ml_dataloader_TensorFlow.py

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### \file
### \ingroup tutorial_ml
### \notebook -nodraw
### Example of getting batches of events from a ROOT dataset into a basic
### TensorFlow workflow.
###
### \macro_code
### \macro_output
### \author Dante Niewenhuis
 
import ROOT
 
# TensorFlow has to be imported after ROOT to avoid LLVM symbol clashes if ROOT
# was built with LLVM in Debug mode and TensorFlow>=2.20.0.
import tensorflow as tf
 
tree_name = "sig_tree"
file_name = str(ROOT.gROOT.GetTutorialDir()) + "/machine_learning/data/Higgs_data.root"
 
batch_size = 128
approx_batches_in_memory = 50
 
rdataframe = ROOT.RDataFrame(tree_name, file_name)
target = ["Type"]
 
# Returns two TF.Dataset for training and validation batches.
dl = ROOT.Experimental.ML.RDataLoader(
    rdataframe,
    batch_size,
    approx_batches_in_memory,
    target=target,
    shuffle=True,
    drop_remainder=True,
)
 
ds_train, ds_valid = dl.train_test_split(test_size=0.3)
 
num_of_epochs = 2
 
# Datasets have to be repeated as many times as there are epochs
ds_train_repeated = ds_train.as_tensorflow().repeat(num_of_epochs)
ds_valid_repeated = ds_valid.as_tensorflow().repeat(num_of_epochs)
 
# Number of batches per epoch must be given for model.fit
train_batches_per_epoch = ds_train.num_batches
validation_batches_per_epoch = ds_valid.num_batches
 
# Get a list of the columns used for training
input_columns = ds_train.train_columns
num_features = len(input_columns)
 
##############################################################################
# AI example
##############################################################################
 
# Define TensorFlow model
model = tf.keras.Sequential(
    [
        tf.keras.layers.Input(shape=(num_features,)),
        tf.keras.layers.Dense(300, activation=tf.nn.tanh),
        tf.keras.layers.Dense(300, activation=tf.nn.tanh),
        tf.keras.layers.Dense(300, activation=tf.nn.tanh),
        tf.keras.layers.Dense(1, activation=tf.nn.sigmoid),
    ]
)
 
loss_fn = tf.keras.losses.BinaryCrossentropy()
model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
 
model.fit(
    ds_train_repeated,
    steps_per_epoch=train_batches_per_epoch,
    validation_data=ds_valid_repeated,
    validation_steps=validation_batches_per_epoch,
    epochs=num_of_epochs,
)