doc/master/ml__dataloader__TensorFlow_8py.html

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

chunk_size = 5000

block_size = 300


rdataframe = ROOT.RDataFrame(tree_name, file_name)

target = ["Type"]


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

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

    rdataframe,

    batch_size,

    chunk_size,

    block_size,

    target=target,

    validation_split=0.3,

    shuffle=True,

    drop_remainder=True,

)


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"])


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,

)

TRangeDynCast
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Definition TCollection.h:360

len
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t Atom_t Time_t UChar_t len
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:44