##The code you provided is a Python script that trains a convolutional neural network (CNN) model on the MNIST dataset. The trained model is then saved as an h5 file, and the accuracy of the model on the test set is written to a text file.
Here's a breakdown of the code:
- The script imports the necessary libraries, including Keras for building the model and loading the MNIST dataset.
- The model_train function takes two arguments: epoch and n. It defines and trains the CNN model using the MNIST dataset. The number of epochs and the number of convolutional layers in the model are determined by the epoch and n arguments, respectively.
- The MNIST dataset is loaded and preprocessed. The input images are reshaped and normalized.
- The CNN model is defined using the Sequential API from Keras. It consists of convolutional layers, max pooling layers, a flatten layer, and dense layers.
- The model is compiled with a loss function, optimizer, and evaluation metric.
- The model is trained on the training set and evaluated on the test set.
- The accuracy of the model on the test set is calculated and saved as a variable a.
- The trained model is saved as an h5 file named "MNIST.h5" and moved to the "/mlops-task" directory.
- The accuracy value is written to a text file named "accuracy.txt" and moved to the "/mlops-task" directory. Overall, this script trains a CNN model on the MNIST dataset and saves the trained model and accuracy value for further use or analysis.