Merge pull request #303 from sryu1/master

Update keras.md as suggested by Pull Request #301

snippets/markdown/image/tensorflow/keras.md

@@ -3,24 +3,34 @@ 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)
 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)
 ```