Konstantin Verner konverner

## convert labels into onehot format
def encode_onehot(labels: list):
    labels = np.array(labels)
    shape = (labels.size, labels.max()+1)
    one_hot = np.zeros(shape)
    rows = np.arange(labels.size)
    one_hot[rows, labels] = 1
    return one_hot

## from_kaggle_notebook.py
from IPython.display import FileLink
FileLink(PATH)

## show_img_grid.py
def show_img_grid(images, labels, N):
    n = int(N**(0.5))
    k = 0
    f, axarr = plt.subplots(n,n,figsize=(10,10))
    for i in range(n):
        for j in range(n):
            axarr[i,j].set_title(labels[k])
            axarr[i,j].imshow(images[k])
            k += 1

## count_parameters.py
def count_parameters(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)

## image.md

      
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                konverner
                / image.md
            
            
              Created
              May 17, 2022 18:35
            
              
                insert and resize image in google colab
              
          
## latin.txt
ABCDEFGHIJKLMNOPQRSTUVWXYZfabcdefghijklmnopqrstuvwxyz

## 3d_scatter.py
fig = plt.figure(figsize=(4,4))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(df['Gene 1'], df['Gene 2'], df['Gene 3'])

## subplots.py
fig, axs = plt.subplots(1, 2)
fig.set_figheight(4)
fig.set_figwidth(13)
axs[0].scatter(X1, Y1)
axs[1].scatter(X2, Y2)

axs[0].set_title("North")
axs[0].set_ylabel('mortality')
axs[0].set_xlabel('hardness')

## random_seed.py
seed = 42
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)

## dataloader.py
class DataLoader:
    def __init__(self, dir):
        self.dir = dir
        self.filenames = [filename for filename in os.listdir(dir)]
        self.index = -1
        self.size = len(self.filenames)

    def __iter__(self):
        return self
	def encode_onehot(labels: list):
	labels = np.array(labels)
	shape = (labels.size, labels.max()+1)
	one_hot = np.zeros(shape)
	rows = np.arange(labels.size)
	one_hot[rows, labels] = 1
	return one_hot
	def show_img_grid(images, labels, N):
	n = int(N**(0.5))
	k = 0
	f, axarr = plt.subplots(n,n,figsize=(10,10))
	for i in range(n):
	for j in range(n):
	axarr[i,j].set_title(labels[k])
	axarr[i,j].imshow(images[k])
	k += 1
	def count_parameters(model):
	return sum(p.numel() for p in model.parameters() if p.requires_grad)
	fig = plt.figure(figsize=(4,4))
	ax = fig.add_subplot(111, projection='3d')
	ax.scatter(df['Gene 1'], df['Gene 2'], df['Gene 3'])
	fig, axs = plt.subplots(1, 2)
	fig.set_figheight(4)
	fig.set_figwidth(13)
	axs[0].scatter(X1, Y1)
	axs[1].scatter(X2, Y2)

	axs[0].set_title("North")
	axs[0].set_ylabel('mortality')
	axs[0].set_xlabel('hardness')
	seed = 42
	torch.manual_seed(seed)
	torch.cuda.manual_seed(seed)
	np.random.seed(seed)
	random.seed(seed)
	class DataLoader:
	def __init__(self, dir):
	self.dir = dir
	self.filenames = [filename for filename in os.listdir(dir)]
	self.index = -1
	self.size = len(self.filenames)

	def __iter__(self):
	return self