o ={cã@sRdZddlZddlmmZddlmZddl m Z e dƒGdd„deƒƒZ dS)zContains the Reshape layer.éN)ÚLayer)Ú keras_exportzkeras.layers.ReshapecsDeZdZdZ‡fdd„Zdd„Zdd„Zdd „Z‡fd d „Z‡Z S) ÚReshapeaÛLayer that reshapes inputs into the given shape. Input shape: Arbitrary, although all dimensions in the input shape must be known/fixed. Use the keyword argument `input_shape` (tuple of integers, does not include the samples/batch size axis) when using this layer as the first layer in a model. Output shape: `(batch_size,) + target_shape` Example: >>> # as first layer in a Sequential model >>> model = tf.keras.Sequential() >>> model.add(tf.keras.layers.Reshape((3, 4), input_shape=(12,))) >>> # model.output_shape == (None, 3, 4), `None` is the batch size. >>> model.output_shape (None, 3, 4) >>> # as intermediate layer in a Sequential model >>> model.add(tf.keras.layers.Reshape((6, 2))) >>> model.output_shape (None, 6, 2) >>> # also supports shape inference using `-1` as dimension >>> model.add(tf.keras.layers.Reshape((-1, 2, 2))) >>> model.output_shape (None, 3, 2, 2) c s tƒjdi|¤Žt|ƒ|_dS)aCreates a `tf.keras.layers.Reshape` layer instance. Args: target_shape: Target shape. Tuple of integers, does not include the samples dimension (batch size). **kwargs: Any additional layer keyword arguments. N©)ÚsuperÚ__init__ÚtupleÚ target_shape)Úselfr Úkwargs©Ú __class__rú>lib/python3.10/site-packages/keras/layers/reshaping/reshape.pyr<szReshape.__init__c Cs²t|ƒ}d ||¡}d\}}t|ƒD]\}}|dkr)|dur!|}qtd|›dƒ‚||9}qtj|td}|durO|dksC||dkrGt|ƒ‚||||<|S||krWt|ƒ‚|S)aÕFind and replace a missing dimension in an output shape. This is a near direct port of the internal Numpy function `_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c` Args: input_shape: Shape of array being reshaped output_shape: Desired shape of the array with at most a single -1 which indicates a dimension that should be derived from the input shape. Returns: The new output shape with a -1 replaced with its computed value. Raises: ValueError: If the total array size of the output_shape is different than the input_shape, or more than one unknown dimension is specified. zNtotal size of new array must be unchanged, input_shape = {}, output_shape = {})éNrNzTThere must be at most one unknown dimension in output_shape. Received: output_shape=Ú.)Zdtype)ÚlistÚformatÚ enumerateÚ ValueErrorÚnpZprodÚint) r Ú input_shapeÚ output_shapeÚmsgZknownÚunknownÚindexZdimZoriginalrrrÚ_fix_unknown_dimensionGs4ÿþÿÿ  þzReshape._fix_unknown_dimensioncCspt |¡ ¡}d|dd…vr!|dg}|tdd„|jDƒƒ7}n|dg}|| |dd…|j¡7}t |¡S)Nrrcss |] }|dkr |ndVqdS)éÿÿÿÿNr)Ú.0ÚsrrrÚ ~s€ ÿz/Reshape.compute_output_shape..)ÚtfZ TensorShapeZas_listrr r)r rrrrrÚcompute_output_shapeys   ÿ ÿ zReshape.compute_output_shapecCs<t |t |¡df|j¡}t ¡s| | |j¡¡|S)Nr)r!ZreshapeÚshaper Zexecuting_eagerlyZ set_shaper")r ZinputsÚresultrrrÚcallˆsz Reshape.callcs0d|ji}tƒ ¡}tt| ¡ƒt| ¡ƒƒS)Nr )r rÚ get_configÚdictrÚitems)r ZconfigZ base_configr rrr&‘s  zReshape.get_config) Ú__name__Ú __module__Ú __qualname__Ú__doc__rrr"r%r&Ú __classcell__rrr rrs  2 r) r,ZnumpyrZtensorflow.compat.v2ÚcompatZv2r!Zkeras.engine.base_layerrZ tensorflow.python.util.tf_exportrrrrrrÚs