Tensorflow: Slicing a Tensor into overlapping blocks

Question

I have a 1D tensor that I wish to partition into overlapping blocks. I\'m thinking of something like: tensor = tf.constant([1, 2, 3, 4, 5, 6, 7])

Answer 1

I am not sure whether this question has been sufficiently answered, but you can use a python generator function to create overlapping windows:

def gen_batch():

# compute number of batches to emit
num_of_batches = round(((len(sequence) - batch_size) / stride))

# emit batches
for i in range(0, num_of_batches * stride, stride):
    result = np.array(sequence[i:i + batch_size])
    yield result

sequence = np.array[1,2,3,4,5,6,7,8,9]
batch_size = 3
stride = 2
gen = gen_batch()

print(next(gen))
[1,2,3]
print(next(gen))
[3,4,5]
...
print(next(gen))
[7,8,9]

Once you have defined your generator function, you can use TensorFlow's Dataset class to call each slice:

ds = tf.data.Dataset.from_generator(
gen_batch,
(tf.float64),
(tf.TensorShape([batch_size, dim_width])))

ds_out = ds.make_one_shot_iterator().get_next()

print(sess.run(ds_out)))
[1,2,3]
print(sess.run(ds_out)))
[3,4,5]
...
print(sess.run(ds_out)))
[7,8,9]