I have 3 categories of classes Tree, Stump, Ground
. I've made a list for these categories:
CATEGORIES = ["Tree", "Stump", "Ground"]
When i print my prediction, it gives me the output of
[[0. 1. 0.]]
I've read up about numpy's Argmax but I'm not entirely sure how to use it in this case.
I've tried using
print(np.argmax(prediction))
But that gives me the output of 1
. That's great but I would like to find out what's the index of 1
and then print out the Category instead of the highest value.
import cv2
import tensorflow as tf
import numpy as np
CATEGORIES = ["Tree", "Stump", "Ground"]
def prepare(filepath):
IMG_SIZE = 150 # This value must be the same as the value in Part1
img_array = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1)
# Able to load a .model, .h3, .chibai and even .dog
model = tf.keras.models.load_model("models/test.model")
prediction = model.predict([prepare('image.jpg')])
print("Predictions:")
print(prediction)
print(np.argmax(prediction))
I expect my prediction to show me:
Predictions:
[[0. 1. 0.]]
Stump
Thanks for reading :) I appreciate any help at all.
CodePudding user response:
You just have to index categories with the result of np.argmax
:
pred_name = CATEGORIES[np.argmax(prediction)]
print(pred_name)