Creating an MXNet Image Classifier in Amazon SageMaker

Log in to the AWS console and navigate to the AWS SageMaker page. From there, load the Jupyter Notebook that has been provided with this hands-on lab.

When prompted, select the conda_python3 kernel. In the notebook, the code line of %pip install mxnet installs the needed mxnet package.

1. Load the training images and labels into an NDArray using Pickle. The images and labels are provided in the lego-simple-mx-train file.
2. Load the testing images and labels into an NDArray using Pickle. The images and labels are provided in the lego-simple-mx-test file.
3. Add in the human-readable class names for the labels.
4. Convert the training and testing NDArrays to MXNet Tensors. For better results, normalize the data using the mean of 0.13 and standard deviation of 0.31, which have been precomputed for this dataset.
5. Visualize the first few images from the training data set to better understand the data.

1. Create a neural network model using Gluon.
   • Remember to check your input shape and adjust if necessary.
   • You can get decent performance from a single hidden layer, but feel free to experiment with different model architectures.
   • You should have as many output nodes as labels you are trying to predict. Remember to pick an activation function that will output categorical probabilities.
2. Keep track of accuracy during your training process.
3. Choose a loss function appropriate for classification tasks.
4. Train the model using the training data and training labels. You won't need to train for many epochs. Save the history of the training process.

1. Calculate the accuracy of the model on the testing data.
2. View the raw output of a model prediction for an image in the test set.
3. Determine the label that the model predicted for the image and compare that to the actual label.

1. Predict the labels for all of the testing data.
2. Compare the predictions against the actual labels.