Tensorflow: tf.data.Dataset, Cannot batch tensors with different shapes in component 0

Question

I have the following error in my input pipeline:

tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot batch tensors with different shape

Answer 1

First case: we want the output to have fixed batch size

In this case, the generator generates values of shape [None, 48, 48, 3] where the first dimension could be anything. We want to batch this so that the output is [batch_size, 48, 48, 3]. If we use directly tf.data.Dataset.batch, we will have an error, so we need to unbatch first.

To do that we can use tf.contrib.data.unbatch like this before batching:

dataset = dataset.apply(tf.contrib.data.unbatch())
dataset = dataset.batch(batch_size)

---

Here is a full example where the generator yields [1], [2, 2], [3, 3, 3] and [4, 4, 4, 4].

We can't batch these output values directly, so we unbatch and then batch them:

def gen():
    for i in range(1, 5):
        yield [i] * i

# Create dataset from generator
# The output shape is variable: (None,)
dataset = tf.data.Dataset.from_generator(gen, tf.int64, tf.TensorShape([None]))

# The issue here is that we want to batch the data
dataset = dataset.apply(tf.contrib.data.unbatch())
dataset = dataset.batch(2)

# Create iterator from dataset
iterator = dataset.make_one_shot_iterator()
x = iterator.get_next()  # shape (None,)

sess = tf.Session()
for i in range(5):
    print(sess.run(x))

This will print the following output:

[1 2]
[2 3]
[3 3]
[4 4]
[4 4]

---

Second case: we want to concatenate variable sized batches

Update (03/30/2018): I removed the previous answer that used sharding which slows down performance by a lot (see comments).

In this case, we want to concatenate a fixed number of batches. The issue is that these batches have variable sizes. For instance the dataset yields [1] and [2, 2] and we want to get [1, 2, 2] as the output.

Here a quick way to solve this is to create a new generator wrapping around the original one. The new generator will yield batched data. (Thanks to Guillaume for the idea)

---

Here is a full example where the generator yields [1], [2, 2], [3, 3, 3] and [4, 4, 4, 4].

def gen():
    for i in range(1, 5):
        yield [i] * i

def get_batch_gen(gen, batch_size=2):
    def batch_gen():
        buff = []
        for i, x in enumerate(gen()):
            if i % batch_size == 0 and buff:
                yield np.concatenate(buff, axis=0)
                buff = []
            buff += [x]

        if buff:
            yield np.concatenate(buff, axis=0)

    return batch_gen

# Create dataset from generator
batch_size = 2
dataset = tf.data.Dataset.from_generator(get_batch_gen(gen, batch_size),
                                         tf.int64, tf.TensorShape([None]))

# Create iterator from dataset
iterator = dataset.make_one_shot_iterator()
x = iterator.get_next()  # shape (None,)


with tf.Session() as sess:
    for i in range(2):
        print(sess.run(x))

This will print the following output:

[1 2 2]
[3 3 3 4 4 4 4]

Answer 2

What worked for me with tensorflow 2 :

1) setting the repeat() function when creating the dataset, therefore replacing

data = tf.data.Dataset.from_tensor_slices(x)

by

data = tf.data.Dataset.from_tensor_slices(x).repeat()

B) Passing the steps/epoch parameter to the fit method, therefore replacing

history = model.fit(dataset, epochs=EPOCHS, callbacks=[checkpoint_callback])

by

history = model.fit(dataset, epochs=EPOCHS, steps_per_epoch=data[0]/BUFFER_SIZE, callbacks=[checkpoint_callback])