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resnet50 in keras

Innocent Iguana answered on April 17, 2024 Popularity 3/10 Helpfulness 8/10

answer resnet50 in keras

resnet50 in keras

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# Import image from keras preprocessing
from tensorflow.keras.preprocessing import image
# Import preprocess_input from tensorflow keras applications resnet50
from tensorflow.keras.applications.resnet50 import preprocess_input
# Load the image with the right target size for your model
img = image.load_img(img_path, target_size=(224, 224))
# Turn it into an array
img = image.img_to_array(img)
# Expand the dimensions so that it's understood by our network:
# img.shape turns from (224, 224, 3) into (1, 224, 224, 3)
img = np.expand_dims(img, axis=0)
# Pre-process the img in the same way training images were
img = preprocess_input(img)
# Import ResNet50 and decode_predictions from tensorflow.keras.applications.resnet50
from tensorflow.keras.applications.resnet50 import ResNet50, decode_predictions
# Instantiate a ResNet50 model with imagenet weights
model = ResNet50(weights='imagenet')
# Predict with ResNet50 on our img
preds = model.predict(img)
# Decode predictions and print it
print('Predicted:', decode_predictions(preds, top=1)[0])

Popularity 3/10 Helpfulness 8/10 Language python

Source: Grepper

Tags: keras python

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Contributed on Apr 17 2024

Innocent Iguana

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