doc/v640/TMVA__SOFIE__Keras_8py.html

import contextlib

import warnings


import numpy as np

import ROOT

from tensorflow.keras.layers import Activation, Dense, Input, Softmax

from tensorflow.keras.models import Model


# Enable ROOT in batch mode (same effect as -nodraw)

ROOT.gROOT.SetBatch(True)


@contextlib.contextmanager

def expect_warning(category, message):

    """Silence a known third-party warning and raise if it stops firing.


    Notifies us to drop the workaround once the upstream library is fixed.

    """

    with warnings.catch_warnings(record=True) as caught:

        warnings.simplefilter("always")

        yield

    seen = False

    for w in caught:

        if issubclass(w.category, category) and message in str(w.message):

            seen = True

        else:

            warnings.warn_explicit(w.message, w.category, w.filename, w.lineno)

    if not seen:

        raise RuntimeError(

            f"Expected {category.__name__} containing {message!r} was not "

            "emitted. This tutorial's workaround can probably be removed."

        )


# -----------------------------------------------------------------------------

# Step 1: Create and train a simple Keras model (via embedded Python)

# -----------------------------------------------------------------------------


input = Input(shape=(4,), batch_size=2)

x = Dense(32)(input)

x = Activation("relu")(x)

x = Dense(16, activation="relu")(x)

x = Dense(8, activation="relu")(x)

x = Dense(2)(x)

output = Softmax()(x)

model = Model(inputs=input, outputs=output)


randomGenerator = np.random.RandomState(0)

x_train = randomGenerator.rand(4, 4)

y_train = randomGenerator.rand(4, 2)


model.compile(loss="mse", optimizer="adam")

model.fit(x_train, y_train, epochs=3, batch_size=2)


# Keras' internal ``np.array(x)`` (TensorFlow backend) does not yet implement

# the NumPy 2.0 ``__array__(copy=...)`` signature, so saving the model emits a

# DeprecationWarning that we cannot fix from user code.

if tuple(int(p) for p in np.__version__.split(".")[:2]) >= (2, 0):

    ctx = expect_warning(DeprecationWarning, "__array__ implementation doesn't accept a copy keyword")

else:

    ctx = contextlib.nullcontext()


with ctx:

    model.save("KerasModel.keras")


model.summary()


# -----------------------------------------------------------------------------

# Step 2: Use TMVA::SOFIE to parse the ONNX model

# -----------------------------------------------------------------------------


# Parse the ONNX model


model = ROOT.TMVA.Experimental.SOFIE.PyKeras.Parse("KerasModel.keras")


# Generate inference code

model.Generate()

model.OutputGenerated()

# print generated code

print("\n**************************************************")

print("           Generated code")

print("**************************************************\n")

model.PrintGenerated()

print("**************************************************\n\n\n")


# Compile the generated code

ROOT.gInterpreter.Declare('#include "KerasModel.hxx"')


# -----------------------------------------------------------------------------

# Step 3: Run inference

# -----------------------------------------------------------------------------


# instantiate SOFIE session class

session = ROOT.TMVA_SOFIE_KerasModel.Session()


# Input tensor (same shape as training input)

x = np.array([[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8]], dtype=np.float32)


# Run inference

y = session.infer(x)


print("Inference output:", y)

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

ROOT::Detail::TRangeCast
Definition TCollection.h:313

int