6.4. Multiple Input and Multiple Output Channels — Dive into Deep Learning 0.17.5 documentation

While we have described the multiple channels that comprise each image (e.g., color images have the standard RGB channels to indicate the amount of red, green and blue) and convolutional layers for multiple channels in Section 6.1.4.1, until now, we simplified all of our numerical examples by working with just a single input and a single output channel. This has allowed us to think of our inputs, convolution kernels, and outputs each as two-dimensional tensors.

7.4. Multiple Input and Multiple Output Channels — Dive into Deep Learning 1.0.3 documentation 7.4. Multiple Input and Multiple Output Channels ¶ Colab [pytorch] Open the notebook in Colab Colab [mxnet] Open the notebook in Colab Colab [jax] Open the notebook in Colab Colab [tensorflow] Open the notebook in Colab SageMaker Studio Lab Open the notebook in SageMaker Studio Lab While we described the multiple channels that comprise each image (e.g., color images have the standard RGB channels to indicate the amount of red, green and blue) and convolutional layers for multiple channels in Se

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