Daniel Plop dantp-ai

## 2d_convolution.py
import numpy as np

def convolve2D_same_output_shape(input_volume, kernel):

    kernel = np.flipud(np.fliplr(kernel))

    kernel_height = kernel.shape[0]
    kernel_width = kernel.shape[1]

    input_height = input_volume.shape[0]

## delete_all_local_brances_except_master_and_develop.sh
git branch | grep -v "master\|develop" | xargs git branch -D

## d5ae8e68696c00ad53ce60d984e8370d0c1e7b2e45df57530eb4f8596868fce1 ProtonVPN_mac_v2.1.0.dmg.txt.sha256.txt
d5ae8e68696c00ad53ce60d984e8370d0c1e7b2e45df57530eb4f8596868fce1 ProtonVPN_mac_v2.1.0.dmg

## example_google.py
# -*- coding: utf-8 -*-
"""Example Google style docstrings.

This module demonstrates documentation as specified by the `Google Python
Style Guide`_. Docstrings may extend over multiple lines. Sections are created
with a section header and a colon followed by a block of indented text.

Example:
    Examples can be given using either the ``Example`` or ``Examples``
    sections. Sections support any reStructuredText formatting, including

## simple_seed_test.py
import torch
import torch.nn as nn
from copy import deepcopy

def trial(seed):
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  torch.manual_seed(seed)
  input_size = 4
  output_size = 1
  step_size = 0.005
	import numpy as np

	def convolve2D_same_output_shape(input_volume, kernel):

	kernel = np.flipud(np.fliplr(kernel))

	kernel_height = kernel.shape[0]
	kernel_width = kernel.shape[1]

	input_height = input_volume.shape[0]
	# -- coding: utf-8 --
	"""Example Google style docstrings.

	This module demonstrates documentation as specified by the `Google Python
	Style Guide`_. Docstrings may extend over multiple lines. Sections are created
	with a section header and a colon followed by a block of indented text.

	Example:
	Examples can be given using either the ``Example`` or ``Examples``
	sections. Sections support any reStructuredText formatting, including
	import torch
	import torch.nn as nn
	from copy import deepcopy

	def trial(seed):
	device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
	torch.manual_seed(seed)
	input_size = 4
	output_size = 1
	step_size = 0.005