tf.split

Splits a tensor value into a list of sub tensors.

tf.split(
    value, num_or_size_splits, axis=0, num=None, name='split'
)

See also tf.unstack.

If num_or_size_splits is an int, then it splits value along the dimension axis into num_or_size_splits smaller tensors. This requires that value.shape[axis] is divisible by num_or_size_splits.

If num_or_size_splits is a 1-D Tensor (or list), then value is split into len(num_or_size_splits) elements. The shape of the i-th element has the same size as the value except along dimension axis where the size is num_or_size_splits[i].

For example:

x = tf.Variable(tf.random.uniform([5, 30], -1, 1))

# Split `x` into 3 tensors along dimension 1
s0, s1, s2 = tf.split(x, num_or_size_splits=3, axis=1)
tf.shape(s0).numpy()
array([ 5, 10], dtype=int32)

# Split `x` into 3 tensors with sizes [4, 15, 11] along dimension 1
split0, split1, split2 = tf.split(x, [4, 15, 11], 1)
tf.shape(split0).numpy()
array([5, 4], dtype=int32)
tf.shape(split1).numpy()
array([ 5, 15], dtype=int32)
tf.shape(split2).numpy()
array([ 5, 11], dtype=int32)

value The Tensor to split.
num_or_size_splits Either an int indicating the number of splits along axis or a 1-D integer Tensor or Python list containing the sizes of each output tensor along axis. If an int, then it must evenly divide value.shape[axis]; otherwise the sum of sizes along the split axis must match that of the value.
axis An int or scalar int32 Tensor. The dimension along which to split. Must be in the range [-rank(value), rank(value)). Defaults to 0.
num Optional, an int, used to specify the number of outputs when it cannot be inferred from the shape of size_splits.
name A name for the operation (optional).

if num_or_size_splits is an int returns a list of num_or_size_splits Tensor objects; if num_or_size_splits is a 1-D list or 1-D Tensor returns num_or_size_splits.get_shape[0] Tensor objects resulting from splitting value.

ValueError If num is unspecified and cannot be inferred.
ValueError If num_or_size_splits is a scalar Tensor.