Use DenseNet to train and inference the Sin function.
[Instructions]
1. 1_delete_log.ipynb
Delete the old logs file.
2. 2_train.ipynb
Set the parameters:
• input_data_filename: input data file path
• model_filename: the path of the output model file
• scaler_filename: output normalized file path
• epochs: the number of epochs
• learning_rate: learning rate
Set neural network parameters:
• n_feature: the number of features entered
• n_hidden_1: neurons in the first hidden layer
• n_hidden_2: neurons in the second hidden layer
• n_output: the number of outputs
After the setting is completed, it can be executed.
3. 3_kill_tensorboard.ipynb
Delete the previous tensorboard.
4. 4_tensorboard.ipynb
Start tensorboard.
5. 5_inference.ipynb
Set the parameters:
• input_data_filename: input data file path
• model_filename: input model file path
• scaler_filename: input normalized file path
Set neural network parameters:
• n_feature: the number of features entered
• n_hidden_1: neurons in the first hidden layer
• n_hidden_2: neurons in the second hidden layer
• n_output: the number of outputs
After the setting is completed, it can be executed.
After execution, you can see the results of DenseNet's inference.
This SDK is built in AppForAI - AI Dev Tools.
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