Training data

We use brew in this chapter to simplify the process of building our LeNet network. We begin by first initializing the model using ModelHelper, which was introduced in the previous chapter:

# Create the model helper for the train model
train_model = model_helper.ModelHelper(name="mnist_lenet_train_model")

We then add inputs to the training network using our add_model_inputs method:

# Specify the input is from the train lmdb
data, label = add_model_inputs(
    train_model,
    batch_size=64,
    db=os.path.join(data_folder, "mnist-train-nchw-lmdb"),
    db_type="lmdb",
)

Training data is usually stored in a database (DB) so that it can be accessed efficiently. Reading from a DB is usually faster than reading from thousands of individual files on the filesystem. For every training image in the MNIST dataset, the DB stores the grayscale pixel...