tf.split

TensorFlow 2 version View source on GitHub

Splits a tensor into sub tensors.

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]

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).

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.

ValueError If num is unspecified and cannot be inferred.