First sentence of document.
To create a line blank add a
and you will receive a line break!
underscore creates italics asterisks for bold
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") |
{'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)}, |
# Install ipython3 on Mac OS | |
# check if brew exists | |
brew | |
# install brew if necessary | |
# follow instructions in | |
# https://brew.sh/ | |
# Update brew | |
brew update |
def factorial(n): | |
if n == 0: | |
return 1 | |
return factorial(n - 1) * n | |
def factorial_cps(k, n): | |
if n == 0: |
# Byte-compiled / optimized / DLL files | |
__pycache__/ | |
*.py[cod] | |
*$py.class | |
# C extensions | |
*.so | |
# Distribution / packaging | |
.Python |
[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] |
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') |
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, |
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 |