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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 

mohdsanadzakirizvi

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'))

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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",

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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)

mohdsanadzakirizvi

# 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)

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    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]);

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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))

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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

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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

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#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',