B «»ˆd ã@s^dZddlZddlmmZddlmZddlm Z ddl m Z e dƒGdd„deƒƒZ dS) zContains the Permute layer.éN)ÚLayer)Ú InputSpec)Ú keras_exportzkeras.layers.Permutecs<eZdZdZ‡fdd„Zdd„Zdd„Z‡fdd „Z‡ZS) ÚPermuteafPermutes the dimensions of the input according to a given pattern. Useful e.g. connecting RNNs and convnets. Example: ```python model = Sequential() model.add(Permute((2, 1), input_shape=(10, 64))) # now: model.output_shape == (None, 64, 10) # note: `None` is the batch dimension ``` Args: dims: Tuple of integers. Permutation pattern does not include the samples dimension. Indexing starts at 1. For instance, `(2, 1)` permutes the first and second dimensions of the input. Input shape: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. Output shape: Same as the input shape, but with the dimensions re-ordered according to the specified pattern. c s^tƒjf|Žt|ƒ|_t|ƒttdt|ƒdƒƒkrDtd|›ƒ‚t t|jƒdd|_ dS)NézˆInvalid permutation argument `dims` for Permute Layer. The set of indices in `dims` must be consecutive and start from 1. Received dims=)Úndim) ÚsuperÚ__init__ÚtupleÚdimsÚsortedÚlistÚrangeÚlenÚ ValueErrorrZ input_spec)Úselfr Úkwargs)Ú __class__©úP/var/www/html/venv/lib/python3.7/site-packages/keras/layers/reshaping/permute.pyr <s   zPermute.__init__cCsNt |¡ ¡}t |¡}x*t|jƒD]\}}||}|||d<q$Wt |¡S)Nr)ÚtfZ TensorShapeÚas_listÚcopyÚ enumerater )rZ input_shapeZ output_shapeÚiÚdimZ target_dimrrrÚcompute_output_shapeGs  zPermute.compute_output_shapecCstj|d|jdS)N)r)Zperm)rZ transposer )rÚinputsrrrÚcallOsz Permute.callcs0d|ji}tƒ ¡}tt| ¡ƒt| ¡ƒƒS)Nr )r rÚ get_configÚdictr Úitems)rÚconfigZ base_config)rrrrRs  zPermute.get_config) Ú__name__Ú __module__Ú __qualname__Ú__doc__r rrrÚ __classcell__rr)rrrs  r) r&rZtensorflow.compat.v2ÚcompatZv2rZkeras.engine.base_layerrZkeras.engine.input_specrZ tensorflow.python.util.tf_exportrrrrrrÚs