e build a single 2D convolution layer, with specific input and output dimensions, no bias nodes and no activation. We start with an input of 5x5 pixels and one channel, we apply no padding, a stride of 1, and a kernel size of 2x2, and our output has one channel. How many nodes in total does the output of the layer have? We start with an input of 5x5 pixels and one channel, we apply a single pixel of padding around the input, use a stride of 1, and a kernel size of 3x3, and our output has one channel. How many nodes in total does the output of the layer have? We start with an input of 5x5 pixels and 3 channels, we a
We build a single 2D convolution layer, with specific input and output dimensions, no bias nodes and no activation.
We start with an input of 5x5 pixels and one channel, we apply no padding, a stride of 1, and a kernel size of 2x2, and our output has one channel. How many nodes in total does the output of the layer have?
We start with an input of 5x5 pixels and one channel, we apply a single pixel of padding around the input, use a stride of 1, and a kernel size of 3x3, and our output has one channel. How many nodes in total does the output of the layer have?
We start with an input of 5x5 pixels and 3 channels, we apply a single pixel of padding around the input, use a stride of 1, and a kernel size of 3x3, and our output has one channel. How many distinct weights does our layer have?
We start with an input of 5x5 pixels and 3 channels, we apply two pixels of padding around the input, use a stride of 1, and a kernel size of 3x3, and our output has 3 channels. How many distinct weights does our layer have?
We start with an input with 3 channels, we apply no padding around the input, use a stride of 1, and a kernel size of 3x3, and our output has 3x3 pixels and 3 channels. How many pixels does our input have?
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