doc/v634/RBatchGenerator__TensorFlow_8py.html

import tensorflow as tf

import ROOT


tree_name = "sig_tree"

file_name = "http://root.cern/files/Higgs_data.root"


batch_size = 128

chunk_size = 5_000


rdataframe = ROOT.RDataFrame(tree_name, file_name)


target = "Type"


# Returns two TF.Dataset for training and validation batches.

ds_train, ds_valid = ROOT.TMVA.Experimental.CreateTFDatasets(

    rdataframe,

    batch_size,

    chunk_size,

    validation_split=0.3,

    target=target,

)


num_of_epochs = 2


# Datasets have to be repeated as many times as there are epochs

ds_train_repeated = ds_train.repeat(num_of_epochs)

ds_valid_repeated = ds_valid.repeat(num_of_epochs)


# Number of batches per epoch must be given for model.fit

train_batches_per_epoch = ds_train.number_of_batches

validation_batches_per_epoch = ds_valid.number_of_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"])


# Train model

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)

len
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Definition TGWin32VirtualXProxy.cxx:249

ROOT::RDataFrame
ROOT's RDataFrame offers a modern, high-level interface for analysis of data stored in TTree ,...
Definition RDataFrame.hxx:41