How to Properly Combine TensorFlow's Dataset API and Keras?

Question

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) 

Here's the error I get:

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 

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

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?

Answer 1

Update June 09, 2018

• Starting from Tensorflow 1.9, one can pass tf.data.Dataset object directly into keras.Model.fit() and it would act similar to fit_generator.
• A complete example can be found on this gist.

# Load mnist training data (x_train, y_train), _ = tf.keras.datasets.mnist.load_data() training_set = tfdata_generator(x_train, y_train,is_training=True)  model = # your keras model here               model.fit(     training_set.make_one_shot_iterator(),     steps_per_epoch=len(x_train) // 128,     epochs=5,     verbose = 1) 

• tfdata_generator is a function that returns an iterable tf.data.Dataset.

def tfdata_generator(images, labels, is_training, batch_size=128):   '''Construct a data generator using `tf.Dataset`. '''    def map_fn(image, label):       '''Preprocess raw data to trainable input. '''     x = tf.reshape(tf.cast(image, tf.float32), (28, 28, 1))     y = tf.one_hot(tf.cast(label, tf.uint8), _NUM_CLASSES)     return x, y    dataset = tf.data.Dataset.from_tensor_slices((images, labels))    if is_training:     dataset = dataset.shuffle(1000)  # depends on sample size   dataset = dataset.map(map_fn)   dataset = dataset.batch(batch_size)   dataset = dataset.repeat()   dataset = dataset.prefetch(tf.contrib.data.AUTOTUNE)    return dataset 

Old Solution:

In addition to @Yu-Yang's answer, you can also modify tf.data.Dataset to become a generator for fit_generator as following

from tensorflow.contrib.learn.python.learn.datasets import mnist  data   = mnist.load_mnist() model  = # your Keras model model.fit_generator(generator = tfdata_generator(data.train.images, data.train.labels),                     steps_per_epoch=200,                     workers = 0 , # This is important                     verbose = 1)   def tfdata_generator(images, labels, batch_size=128, shuffle=True,):     def map_func(image, label):         '''A transformation function'''         x_train = tf.reshape(tf.cast(image, tf.float32), image_shape)         y_train = tf.one_hot(tf.cast(label, tf.uint8), num_classes)         return [x_train, y_train]      dataset  = tf.data.Dataset.from_tensor_slices((images, labels))     dataset  = dataset.map(map_func)     dataset  = dataset.shuffle().batch(batch_size).repeat()     iterator = dataset.make_one_shot_iterator()      next_batch = iterator.get_next()     while True:         yield K.get_session().run(next_batch)