull圜onnectedLayer ¶Ī fully connected layer in an INetworkDefinition. Default: (0, …, 0)ĭilation_nd – Dims The multi-dimension dilation for the convolution. If the padding is asymmetric, this value corresponds to the pre-padding. The input will be zero-padded by this number of elements in each dimension. Padding_nd – Dims The multi-dimension padding of the convolution. Stride_nd – Dims The multi-dimension stride of the convolution. Kernel_size_nd – Dims The multi-dimension kernel size of the convolution. The bias is applied per-channel, so the number of weights (if non-zero) must be equal to the number of output feature maps.ĭilation – DimsHW The dilation for a convolution. The weights are specified as a contiguous array in GKCRS order, where G is the number of groups, K the number of output feature maps, C the number of input channels, and R and S are the height and width of the filter.īias – Weights The bias weights for the convolution. Kernel – Weights The kernel weights for the convolution. the channel count divided by the group count) must be a multiple of 4 for both input and output. Note When using groups in int8 mode, the size of the groups (i.e. The results of the group convolutions are concatenated to form the output. The input tensor channels are divided into this many groups, and a convolution is executed for each group, using a filter per group. Num_groups – int The number of groups for a convolution. Padding mode takes precedence if both IConvolutionLayer.padding_mode and either IConvolutionLayer.pre_padding or IConvolutionLayer.post_padding are set. Padding_mode – PaddingMode The padding mode. The end of input will be zero-padded by this number of elements in the height and width directions. The start of input will be zero-padded by this number of elements in the height and width directions. The input will be zero-padded by this number of elements in the height and width directions. Padding – DimsHW The padding of the convolution. Stride – DimsHW The stride of the convolution. Num_output_maps – int The number of output maps for the convolution. Kernel_size – DimsHW The HW kernel size of the convolution. This layer performs a correlation operation between 3-dimensional filter with a 4-dimensional tensor to produce another 4-dimensional tensor.Īn optional bias argument is supported, which adds a per-channel constant to each value in the output. IConvolutionLayer ¶Ī convolution layer in an INetworkDefinition.
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