mohdsanadzakirizvi

license: gpl-3.0
height: 700
width: 960
scrolling: no
border: no

mohdsanadzakirizvi

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'))

mohdsanadzakirizvi

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",

mohdsanadzakirizvi

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)

mohdsanadzakirizvi

    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]);

mohdsanadzakirizvi

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))

mohdsanadzakirizvi

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

mohdsanadzakirizvi

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