Conv2D
2-dimensional convolutional layer. Performs a convolution operation on input data.
Learn more about convolutions (opens in a new tab)
Usage
from tinygrad.nn import Conv2d
BS, C1, H, W = 4, 16, 224, 224
C2, K, S, P = 64, 7, 2, 1
# create in tinygrad
layer = Conv2d(C1, C2, K, S, P)Arguments
in_chnnaels
The number of channels in the input image.
out_channels
The number of channels produced by the convolution.
kernel_size
The size of the convolving kernel.
stride (default=1)
The stride of the convolution.
padding (default=0)
The zero-padding added to both sides of the input.
dilation (default=1)
The spacing between kernel elements.
groups (default=1)
The number of blocked connections from input channels to output channels.
bias (default=True)
The bias term.