Question

我在Keras中有一个简单的自动编码器，我想使用日志记录到张量板（因此我需要传递验证数据），并使用Tensorflow Dataset API和prefetch从TFRecord加载数据。我读了一些有关它的文章，但他们要么省略了验证管道，要么直接传递数据而没有提要dict的事实要慢得多。

源代码是

import tensorflow as tf
from keras.losses import mean_squared_error
from keras.models import Sequential, Model
from keras.layers import Dense, Input, Flatten, Reshape, Convolution2D,     Convolution2DTranspose, Conv2D, Conv2DTranspose
from keras.optimizers import Adam
from keras import backend as K
from keras.callbacks import TensorBoard

def create_dataset(tf_record, batch_size):
    data = tf.data.TFRecordDataset(tf_record)
    data = data.map(TFReader._parse_example_encoded, num_parallel_calls=8)
    data = data.apply(tf.data.experimental.shuffle_and_repeat(buffer_size=100))
    data = data.batch(batch_size, drop_remainder=True)
    data = data.prefetch(4)
    return data


def main(_):
    batch_size = 8  # todo: check and try bigger
    data = create_dataset('../../datasets/anime/no-game-no-life-ep-2.tfrecord', batch_size)
    iterator = data.make_one_shot_iterator()

    K.set_image_data_format('channels_last')  # set format

    input_tensor = Input(tensor=iterator.get_next())
    out = Conv2D(8, (3, 3), activation='elu', border_mode='valid', batch_input_shape=(batch_size, 432, 768, 3))(input_tensor)
    out = Conv2D(16, (3, 3), activation='elu', border_mode='valid')(out)
    out = Conv2D(32, (3, 3), activation='elu', border_mode='valid', name='bottleneck')(out)
    out = Conv2DTranspose(32, (3, 3), activation='elu', padding='valid')(out)
    out = Conv2DTranspose(16, (3, 3), activation='elu', padding='valid')(out)
    out = Conv2DTranspose(8, (3, 3), activation='elu', padding='valid')(out)
    out = Conv2D(3, (3, 3), activation='elu', padding='same')(out)
    m = Model(inputs=input_tensor, outputs=out)
    m.compile(loss=mean_squared_error, optimizer=Adam(), target_tensors=iterator.get_next())
    print(m.summary())
    tensorboard = TensorBoard(
        log_dir='logs/anime', histogram_freq=5, embeddings_freq=5, embeddings_layer_names=['bottleneck'],
    write_images=True, embeddings_data=iterator.get_next(), embeddings_metadata='embeddings.tsv')
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    set_session(tf.Session(config=config))

    history = m.fit(steps_per_epoch=100, epochs=50, verbose=1,
                validation_data=(iterator.get_next(), iterator.get_next()),
                validation_steps=4,
                callbacks=[tensorboard]
                )


if __name__ == '__main__':
    tf.app.run()

训练本身开始，第一个纪元训练，但随后在验证过程中失败，

File "C:\Users\Azathoth\AppData\Local\JetBrains\Toolbox\apps\PyCharm-P\ch-0\183.5429.31\helpers\pydev\pydevd.py", line 1741, in <module>
main()
  File "C:\Users\Azathoth\AppData\Local\JetBrains\Toolbox\apps\PyCharm-P\ch-0\183.5429.31\helpers\pydev\pydevd.py", line 1735, in main
globals = debugger.run(setup['file'], None, None, is_module)
File "C:\Users\Azathoth\AppData\Local\JetBrains\Toolbox\apps\PyCharm-P\ch-0\183.5429.31\helpers\pydev\pydevd.py", line 1135, in run
pydev_imports.execfile(file, globals, locals)  # execute the script
File "C:\Users\Azathoth\AppData\Local\JetBrains\Toolbox\apps\PyCharm-P\ch-0\183.5429.31\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "E:/Projects/anime-style-transfer/code/neural_style_transfer/anime_dimension_reduction_keras.py", line 95, in <module>
tf.app.run()
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\platform\app.py", line 125, in run
_sys.exit(main(argv))
File "E:/Projects/anime-style-transfer/code/neural_style_transfer/anime_dimension_reduction_keras.py", line 78, in main
callbacks=[tensorboard]
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 1039, in fit
validation_steps=validation_steps)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training_arrays.py", line 217, in fit_loop
callbacks.on_epoch_end(epoch, epoch_logs)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\callbacks.py", line 79, in on_epoch_end
callback.on_epoch_end(epoch, logs)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\callbacks.py", line 912, in on_epoch_end
raise ValueError("If printing histograms, validation_data must be "
ValueError: If printing histograms, validation_data must be provided, and cannot be a generator.

我认为问题在于传递验证数据，因为它直接使用了训练tfrecord的输入张量。

尽管我不需要单独的训练和验证数据，所以如果有什么办法告诉Keras，它可以在相同的输入上进行验证，那只要我获得TensorBoard日志就可以了。

Answer 1

很少有选择：

您是否看过此链接https://github.com/keras-team/keras/issues/3358（juiceboxjoe的解决方案）？
编写一个TensorboardWrapper，从生成器加载验证数据，并将其作为回调传递。
如果您不关心验证，请从训练数据中加载一两个样本，然后将它们作为数组传递给validation_data。
如果不需要直方图，则将histogram_freq设置为0。

具有Tensorflow Dataset API的Keras自动编码器，并记录到Tensorboard

1 个答案: