如何正确结合 TensorFlow 的 Dataset API 和 Keras? [英] How to Properly Combine TensorFlow's Dataset API and Keras?

问题描述

Keras 的 fit_generator() 模型方法需要一个生成器来生成形状(输入、目标)的元组，其中两个元素都是 NumPy 数组.文档 似乎暗示如果我简单地将 Dataset 迭代器 在生成器中，并确保将张量转换为 NumPy 数组，我应该很高兴.但是，此代码给了我一个错误:

Keras' fit_generator() model method expects a generator which produces tuples of the shape (input, targets), where both elements are NumPy arrays. The documentation seems to imply that if I simply wrap a Dataset iterator in a generator, and make sure to convert the Tensors to NumPy arrays, I should be good to go. This code, however, gives me an error:

import numpy as np
import os
import keras.backend as K
from keras.layers import Dense, Input
from keras.models import Model
import tensorflow as tf
from tensorflow.contrib.data import Dataset

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

with tf.Session() as sess:
    def create_data_generator():
        dat1 = np.arange(4).reshape(-1, 1)
        ds1 = Dataset.from_tensor_slices(dat1).repeat()

        dat2 = np.arange(5, 9).reshape(-1, 1)
        ds2 = Dataset.from_tensor_slices(dat2).repeat()

        ds = Dataset.zip((ds1, ds2)).batch(4)
        iterator = ds.make_one_shot_iterator()
        while True:
            next_val = iterator.get_next()
            yield sess.run(next_val)

datagen = create_data_generator()

input_vals = Input(shape=(1,))
output = Dense(1, activation='relu')(input_vals)
model = Model(inputs=input_vals, outputs=output)
model.compile('rmsprop', 'mean_squared_error')
model.fit_generator(datagen, steps_per_epoch=1, epochs=5,
                    verbose=2, max_queue_size=2)

这是我得到的错误:

Using TensorFlow backend.
Epoch 1/5
Exception in thread Thread-1:
Traceback (most recent call last):
  File "/home/jsaporta/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 270, in __init__
    fetch, allow_tensor=True, allow_operation=True))
  File "/home/jsaporta/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2708, in as_graph_element
    return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
  File "/home/jsaporta/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2787, in _as_graph_element_locked
    raise ValueError("Tensor %s is not an element of this graph." % obj)
ValueError: Tensor Tensor("IteratorGetNext:0", shape=(?, 1), dtype=int64) is not an element of this graph.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/jsaporta/anaconda3/lib/python3.6/threading.py", line 916, in _bootstrap_inner
    self.run()
  File "/home/jsaporta/anaconda3/lib/python3.6/threading.py", line 864, in run
    self._target(*self._args, **self._kwargs)
  File "/home/jsaporta/anaconda3/lib/python3.6/site-packages/keras/utils/data_utils.py", line 568, in data_generator_task
    generator_output = next(self._generator)
  File "./datagen_test.py", line 25, in create_data_generator
    yield sess.run(next_val)
  File "/home/jsaporta/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 895, in run
    run_metadata_ptr)
  File "/home/jsaporta/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1109, in _run
    self._graph, fetches, feed_dict_tensor, feed_handles=feed_handles)
  File "/home/jsaporta/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 413, in __init__
    self._fetch_mapper = _FetchMapper.for_fetch(fetches)
  File "/home/jsaporta/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 233, in for_fetch
    return _ListFetchMapper(fetch)
  File "/home/jsaporta/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 340, in __init__
    self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
  File "/home/jsaporta/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 340, in <listcomp>
    self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
  File "/home/jsaporta/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 241, in for_fetch
    return _ElementFetchMapper(fetches, contraction_fn)
  File "/home/jsaporta/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 277, in __init__
    'Tensor. (%s)' % (fetch, str(e)))
ValueError: Fetch argument <tf.Tensor 'IteratorGetNext:0' shape=(?, 1) dtype=int64> cannot be interpreted as a Tensor. (Tensor Tensor("IteratorGetNext:0", shape=(?, 1), dtype=int64) is not an element of this graph.)

Traceback (most recent call last):
  File "./datagen_test.py", line 34, in <module>
    verbose=2, max_queue_size=2)
  File "/home/jsaporta/anaconda3/lib/python3.6/site-packages/keras/legacy/interfaces.py", line 87, in wrapper
    return func(*args, **kwargs)
  File "/home/jsaporta/anaconda3/lib/python3.6/site-packages/keras/engine/training.py", line 2011, in fit_generator
    generator_output = next(output_generator)
StopIteration

奇怪的是，在我初始化 datagen 的位置之后直接添加包含 next(datagen) 的行会导致代码运行得很好，没有错误.

Strangely enough, adding a line containing next(datagen) directly after where I initialize datagen causes the code to run just fine, with no errors.

为什么我的原始代码不起作用?为什么当我将该行添加到我的代码时它开始工作?有没有更有效的方法来将 TensorFlow 的数据集 API 与 Keras 结合使用，而不涉及将张量转换为 NumPy 数组然后再返回?

Why does my original code not work? Why does it begin to work when I add that line to my code? Is there a more efficient way to use TensorFlow's Dataset API with Keras that doesn't involve converting Tensors to NumPy arrays and back again?

推荐答案

确实有一种更有效的方法来使用 Dataset 而不必将张量转换为 numpy 数组.但是，它不是(还?)在官方文档中.从发行说明来看，这是 Keras 2.0.7 中引入的一项功能.您可能需要安装 keras>=2.0.7 才能使用它.

There is indeed a more efficient way to use Dataset without having to convert the tensors into numpy arrays. However, it is not (yet?) on the official documentation. From the release note, it's a feature introduced in Keras 2.0.7. You may have to install keras>=2.0.7 in order to use it.

x = np.arange(4).reshape(-1, 1).astype('float32')
ds_x = Dataset.from_tensor_slices(x).repeat().batch(4)
it_x = ds_x.make_one_shot_iterator()

y = np.arange(5, 9).reshape(-1, 1).astype('float32')
ds_y = Dataset.from_tensor_slices(y).repeat().batch(4)
it_y = ds_y.make_one_shot_iterator()

input_vals = Input(tensor=it_x.get_next())
output = Dense(1, activation='relu')(input_vals)
model = Model(inputs=input_vals, outputs=output)
model.compile('rmsprop', 'mse', target_tensors=[it_y.get_next()])
model.fit(steps_per_epoch=1, epochs=5, verbose=2)

几个不同点:

1. 将 tensor 参数提供给 Input 层.Keras 将从该张量中读取值，并将其用作拟合模型的输入.
2. 将 target_tensors 参数提供给 Model.compile().
3. 记得把 x 和 y 都转换成 float32.在正常使用情况下，Keras 会为你做这个转换.但现在你必须自己做.
4. 批量大小是在Dataset 的构建过程中指定的.使用 steps_per_epoch 和 epochs 来控制何时停止模型拟合.

1. Supply the tensor argument to the Input layer. Keras will read values from this tensor, and use it as the input to fit the model.
2. Supply the target_tensors argument to Model.compile().
3. Remember to convert both x and y into float32. Under normal usage, Keras will do this conversion for you. But now you'll have to do it yourself.
4. Batch size is specified during the construction of Dataset. Use steps_per_epoch and epochs to control when to stop model fitting.

简而言之，使用 Input(tensor=...)、model.compile(target_tensors=...) 和 model.fit(x=None, y=None, ...) 如果要从张量中读取数据.

In short, use Input(tensor=...), model.compile(target_tensors=...) and model.fit(x=None, y=None, ...) if your data are to be read from tensors.

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