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Mohd Sanad Zaki Rizvi mohdsanadzakirizvi

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View imagenette_map.py
imagenette_map = {
"n01440764" : "tench",
"n02102040" : "springer",
"n02979186" : "casette_player",
"n03000684" : "chain_saw",
"n03028079" : "church",
"n03394916" : "French_horn",
"n03417042" : "garbage_truck",
"n03425413" : "gas_pump",
"n03445777" : "golf_ball",
View inltk_embed.py
from inltk.inltk import get_embedding_vectors
# get embedding for input words
vectors = get_embedding_vectors("विश्लेषिकी विद्या", "hi")
print(vectors)
# print shape of the first word
print("shape:", vectors[0].shape)
View decl_independance.txt
The unanimous Declaration of the thirteen united States of America, When in the Course of human events, it becomes necessary for one people to dissolve the political bands which have connected them with another, and to assume among the powers of the earth, the separate and equal station to which the Laws of Nature and of Nature's God entitle them, a decent respect to the opinions of mankind requires that they should declare the causes which impel them to the separation.
We hold these truths to be self-evident, that all men are created equal, that they are endowed by their Creator with certain unalienable Rights, that among these are Life, Liberty and the pursuit of Happiness.--That to secure these rights, Governments are instituted among Men, deriving their just powers from the consent of the governed, --That whenever any Form of Government becomes destructive of these ends, it is the Right of the People to alter or to abolish it, and to institute new Government, laying its foundation on such principles and
View mnist_nn.py
# keras imports for the dataset and building our neural network
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Conv2D, MaxPool2D
from keras.utils import np_utils
# Flattening the images from the 28x28 pixels to 1D 787 pixels
X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
View cnn_base.py
from keras.models import Sequential
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, InputLayer, BatchNormalization, Dropout
# build a sequential model
model = Sequential()
model.add(InputLayer(input_shape=(224, 224, 3)))
# 1st conv block
model.add(Conv2D(25, (5, 5), activation='relu', strides=(1, 1), padding='same'))
model.add(MaxPool2D(pool_size=(2, 2), padding='same'))
View basic_regression.js
const callbacks = {
onEpochEnd: async (epoch, logs) => {
console.log("epoch: " + epoch + JSON.stringify(logs))
}
};
// Generate some synthetic data for training.
const xs = tf.tensor2d([[1], [2], [3], [4]], [4, 1]);
const ys = tf.tensor2d([[1], [3], [5], [7]], [4, 1]);
View datagen.py
from keras.preprocessing.image import ImageDataGenerator
# create a new generator
imagegen = ImageDataGenerator()
# load train data
train = imagegen.flow_from_directory("imagenette2/train/", class_mode="categorical", shuffle=False, batch_size=128, target_size=(224, 224))
# load val data
val = imagegen.flow_from_directory("imagenette2/val/", class_mode="categorical", shuffle=False, batch_size=128, target_size=(224, 224))
View bert_data.py
import pandas as pd
import numpy as np
# load training data
train = pd.read_csv('BERT_proj/train_E6oV3lV.csv', encoding='iso-8859-1')
train.shape
View lemma.py
import pandas as pd
def extract_lemma(doc):
parsed_text = {'word':[], 'lemma':[]}
for sent in doc.sentences:
for wrd in sent.words:
#extract text and lemma
parsed_text['word'].append(wrd.text)
parsed_text['lemma'].append(wrd.lemma)
#return a dataframe
View indic_stanfordnlp.py
#dictionary that contains pos tags and their explanations
pos_dict = {
'CC': 'coordinating conjunction','CD': 'cardinal digit','DT': 'determiner',
'EX': 'existential there (like: \"there is\" ... think of it like \"there exists\")',
'FW': 'foreign word','IN': 'preposition/subordinating conjunction','JJ': 'adjective \'big\'',
'JJR': 'adjective, comparative \'bigger\'','JJS': 'adjective, superlative \'biggest\'',
'LS': 'list marker 1)','MD': 'modal could, will','NN': 'noun, singular \'desk\'',
'NNS': 'noun plural \'desks\'','NNP': 'proper noun, singular \'Harrison\'',
'NNPS': 'proper noun, plural \'Americans\'','PDT': 'predeterminer \'all the kids\'',
'POS': 'possessive ending parent\'s','PRP': 'personal pronoun I, he, she',