Example of getting batches of events from a ROOT dataset into a basic TensorFlow workflow.
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"
ds_train, ds_valid = ROOT.TMVA.Experimental.CreateTFDatasets(
rdataframe,
batch_size,
chunk_size,
validation_split=0.3,
target=target,
)
num_of_epochs = 2
ds_train_repeated = ds_train.repeat(num_of_epochs)
ds_valid_repeated = ds_valid.repeat(num_of_epochs)
train_batches_per_epoch = ds_train.number_of_batches
validation_batches_per_epoch = ds_valid.number_of_batches
input_columns = ds_train.train_columns
num_features =
len(input_columns)
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)
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
ROOT's RDataFrame offers a modern, high-level interface for analysis of data stored in TTree ,...
Epoch 1/2
1/54 [..............................] - ETA: 3:06 - loss: 0.6882 - accuracy: 0.6094␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
12/54 [=====>........................] - ETA: 0s - loss: 0.0655 - accuracy: 0.9674 ␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
26/54 [=============>................] - ETA: 0s - loss: 0.0302 - accuracy: 0.9850␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
41/54 [=====================>........] - ETA: 0s - loss: 0.0192 - accuracy: 0.9905␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
54/54 [==============================] - ETA: 0s - loss: 0.0145 - accuracy: 0.9928␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
54/54 [==============================] - 4s 11ms/step - loss: 0.0145 - accuracy: 0.9928 - val_loss: 3.5051e-07 - val_accuracy: 1.0000
Epoch 2/2
1/54 [..............................] - ETA: 0s - loss: 2.7093e-07 - accuracy: 1.0000␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
16/54 [=======>......................] - ETA: 0s - loss: 3.6518e-07 - accuracy: 1.0000␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
30/54 [===============>..............] - ETA: 0s - loss: 3.5475e-07 - accuracy: 1.0000␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
45/54 [========================>.....] - ETA: 0s - loss: 3.4626e-07 - accuracy: 1.0000␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
54/54 [==============================] - 0s 9ms/step - loss: 4.0991e-07 - accuracy: 1.0000 - val_loss: 3.3808e-07 - val_accuracy: 1.0000
- Author
- Dante Niewenhuis
Definition in file RBatchGenerator_TensorFlow.py.