sameerg07

import os
import sys
import glob
import argparse
import matplotlib.pyplot as plt
from keras import __version__
from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras.preprocessing.image import ImageDataGenerator

sameerg07

import sys
import argparse
import numpy as np
from PIL import Image
import requests
from io import BytesIO
import matplotlib.pyplot as plt
from PIL import Image,ImageDraw,ImageFont
from keras.preprocessing import image
from keras.models import load_model

sameerg07

model = Sequential()
model.add(Dense(32, activation='relu', input_dim=100))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])

# Generate dummy data
import numpy as np
data = np.random.random((1000, 100))

sameerg07

# Create the graph, etc.
init_op = tf.global_variables_initializer()
# Create a session for running operations in the Graph.
sess = tf.Session()
# Initialize the variables (like the epoch counter).
sess.run(init_op)
# Start input enqueue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:

sameerg07

#This script has been solely created under dataturks. Copyrights are reserved

#EXAMPLE USAGE
#python3 tensorflow_json_parser.py --json_file "flower.json" --dataset_path "Dataset5/" 



import json 
import glob
import urllib.request

sameerg07

#This script has been solely created under dataturks. Copyrights are reserved

#EXAMPLE USAGE
#python3 keras_json_parser.py --json_file "flower.json" --dataset_path "Dataset5/" --train_percentage 80 --validation_percentage 20


import json 
import glob
import urllib.request
import argparse

sameerg07

from keras.preprocessing.image import ImageDataGenerator,img_to_array, load_img

datagen = ImageDataGenerator(
        rotation_range=40,
        width_shift_range=0.2,
        height_shift_range=0.2,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True,
fill_mode='nearest')

sameerg07

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

sameerg07

from keras.models import load_model
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
import cv2
import numpy as np

sameerg07

def fibonacci(n):
    if n == 1 or n == 2:
        return n-1
    else:
        return fibonacci(n-1) + fibonacci(n-2)

n = int(input())
i = 1
while i <= n:
    print(fibonacci(i))