Instructions to use DataScienceProject/CNN_And_ELA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use DataScienceProject/CNN_And_ELA with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://DataScienceProject/CNN_And_ELA") - Notebooks
- Google Colab
- Kaggle
Update cnn_ela_test.py
Browse files- cnn_ela_test.py +1 -1
cnn_ela_test.py
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@@ -20,7 +20,7 @@ Created on Fri May 24 14:31:20 2024
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from keras.models import Sequential
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from keras.layers import Conv2D, MaxPool2D, Dropout, Flatten, Dense
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from
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import pandas as pd
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import numpy as np
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from PIL import Image
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from keras.models import Sequential
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from keras.layers import Conv2D, MaxPool2D, Dropout, Flatten, Dense
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from cnn_ela_training import convert_to_ela_image, shuffle_and_split_data, labeling
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import pandas as pd
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import numpy as np
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from PIL import Image
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