himanshurawlani

from keras.applications.inception_v3 import InceptionV3
from keras.layers import Input

inception_model = InceptionV3(weights='imagenet', input_tensor=Input(shape=(224, 224, 3)))
inception_model.save('inception.h5')

himanshurawlani

import tensorflow as tf

# The export path contains the name and the version of the model
tf.keras.backend.set_learning_phase(0)  # Ignore dropout at inference
model = tf.keras.models.load_model('./inception.h5')
export_path = '../my_image_classifier/1'

# Fetch the Keras session and save the model
# The signature definition is defined by the input and output tensors
# And stored with the default serving key

himanshurawlani

import argparse
import json

import numpy as np
import requests
from keras.applications import inception_v3
from keras.preprocessing import image

# Argument parser for giving input image_path from command line
ap = argparse.ArgumentParser()

himanshurawlani

import base64
import json
from io import BytesIO

import numpy as np
import requests
from flask import Flask, request, jsonify
from keras.applications import inception_v3
from keras.preprocessing import image

himanshurawlani

import os
import signal
import subprocess

# Making sure to use virtual environment libraries
activate_this = "/home/ubuntu/tensorflow/bin/activate_this.py"
exec(open(activate_this).read(), dict(__file__=activate_this))

# Change directory to where your Flask's app.py is present
os.chdir("/home/ubuntu/Desktop/Medium/keras-and-tensorflow-serving/flask_server")

himanshurawlani

# importing the requests library
import argparse
import base64

import requests

# defining the api-endpoint
API_ENDPOINT = "http://localhost:5000/imageclassifier/predict/"

# taking input image via command line

himanshurawlani

import tensorflow_datasets as tfds

SPLIT_WEIGHTS = (8, 1, 1)
splits = tfds.Split.TRAIN.subsplit(weighted=SPLIT_WEIGHTS)
(raw_train, raw_validation, raw_test), metadata = tfds.load(name="tf_flowers", 
                                                            with_info=True,
                                                            split=list(splits),
# specifying batch_size=-1 will load full dataset in the memory
#                                                             batch_size=-1,
# as_supervised: `bool`, if `True`, the returned `tf.data.Dataset`

himanshurawlani

def format_example(image, label):
    image = tf.cast(image, tf.float32)
    # Normalize the pixel values
    image = image / 255.0
    # Resize the image
    image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))
    return image, label

train = raw_train.map(format_example)
validation = raw_validation.map(format_example)

himanshurawlani

plt.figure(figsize=(12,12)) 

for batch in train.take(1):
    for i in range(9):
        image, label = batch[0][i], batch[1][i]
        plt.subplot(3, 3, i+1)
        plt.imshow(image.numpy())
        plt.title(get_label_name(label.numpy()))
        plt.grid(False)    
# OR

himanshurawlani

from tensorflow import keras

# Creating a simple CNN model in keras using functional API
def create_model():
    img_inputs = keras.Input(shape=IMG_SHAPE)
    conv_1 = keras.layers.Conv2D(32, (3, 3), activation='relu')(img_inputs)
    maxpool_1 = keras.layers.MaxPooling2D((2, 2))(conv_1)
    conv_2 = keras.layers.Conv2D(64, (3, 3), activation='relu')(maxpool_1)
    maxpool_2 = keras.layers.MaxPooling2D((2, 2))(conv_2)
    conv_3 = keras.layers.Conv2D(64, (3, 3), activation='relu')(maxpool_2)