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model2 = Sequential()
model2.add(Flatten(input_shape=(7,7,512)))
model2.add(Dense(100, activation='relu'))
model2.add(Dropout(0.5))
model2.add(BatchNormalization())
model2.add(Dense(10, activation='softmax'))

# compile the model
model2.compile(optimizer='adam', metrics=['accuracy'], loss='categorical_crossentropy')

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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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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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

# keras imports for the dataset and building our neural network
from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers import Dense, Dropout, Conv2D, MaxPool2D, Flatten
from keras.utils import np_utils

# loading the dataset
(X_train, y_train), (X_test, y_test) = cifar10.load_data()

# # building the input vector from the 32x32 pixels

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, Flatten
from keras.utils import np_utils

# to calculate accuracy
from sklearn.metrics import accuracy_score

# loading the dataset

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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import re
import nltk
nltk.download('stopwords')

# download stopwords list from nltk
from nltk.corpus import stopwords

stop_words = set(stopwords.words('english'))

def clean_text(text):

mohdsanadzakirizvi

import re

re.findall(r"(\d{4})-(\d{2})-(\d{2})", date)

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html = """<table class="vertical-navbox nowraplinks" style="float:right;clear:right;width:22.0em;margin:0 0 1.0em 1.0em;background:#f9f9f9;border:1px solid #aaa;padding:0.2em;border-spacing:0.4em 0;text-align:center;line-height:1.4em;font-size:88%"><tbody><tr><th style="padding:0.2em 0.4em 0.2em;font-size:145%;line-height:1.2em"><a href="/wiki/Machine_learning" title="Machine learning">Machine learning</a> and<br /><a href="/wiki/Data_mining" title="Data mining">data mining</a></th></tr><tr><td style="padding:0.2em 0 0.4em;padding:0.25em 0.25em 0.75em;"><a href="/wiki/File:Kernel_Machine.svg" class="image"><img alt="Kernel Machine.svg" src="//upload.wikimedia.org/wikipedia/commons/thumb/f/fe/Kernel_Machine.svg/220px-Kernel_Machine.svg.png" decoding="async" width="220" height="100" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/f/fe/Kernel_Machine.svg/330px-Kernel_Machine.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/f/fe/Kernel_Machine.svg/440px-Kernel_Machine.svg.png 2x" data-file-widt