keras_model_summary_print_fn_line_break_bug.py

import io
import logging
import os
from logging import Logger

from keras.layers import (
    ConvLSTM2D,
    Dense,
    Dropout,
    Flatten,
    Input,
    MaxPooling3D,
    TimeDistributed,
)
from keras.models import Model, Sequential


# Constant variables
SUBSET_SIZE = 10
SEQUENCE_LENGTH = 20
IMAGE_HEIGHT = 64
IMAGE_WIDTH = 64
OUTPUT_LAYER_ACTIVATION_FUNCTION = "softmax"


def setup_logger() -> Logger:
    logger = logging.getLogger(__name__)

    log_file_handler = logging.FileHandler(os.path.join(os.getcwd(), "my_log.log"))
    log_file_handler.setFormatter(
        logging.Formatter("%(asctime)s [%(levelname)s] %(message)s")
    )

    logger.setLevel(logging.INFO)
    logger.addHandler(log_file_handler)

    log_stream_handler = logging.StreamHandler()
    log_stream_handler.setFormatter(
        logging.Formatter("%(asctime)s [%(levelname)s] %(message)s")
    )
    logger.addHandler(log_stream_handler)

    return logger


def get_summary_string(model: Model) -> str:
    # SOURCE: https://stackoverflow.com/a/53668338/14319439
    string_list = []
    model.summary(print_fn=lambda x: string_list.append(x))
    short_model_summary = "\n".join(string_list)

    return short_model_summary


def get_model_summary(model):
    # SOURCE: https://stackoverflow.com/a/53937848/14319439
    stream = io.StringIO()
    model.summary(print_fn=lambda x: stream.write(x + "\n"))
    summary_string = stream.getvalue()
    stream.close()
    return summary_string


class MyModel:

    def __init__(self, logger: Logger) -> None:
        self.__logger = logger
        self.__model = self.__build_model()

    def __build_model(self) -> Model:
        self.__logger.info("Building Model")

        model = Sequential()
        model.add(Input(shape=(SEQUENCE_LENGTH, IMAGE_HEIGHT, IMAGE_WIDTH, 3)))

        for iteration, number_of_filters in enumerate([4, 8, 14, 16]):
            model.add(
                ConvLSTM2D(
                    filters=number_of_filters,
                    kernel_size=(3, 3),
                    activation="tanh",
                    data_format="channels_last",
                    recurrent_dropout=0.2,
                    return_sequences=True,
                )
            )

            model.add(
                MaxPooling3D(
                    pool_size=(1, 2, 2), padding="same", data_format="channels_last"
                )
            )

            if iteration != 3:
                model.add(TimeDistributed(layer=Dropout(rate=0.2)))

        model.add(Flatten())

        model.add(Dense(units=SUBSET_SIZE, activation=OUTPUT_LAYER_ACTIVATION_FUNCTION))

        self.__logger.info("Finished building Model")

        # Option 1
        self.__logger.info(f"Model architecture: {get_summary_string(model=model)}")

        # # Option 2
        # self.__logger.info(
        #     f'Model architecture: {get_model_summary(model=model)}'
        # )

        # # Option 3
        # # SOURCE: https://stackoverflow.com/a/50077092/14319439
        # model.summary(print_fn=self.__logger.info)

        # Option 4
        # model.summary(
        #     print_fn=lambda x: self.__logger.info(f'Model architecture: {x}')
        # )

        return model


if __name__ == "__main__":
    logger = setup_logger()
    model = MyModel(logger=logger)

requirements.txt

absl-py==2.1.0
astunparse==1.6.3
certifi==2024.2.2
charset-normalizer==3.3.2
flatbuffers==24.3.25
gast==0.5.4
google-pasta==0.2.0
grpcio==1.62.1
h5py==3.10.0
idna==3.6
keras==3.1.1
libclang==18.1.1
Markdown==3.6
markdown-it-py==3.0.0
MarkupSafe==2.1.5
mdurl==0.1.2
ml-dtypes==0.3.2
namex==0.0.7
numpy==1.26.4
opt-einsum==3.3.0
optree==0.11.0
packaging==24.0
protobuf==4.25.3
Pygments==2.17.2
requests==2.31.0
rich==13.7.1
six==1.16.0
tensorboard==2.16.2
tensorboard-data-server==0.7.2
tensorflow==2.16.1
tensorflow-io-gcs-filesystem==0.36.0
termcolor==2.4.0
typing_extensions==4.10.0
urllib3==2.2.1
Werkzeug==3.0.1
wrapt==1.16.0