在Keras中具有多个输入/输出的tf.data [英] tf.data with multiple inputs / outputs in Keras

问题描述

对于应用程序，例如配对文本相似性，输入数据类似于:pair_1, pair_2.在这些问题中，我们通常有多个输入数据.以前，我成功实现了模型:

For the application, such as pair text similarity, the input data is similar to: pair_1, pair_2. In these problems, we usually have multiple input data. Previously, I implemented my models successfully:

model.fit([pair_1, pair_2], labels, epochs=50)

我决定将输入管道替换为 tf.data API.为此，我创建一个类似于以下内容的数据集:

I decided to replace my input pipeline with tf.data API. To this end, I create a Dataset similar to:

dataset = tf.data.Dataset.from_tensor_slices((pair_1, pair2, labels))

它编译成功，但是在开始训练时会引发以下异常:

It compiles successfully but when start to train it throws the following exception:

AttributeError: 'tuple' object has no attribute 'ndim'

我的Keras和Tensorflow版本分别为2.1.6和1.11.0.我在Tensorflow存储库中发现了类似的问题: tf.keras，因为使用tf.data.Dataset时多输入模型不起作用.

My Keras and Tensorflow version respectively are 2.1.6 and 1.11.0. I found a similar issue in Tensorflow repository: tf.keras multi-input models don't work when using tf.data.Dataset.

有人知道如何解决此问题吗?

Does anyone know how to fix the issue?

这是代码的主要部分:

(q1_test, q2_test, label_test) = test
(q1_train, q2_train, label_train) = train

    def tfdata_generator(sent1, sent2, labels, is_training):
        '''Construct a data generator using tf.Dataset'''

        dataset = tf.data.Dataset.from_tensor_slices((sent1, sent2, labels))
        if is_training:
            dataset = dataset.shuffle(1000)  # depends on sample size

        dataset = dataset.repeat()
        dataset = dataset.prefetch(tf.contrib.data.AUTOTUNE)

        return dataset

train_dataset = tfdata_generator(q1_train, q2_train, label_train, is_training=True, batch_size=_BATCH_SIZE)
test_dataset = tfdata_generator(q1_test, q2_test, label_test, is_training=False, batch_size=_BATCH_SIZE)


inps1 = keras.layers.Input(shape=(50,))
inps2 = keras.layers.Input(shape=(50,))

embed = keras.layers.Embedding(input_dim=nb_vocab, output_dim=300, weights=[embedding], trainable=False)
embed1 = embed(inps1)
embed2 = embed(inps2)

gru = keras.layers.CuDNNGRU(256)
gru1 = gru(embed1)
gru2 = gru(embed2)

concat = keras.layers.concatenate([gru1, gru2])

preds = keras.layers.Dense(1, 'sigmoid')(concat)

model = keras.models.Model(inputs=[inps1, inps2], outputs=preds)
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
print(model.summary())

model.fit(
    train_dataset.make_one_shot_iterator(),
    steps_per_epoch=len(q1_train) // _BATCH_SIZE,
    epochs=50,
    validation_data=test_dataset.make_one_shot_iterator(),
    validation_steps=len(q1_test) // _BATCH_SIZE,
    verbose=1)

推荐答案

我不使用Keras，但我会使用tf.data.Dataset.from_generator()-例如:

I'm not using Keras but I would go with an tf.data.Dataset.from_generator() - like:

def _input_fn():
  sent1 = np.array([1, 2, 3, 4, 5, 6, 7, 8], dtype=np.int64)
  sent2 = np.array([20, 25, 35, 40, 600, 30, 20, 30], dtype=np.int64)
  sent1 = np.reshape(sent1, (8, 1, 1))
  sent2 = np.reshape(sent2, (8, 1, 1))

  labels = np.array([40, 30, 20, 10, 80, 70, 50, 60], dtype=np.int64)
  labels = np.reshape(labels, (8, 1))

  def generator():
    for s1, s2, l in zip(sent1, sent2, labels):
      yield {"input_1": s1, "input_2": s2}, l

  dataset = tf.data.Dataset.from_generator(generator, output_types=({"input_1": tf.int64, "input_2": tf.int64}, tf.int64))
  dataset = dataset.batch(2)
  return dataset

...

model.fit(_input_fn(), epochs=10, steps_per_epoch=4)

此生成器可以遍历您的文本文件/numpy数组，并在每次调用示例时产生收益. 在此示例中，我假设句子的单词已经转换为词汇表中的索引.

This generator can iterate over your e.g text-files / numpy arrays and yield on every call a example. In this example, I assume that the word of the sentences are already converted to the indices in the vocabulary.

由于OP的要求，使用Dataset.from_tensor_slices():

Since OP asked, it should be also possible with Dataset.from_tensor_slices():

def _input_fn():
  sent1 = np.array([1, 2, 3, 4, 5, 6, 7, 8], dtype=np.int64)
  sent2 = np.array([20, 25, 35, 40, 600, 30, 20, 30], dtype=np.int64)
  sent1 = np.reshape(sent1, (8, 1))
  sent2 = np.reshape(sent2, (8, 1))

  labels = np.array([40, 30, 20, 10, 80, 70, 50, 60], dtype=np.int64)
  labels = np.reshape(labels, (8))

  dataset = tf.data.Dataset.from_tensor_slices(({"input_1": sent1, "input_2": sent2}, labels))
  dataset = dataset.batch(2, drop_remainder=True)
  return dataset

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