piyush2896

import pandas as pd
import numpy as np

def get_train_data():
    df = pd.read_csv('datasets/all_stocks_5yr.csv')
    data_aal = df[df['Name'] == 'AAL']
    X_train = data_aal[data_aal.columns[1:5]].values[:-1]
    Y_train = np.expand_dims(data_aal['open'].values[1:], 1)
    return X_train, Y_train

piyush2896

li = []

for i in range(10):
    n = int(input('Enter a no: '))
    li.append(n)

max_val = li[0]
for ele in li:
    if ele >  max_val:
        max_val = ele

piyush2896

def get_grade(marks):
    if marks >= 80:
        return 'A'
    elif marks >= 65:
        return 'B'
    elif marks >= 50:
        return 'C'
    elif marks >= 35:
        return 'D'
    return 'E'

piyush2896

name = input('Enter Student Name: ')
roll_no = int(input('Enter Roll No: '))

subjects_list = ['Maths',
                 'Physics',
                 'Chemistry',
                 'English',
                 'Python']

print('\n' + '-' * 10 + 'GRADING CRITERIA' + '-' * 10)

piyush2896

no = int(input('Enter a positive number: '))

if no % 2 == 0:
    print(no, 'is even.')
else:
    print(no, 'is odd')

piyush2896

a = 10
b = 5

print(a + b)
print(a - b)
print(a * b)
print(a / b)
print(a // b)
print(a % b)

piyush2896

piyush2896

import tensorflow as tf
from pprint import pprint


def get_weights(shape, name):
    return tf.get_variable(name, shape=shape)


def get_bias(shape, name):
    return tf.zeros(shape=shape, name=name)

piyush2896

import tensorflow as tf
import pprint


def vgg16(is_input_trainable=False, fine_tune_last=False,
          n_classes=1000, input_shape=[None, 224, 224, 3]):
    """
    @params:
    is_input_trainable: True in case of Neural Style Transfer to generate images
    fine_tune_last: Make True if you want to fine tune the model for transfer learning