vihar

import torch
import torch.nn as nn
import torch.nn.functional as F
    

class Net(nn.Module):
    
    def __init__(self, weight):
        super(Net, self).__init__()
        # initializes the weights of the convolutional layer to be the weights of the 4 defined filters

vihar

import numpy as np

print("Enter the two values for input layers")

print('a = ')
a = int(input())
# 2
print('b = ')
b = int(input())
# 3

vihar

import gym
from time import sleep

# Creating thr env
env = gym.make("Taxi-v2").env

env.s = 328


# Setting the number of iterations, penalties and reward to zero,

vihar

# Importing Modules
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
from sklearn.cluster import DBSCAN
from sklearn.decomposition import PCA

# Load Dataset
iris = load_iris()

# Declaring Model

vihar

# Importing Modules
from sklearn import datasets
import matplotlib.pyplot as plt

# Loading dataset
iris_df = datasets.load_iris()

# Available methods on dataset
print(dir(iris_df))

vihar

import cv2
import matplotlib.pyplot as plt
%matplotlib inline

img_path = 'dog.jpg'

bgr_img = cv2.imread(img_path)
gray_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2GRAY)

# Normalise

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viz_layer(activated_layer)
viz_layer(pooled_layer)

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def viz_layer(layer, n_filters= 4):
    fig = plt.figure(figsize=(20, 20))
    
    for i in range(n_filters):
        ax = fig.add_subplot(1, n_filters, i+1)
        ax.imshow(np.squeeze(layer[0,i].data.numpy()), cmap='gray')
        ax.set_title('Output %s' % str(i+1))

fig = plt.figure(figsize=(12, 6))
fig.subplots_adjust(left=0, right=1.5, bottom=0.8, top=1, hspace=0.05, wspace=0.05)

vihar

import numpy as np

filter_vals = np.array([
  [-1, -1, 1, 1],
  [-1, -1, 1, 1],
  [-1, -1, 1, 1],
  [-1, -1, 1, 1]
])

print('Filter shape: ', filter_vals.shape)

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img = np.squeeze(images[7])

fig = plt.figure(figsize = (12,12)) 
ax = fig.add_subplot(111)
ax.imshow(img, cmap='gray')
width, height = img.shape
thresh = img.max()/2.5
for x in range(width):
    for y in range(height):
        val = round(img[x][y],2) if img[x][y] !=0 else 0