keras 11 mobileNet 2

mobileNet prediction

[[('n02510455', 'giant_panda', 0.9997156), 

('n02509815', 'lesser_panda', 0.00016485745), 

('n02447366', 'badger', 8.6640284e-05), 

('n02445715', 'skunk', 1.2499775e-05),

 ('n02443114', 'polecat', 1.1246146e-05)]]

#mobileNet.py

import  tensorflow as tf

from tensorflow import keras

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Activation, Dense, Flatten, Conv2D, MaxPool2D, Dropout

from tensorflow.keras.optimizers import Adam

from tensorflow.keras.metrics import categorical_crossentropy

from tensorflow.keras.preprocessing import image

from tensorflow.keras.applications import imagenet_utils

from sklearn.metrics import confusion_matrix

import itertools

import shutil

import random

import os

import glob

import matplotlib.pyplot as plt

from tensorflow.keras.models import load_model

import numpy as np

#train on GPU

pysical_devices = tf.config.experimental.list_physical_devices('GPU')

#print("Num GPUs Available: ", len(pysical_devices))

tf.config.experimental.set_memory_growth(pysical_devices[0], True)

mobile = tf.keras.applications.mobilenet.MobileNet()

def prepare_image(file):

    img = image.load_img('data/' + 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('panda.jpg')

predictions = mobile.predict(preprocessed_image)

results = imagenet_utils.decode_predictions(predictions)

print(results)

reference:

https://www.youtube.com/watch?v=qFJeN9V1ZsI&feature=youtu.be

http://chuanshuoge2.blogspot.com/2021/01/keras-10-mobilenet.html