suriyadeepan

from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import time


browser=webdriver.Chrome()

#first tab
browser.execute_script("window.open('about:blank', 'tab1');")
browser.switch_to_window("tab1")

suriyadeepan

{'b': {'args': (),
       'fn': Normal(loc: 0.0, scale: 1.0),
       'name': 'b',
       'type': 'sample',
       'value': tensor(-1.1897, grad_fn=<AddBackward0>)},
 'b_loc': {'args': (tensor(0.), Real()),
           'fn': <function param.<locals>.fn at 0x127388950>,
           'name': 'b_loc',
           'type': 'param',
           'value': tensor(0., requires_grad=True)},

suriyadeepan

# Install ipython3 on Mac OS

# check if brew exists
brew
# install brew if necessary
# follow instructions in
#     https://brew.sh/

# Update brew
brew update

suriyadeepan

def factorial(n):
  if n == 0:
    return 1

  return factorial(n - 1) * n


def factorial_cps(k, n):

  if n == 0:

suriyadeepan

# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python

suriyadeepan

Document Title

First sentence of document.

To create a line blank add a
and you will receive a line break!

underscore creates italics asterisks for bold

suriyadeepan

[0]

Question:
With a given tuple (1,2,3,4,5,6,7,8,9,10), write a program to print the first half values in one line and the last half values in one line.

Hints:

Use [n1:n2] notation to get a slice from a tuple.

[1]

suriyadeepan

import tensorflow as tf
import numpy as np


class LogisticRegressor(object):

    def __init__(self, num_attrs, num_labels, threshold=0.8, lr=0.01):

        self.attrs = tf.placeholder(tf.float32, [None, num_attrs], name='attrs')
        self.labels= tf.placeholder(tf.int32, [None, num_labels], name='labels')

suriyadeepan

import tensorflow as tf
import numpy as np

DropoutWrapper = tf.nn.rnn_cell.DropoutWrapper


class SentimentNetwork(object):

    
    def __init__(self, hdim=25, wdim=25, pdim=25, vocab_size=2000, pos_vocab_size=30, 

suriyadeepan

def apply_filter(x, filter_size, emb_dim, max_word_len):
    """
    Run convolution operation on x with "filter_size"d filter
    Args:
        x            : sequence to which conv should be applied 
        filter_size  : size of filter
        emb_dim      : embedding dimensions
        max_word_len : maximum length of a word
    Returns:
        Output of 1D convolution