tf.split

Splits a tensor into sub tensors.

Aliases:

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

If num_or_size_splits is an integer, then value is split along dimension axis into num_split smaller tensors. This requires that num_split evenly divides value.shape[axis].

If num_or_size_splits is a 1-D Tensor (or list), we call it size_splits and value is split into len(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 size_splits[i].

For example:

# 'value' is a tensor with shape [5, 30]
# Split 'value' into 3 tensors with sizes [4, 15, 11] along dimension 1
split0, split1, split2 = tf.split(value, [4, 15, 11], 1)
tf.shape(split0)  # [5, 4]
tf.shape(split1)  # [5, 15]
tf.shape(split2)  # [5, 11]
# Split 'value' into 3 tensors along dimension 1
split0, split1, split2 = tf.split(value, num_or_size_splits=3, axis=1)
tf.shape(split0)  # [5, 10]

Args:

value: The Tensor to split.
num_or_size_splits: Either an integer indicating the number of splits along split_dim or a 1-D integer Tensor or Python list containing the sizes of each output tensor along split_dim. If a scalar then it must evenly divide value.shape[axis]; otherwise the sum of sizes along the split dimension must match that of the value.
axis: An integer or scalar int32 Tensor. The dimension along which to split. Must be in the range [-rank(value), rank(value)). Defaults to 0.
num: Optional, 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).

Returns:

if num_or_size_splits is a scalar returns num_or_size_splits Tensor objects; if num_or_size_splits is a 1-D Tensor returns num_or_size_splits.get_shape[0] Tensor objects resulting from splitting value.

Raises:

ValueError: If num is unspecified and cannot be inferred.