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import numpy as np | |
from keras.preprocessing import image | |
from keras.applications import inception_v3 | |
# Load pre-trained image recognition model | |
model = inception_v3.InceptionV3() | |
# Load the image file and convert it to a numpy array | |
img = image.load_img("cat.png", target_size=(299, 299)) | |
input_image = image.img_to_array(img) | |
# Scale the image so all pixel intensities are between [-1, 1] as the model expects | |
input_image /= 255. | |
input_image -= 0.5 | |
input_image *= 2. | |
# Add a 4th dimension for batch size (as Keras expects) | |
input_image = np.expand_dims(input_image, axis=0) | |
# Run the image through the neural network | |
predictions = model.predict(input_image) | |
# Convert the predictions into text and print them | |
predicted_classes = inception_v3.decode_predictions(predictions, top=1) | |
imagenet_id, name, confidence = predicted_classes[0][0] | |
print("This is a {} with {:.4}% confidence!".format(name, confidence * 100)) |
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