我在 Keras 中有簡(jiǎn)單的自動(dòng)編碼器，我想使用日志記錄到張量板（因此我需要傳遞驗(yàn)證數(shù)據(jù)），并使用 Tensorflow Dataset API 使用預(yù)取從 TFRecord 加載數(shù)據(jù)。我讀了一些關(guān)于它的文章，但他們要么省略了驗(yàn)證管道，要么直接傳遞數(shù)據(jù)而不使用 feed dict 的事實(shí)要慢得多。源代碼是import tensorflow as tffrom keras.losses import mean_squared_errorfrom keras.models import Sequential, Modelfrom keras.layers import Dense, Input, Flatten, Reshape, Convolution2D,     Convolution2DTranspose, Conv2D, Conv2DTransposefrom keras.optimizers import Adamfrom keras import backend as Kfrom keras.callbacks import TensorBoarddef 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 datadef 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())