Project 6 Mini Project

#IMPORT ALL THE NECESSARY MODULE LIBARARIES SUCH AS TENSORFLOW AND ITS BASIC LAYERS

import numpy as np

import cv2

import tensorflow as tf

from tensorflow import keras

from tensorflow.keras.layers import Dense, Activation

from tensorflow.keras.optimizers import Adam

from tensorflow.keras.metrics import categorical_crossentropy

from tensorflow.keras.preprocessing import image

from tensorflow.keras.preprocessing.image import ImageDataGenerator

from tensorflow.keras.applications import imagenet_utils

from sklearn.metrics import confusion_matrix

from tensorflow.keras.models import Model

import itertools

import shutil

import random

import matplotlib.pyplot as plt

 
# USE THE MOBILENET MODEL TO CHECK THE QUALITY OF CLASSIFICATION OF THIS PERTICULAR MODULE ON THE BASIS OF THE ACCORDANCE OF IMAGENET CLASSES.

mobile = tf.keras.applications.mobilenet.MobileNet(  input_shape=(224, 224, 3),

    alpha=1.0,

    include_top=True,

    weights="imagenet",

    input_tensor=None,

    classes=1000,

    pooling=None,

    classifier_activation="softmax",

    )

def prepare_image(file):

    img = image.load_img(file, target_size=(224, 224))

    img_array = image.img_to_array(img)

    img_array_expanded_dims = np.expand_dims(img_array, axis=0)

    return tf.keras.applications.mobilenet.preprocess_input(img_array_expanded_dims)

preprocessed_image = prepare_image('tiger.jpg')

predictions = mobile.predict(preprocessed_image)

results = imagenet_utils.decode_predictions(predictions)

print(results)