From 3ec7d22d1ca0c5a5bbd54a735f917aebab633928 Mon Sep 17 00:00:00 2001 From: Navan Chauhan Date: Mon, 1 Jun 2020 12:33:18 +0530 Subject: Publish deploy 2020-06-01 12:33 --- posts/2019-12-10-TensorFlow-Model-Prediction/index.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'posts/2019-12-10-TensorFlow-Model-Prediction') diff --git a/posts/2019-12-10-TensorFlow-Model-Prediction/index.html b/posts/2019-12-10-TensorFlow-Model-Prediction/index.html index da7cae5..5f73c10 100644 --- a/posts/2019-12-10-TensorFlow-Model-Prediction/index.html +++ b/posts/2019-12-10-TensorFlow-Model-Prediction/index.html @@ -1,4 +1,4 @@ -Making Predictions using Image Classifier (TensorFlow) | Navan Chauhan
Navan Chauhan
1 minute readCreated on December 10, 2019Last modified on January 18, 2020

Making Predictions using Image Classifier (TensorFlow)

This was tested on TF 2.x and works as of 2019-12-10

If you want to understand how to make your own custom image classifier, please refer to my previous post.

If you followed my last post, then you created a model which took an image of dimensions 50x50 as an input.

First we import the following if we have not imported these before

import cv2
+Making Predictions using Image Classifier (TensorFlow) | Navan Chauhan
Navan Chauhan
1 minute readCreated on December 10, 2019Last modified on June 1, 2020
Making Predictions using Image Classifier (TensorFlow)
This was tested on TF 2.x and works as of 2019-12-10
If you want to understand how to make your own custom image classifier, please refer to my previous post.
If you followed my last post, then you created a model which took an image of dimensions 50x50 as an input.
First we import the following if we have not imported these before
import cv2
 import os
 
Then we read the file using OpenCV.
image=cv2.imread(imagePath)
 
The cv2. imread() function returns a NumPy array representing the image. Therefore, we need to convert it before we can use it.
image_from_array = Image.fromarray(image, 'RGB')
@@ -6,4 +6,4 @@
 
After this we create a batch consisting of only one image
p = np.expand_dims(size_image, 0)
 
We then convert this uint8 datatype to a float32 datatype
img = tf.cast(p, tf.float32)
 
Finally we make the prediction
print(['Infected','Uninfected'][np.argmax(model.predict(img))])
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Infected
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Infected

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