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 README.md
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README.md
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@@ -75,12 +75,12 @@ Train the CNN with the Preprocessed images , use valitadion set.
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#### Training Hyperparameters
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optimizer = RMSprop(lr=0.0005, rho=0.9, epsilon=1e-08, decay=0.0)
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epochs = 22
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batch_size = 100
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loss = "categorical_crossentropy"
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metrics=["accuracy"]
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early_stopping = EarlyStopping(monitor='val_acc',min_delta=0,patience=2,verbose=0, mode='auto')
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#### Training Hyperparameters
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-optimizer = RMSprop(lr=0.0005, rho=0.9, epsilon=1e-08, decay=0.0)
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-epochs = 22
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-batch_size = 100
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-loss = "categorical_crossentropy"
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-metrics=["accuracy"]
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-early_stopping = EarlyStopping(monitor='val_acc',min_delta=0,patience=2,verbose=0, mode='auto')
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