tf.split | TensorFlow v2.0.0 (original) (raw)
tf.split
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Splits a tensor into sub tensors.
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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 dimensionaxis 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_splitsand 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 dividevalue.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 Tensorobjects; if num_or_size_splits is a 1-D Tensor returnsnum_or_size_splits.get_shape[0] Tensor objects resulting from splittingvalue. |
| Raises | |
|---|---|
| ValueError | If num is unspecified and cannot be inferred. |