kenorb

## Unity ##

*.cs diff=csharp text
*.cginc text
*.shader text

*.mat merge=unityyamlmerge eol=lf
*.anim merge=unityyamlmerge eol=lf
*.unity merge=unityyamlmerge eol=lf
*.prefab merge=unityyamlmerge eol=lf

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function trigger(el, eventType) {
  if (typeof eventType === 'string' && typeof el[eventType] === 'function') {
    el[eventType]();
  } else {
    const event =
      eventType === 'string'
        ? new Event(eventType, {bubbles: true})
        : eventType;
    el.dispatchEvent(event);
  }

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alizarin
amaranth
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aquamarine
asparagus
auburn
azure

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alizarin
amaranth
amber
amethyst
apricot
aqua
aquamarine
asparagus
auburn
azure

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==> auth.log <==
Dec 18 13:37:48 Ubuntu-ROG polkitd(authority=local): Unregistered Authentication Agent for unix-session:c14 (system bus name :1.628, object path /org/freedesktop/PolicyKit1/AuthenticationAgent, locale en_GB.UTF-8) (disconnected from bus)

==> gpu-manager-switch.log <==

==> kern.log <==
Dec 18 13:37:48 Ubuntu-ROG kernel: [ 7673.827587] [drm:nv_drm_master_set [nvidia_drm]] *ERROR* [nvidia-drm] [GPU ID 0x0000b400] Failed to grab modeset ownership
Dec 18 13:37:48 Ubuntu-ROG kernel: [ 7673.827750] [drm:nv_drm_master_set [nvidia_drm]] *ERROR* [nvidia-drm] [GPU ID 0x00006500] Failed to grab modeset ownership
Dec 18 13:37:48 Ubuntu-ROG kernel: [ 7673.827848] [drm:nv_drm_master_set [nvidia_drm]] *ERROR* [nvidia-drm] [GPU ID 0x00001800] Failed to grab modeset ownership
Dec 18 13:37:48 Ubuntu-ROG kernel: [ 7673.827940] [drm:nv_drm_master_set [nvidia_drm]] *ERROR* [nvidia-drm] [GPU ID 0x00001700] Failed to grab modeset ownership

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# Show Tensor Images utility function

from torchvision.utils import make_grid
import matplotlib.pyplot as plt

def show_tensor_images(image_tensor, num_images=25, size=(1, 28, 28)):
    '''
    Function for visualizing images: Given a tensor of images, number of images, and
    size per image, plots and prints the images in a uniform grid.
    '''

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def conv_backward(dH, cache):
    '''
    The backward computation for a convolution function
    
    Arguments:
    dH -- gradient of the cost with respect to output of the conv layer (H), numpy array of shape (n_H, n_W) assuming channels = 1
    cache -- cache of values needed for the conv_backward(), output of conv_forward()
    
    Returns:
    dX -- gradient of the cost with respect to input of the conv layer (X), numpy array of shape (n_H_prev, n_W_prev) assuming channels = 1

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def conv_forward(X, W):
    '''
    The forward computation for a convolution function
    
    Arguments:
    X -- output activations of the previous layer, numpy array of shape (n_H_prev, n_W_prev) assuming input channels = 1
    W -- Weights, numpy array of size (f, f) assuming number of filters = 1
    
    Returns:
    H -- conv output, numpy array of size (n_H, n_W)

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def conv_forward(X, W):
    '''
    The forward computation for a convolution function
    
    Arguments:
    X -- output activations of the previous layer, numpy array of shape (n_H_prev, n_W_prev) assuming input channels = 1
    W -- Weights, numpy array of size (f, f) assuming number of filters = 1
    
    Returns:
    H -- conv output, numpy array of size (n_H, n_W)