From 10ff6fd7bb226a2fdeb35617347a05c491af7718 Mon Sep 17 00:00:00 2001
From: sryu1 <95025816+sryu1@users.noreply.github.com>
Date: Sun, 23 Oct 2022 11:30:54 +1000
Subject: [PATCH] Update Keras.md as suggested by Pull Request #301

As suggested by https://github.com/googlecreativelab/teachablemachine-community/pull/301, Defined class_names, added end='' for print class (line 43) since using readlines() for labels.txt adds an extra line between class and confidence score.
Added compile=False since the model is already trained. Fixed a couple of syntax (e.g. #301 is missing some things in line 7)
---
 snippets/markdown/image/tensorflow/keras.md | 16 +++++++++++++---
 1 file changed, 13 insertions(+), 3 deletions(-)

diff --git a/snippets/markdown/image/tensorflow/keras.md b/snippets/markdown/image/tensorflow/keras.md
index 25bd6e1..8306244 100644
--- a/snippets/markdown/image/tensorflow/keras.md
+++ b/snippets/markdown/image/tensorflow/keras.md
@@ -3,15 +3,23 @@ from keras.models import load_model
 from PIL import Image, ImageOps
 import numpy as np
 
+# Disable scientific notation for clarity
+np.set_printoptions(suppress=True)
+
 # Load the model
-model = load_model('keras_model.h5')
+model = load_model('keras_Model.h5', compile=False)
+
+# Load the labels
+class_names = open('labels.txt', 'r').readlines()
 
 # Create the array of the right shape to feed into the keras model
 # The 'length' or number of images you can put into the array is
 # determined by the first position in the shape tuple, in this case 1.
 data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+
 # Replace this with the path to your image
 image = Image.open('<IMAGE_PATH>').convert('RGB')
+
 #resize the image to a 224x224 with the same strategy as in TM2:
 #resizing the image to be at least 224x224 and then cropping from the center
 size = (224, 224)
@@ -19,8 +27,10 @@ image = ImageOps.fit(image, size, Image.ANTIALIAS)
 
 #turn the image into a numpy array
 image_array = np.asarray(image)
+
 # Normalize the image
 normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+
 # Load the image into the array
 data[0] = normalized_image_array
 
@@ -30,6 +40,6 @@ index = np.argmax(prediction)
 class_name = class_names[index]
 confidence_score = prediction[0][index]
 
-print("Class: ", class_name)
-print("Confidence Score: ", confidence_score)
+print('Class: ', class_name, end='')
+print('Confidence Score: ', confidence_score)
 ```