doc/v632/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


target = "Type"


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

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

    tree_name,

    file_name,

    batch_size,

    chunk_size,

    validation_split=0.3,

    target=target,

)


# 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.Dense(

            300, activation=tf.nn.tanh, input_shape=(num_features,)

        ),  # input shape required

        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, validation_data=ds_valid, epochs=2)

len
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Definition TGWin32VirtualXProxy.cxx:249