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]
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.
|