n0obcoder

help(model)

n0obcoder

model.state_dict()

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for name, param in model.named_parameters():
    print(name, ':', param.requires_grad)

n0obcoder

for name, param in model.named_parameters():
    if name in ['fc.weight', 'fc.bias']:
        param.requires_grad = True
    else:
        param.requires_grad = False

n0obcoder

for name, param in model.named_parameters():
    print('name: ', name)
    print(type(param))
    print('param.shape: ', param.shape)
    print('param.requires_grad: ', param.requires_grad)
    print('=====')

n0obcoder

model = NeuralNet()

print(model)

n0obcoder

import torch
import torch.nn as nn

n0obcoder

class NeuralNet(nn.Module):
    def __init__(self):
        super(NeuralNet, self).__init__()
        
        self.sequential = nn.Sequential(nn.Conv2d(1, 32, 5), 
                                        nn.Conv2d(32, 64, 5), 
                                        nn.Dropout(0.3))
        self.layer1 = nn.Conv2d(64, 128, 5)
        self.layer2 = nn.Conv2d(128, 256, 5)
        self.fc = nn.Linear(256*34*34, 128)

n0obcoder

iterations = 100
stop=[False]*k

for n in range(iterations):
    # iterate over the data points and assign them to one of the k clusters
    print('iteration number {}'.format(n))
    
    for i, (x, y) in enumerate(zip(x_data, y_data)):      
        cluster_index= assign_cluster(x, y, kcenters)  
        # updating the color_list for all the data points on the basis of the newly assigned clusters to them.

n0obcoder

def get_mean_shit_value(old_point, new_point):
    mean_shift_value = np.sqrt(  np.square(old_point[0] - new_point[0]) + np.square(old_point[1] - new_point[1])  )
    return mean_shift_value