mohdsanadzakirizvi

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

mohdsanadzakirizvi

from indicnlp.transliterate.unicode_transliterate import ItransTransliterator

input_text='आज मौसम अच्छा है। इसलिए हम आज खेल सकते हैं!'

# Transliterate Hindi to Roman
print(ItransTransliterator.to_itrans(input_text, 'hi'))

mohdsanadzakirizvi

# generate a sequence of characters with a language model
def generate_seq(model, mapping, seq_length, seed_text, n_chars):
	in_text = seed_text
	# generate a fixed number of characters
	for _ in range(n_chars):
		# encode the characters as integers
		encoded = [mapping[char] for char in in_text]
		# truncate sequences to a fixed length
		encoded = pad_sequences([encoded], maxlen=seq_length, truncating='pre')
		# predict character

mohdsanadzakirizvi

from indicnlp.syllable import  syllabifier

# Word to be broken into syllables
w='जगदीशचंद्र'
# Language code Hindi in this case 
lang='hi'

# Break into syllables
print(' '.join(syllabifier.orthographic_syllabify(w,lang)))

mohdsanadzakirizvi

from inltk.inltk import get_similar_sentences

# get similar sentences to the one given in hindi
output = get_similar_sentences('मैं आज बहुत खुश हूं', 5, 'hi')

print(output)

mohdsanadzakirizvi

from inltk.inltk import tokenize

hindi_text = """प्राचीन काल में विक्रमादित्य नाम के एक आदर्श राजा हुआ करते थे।
अपने साहस, पराक्रम और शौर्य के लिए  राजा विक्रम मशहूर थे। 
ऐसा भी कहा जाता है कि राजा विक्रम अपनी प्राजा के जीवन के दुख दर्द जानने के लिए रात्री के पहर में भेष बदल कर नगर में घूमते थे।"""

# tokenize(input text, language code)
tokenize(hindi_text, "hi")

mohdsanadzakirizvi

function draw() {
  image(video, 0, 0, width, height);

  // We can call both functions to draw all keypoints and the skeletons
  drawKeypoints();
  drawSkeleton();
}

mohdsanadzakirizvi

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

# 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