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"
batch_size = 128
chunk_size = 5_000
target = "Type"
tree_name,
file_name,
batch_size,
chunk_size,
validation_split=0.3,
target=target,
)
num_features =
len(input_columns)
[
300, activation=
tf.nn.tanh, input_shape=(num_features,)
),
]
)
model.compile(optimizer=
"adam", loss=loss_fn, metrics=[
"accuracy"])
model.fit(ds_train, validation_data=ds_valid, epochs=2)
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
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
Epoch 1/2
1/Unknown ␛[1m4s␛[0m 4s/step - accuracy: 0.5156 - loss: 0.6862␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
16/Unknown ␛[1m4s␛[0m 4ms/step - accuracy: 0.8977 - loss: 0.1663␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
32/Unknown ␛[1m5s␛[0m 3ms/step - accuracy: 0.9386 - loss: 0.1009␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
49/Unknown ␛[1m5s␛[0m 3ms/step - accuracy: 0.9557 - loss: 0.0731␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m54/54␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m5s␛[0m 8ms/step - accuracy: 0.9592 - loss: 0.0669 - val_accuracy: 0.9565 - val_loss: 7.2134e-07
Epoch 2/2
␛[1m 1/54␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 51ms/step - accuracy: 1.0000 - loss: 1.0732e-06␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m16/54␛[0m ␛[32m━━━━━␛[0m␛[37m━━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 4ms/step - accuracy: 1.0000 - loss: 8.2541e-07 ␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m32/54␛[0m ␛[32m━━━━━━━━━━━␛[0m␛[37m━━━━━━━━━␛[0m ␛[1m0s␛[0m 3ms/step - accuracy: 1.0000 - loss: 7.7886e-07␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m48/54␛[0m ␛[32m━━━━━━━━━━━━━━━━━␛[0m␛[37m━━━␛[0m ␛[1m0s␛[0m 3ms/step - accuracy: 1.0000 - loss: 7.5997e-07␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m54/54␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 5ms/step - accuracy: 0.9997 - loss: 7.5562e-07 - val_accuracy: 0.9565 - val_loss: 6.8101e-07
- Author
- Dante Niewenhuis
Definition in file RBatchGenerator_TensorFlow.py.