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Vihar Kurama vihar

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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)
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
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)
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
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
# Load the libraries
import torch
import numpy as np
from torchvision import datasets
import torchvision.transforms as transforms
# Set the parameters
num_workers = 0
batch_size = 20

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  • Write a code snippet about how this works in JavaScript and explain it.

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  • Write a code snippet which takes in an E-mail from the user and validates it using a regular expression. The Email should start with the letter c and end with It can contain three numbers at the end. The email should not exceed ten letters.

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Caravel.AI Intern Interview 2018

  • Write a code snippet about how this works in JavaScript and explain it. Your code should be restricted to 5 or 6 lines.

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  • What is JSON? Explain in detail, and write a small snippet in Python to generate a JSON type object for given string and explain the process.

import gym
import numpy as np
import random
from IPython.display import clear_output
# Init Taxi-V2 Env
env = gym.make("Taxi-v2").env
# Init arbitary values
q_table = np.zeros([env.observation_space.n, env.action_space.n])
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