Log keras training output by wrapping stdout

wrap_keras_training.py

from __future__ import print_function
from streamlogger import StreamLogger
import logging
import sys

# Set up logger
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
handler = logging.FileHandler("./out.log")
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setLevel(logging.DEBUG)
handler.setFormatter(formatter)
logger.addHandler(handler)


class StdWrapper(object):
    def __init__(self, logger, std):
        self.old_std = std
        self.logger = logger or logging.getLogger()

    def write(self, *arg, **kwargs):
        if '\n' not in arg and ' ' != arg[-1]:
            msg = ''.join(map(str, arg)).replace('\n', "")
            logger.info(msg)
        # logger.info(arg)
        self.old_std.write(*arg)

    def flush(self):
        self.old_std.flush()


# wrap stdout
wrap_stdout = StdWrapper(logger, sys.stdout)
sys.stdout = wrap_stdout


# Begin training

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop

batch_size = 128
num_classes = 10
epochs = 2

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784, )))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))

model.summary()

model.compile(
    loss='categorical_crossentropy', optimizer=RMSprop(), metrics=['accuracy'])

history = model.fit(
    x_train,
    y_train,
    batch_size=batch_size,
    epochs=epochs,
    verbose=2,
    validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])