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June 18, 2022 02:42
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How to use keras h5 model
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from keras.models import load_model | |
from PIL import Image, ImageOps | |
import numpy as np | |
# Load the model | |
model = load_model('keras_model.h5') | |
# 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>') | |
#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 | |
# run the inference | |
prediction = model.predict(data) | |
print(prediction) |
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