a Sic@sRdZddlZddlmmZddlmZddl m Z e dGdddeZ dS)zContains the Reshape layer.N)Layer) keras_exportzkeras.layers.ReshapecsDeZdZdZfddZddZddZdd Zfd d ZZ S) ReshapeaLayer 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 tjfi|t||_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)super__init__tuple target_shape)selfrkwargs __class__Z/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/layers/reshaping/reshape.pyr<szReshape.__init__c Cst|}d||}d\}}t|D]8\}}|dkrT|durB|}q\td|dq$||9}q$tj|td}|dur|dks||dkrt|||||<n||krt||S)aFind 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 = {})NrNzTThere must be at most one unknown dimension in output_shape. Received: output_shape=.)dtype)listformat enumerate ValueErrornpprodint) r input_shape output_shapemsgknownunknownindexdimoriginalr r r_fix_unknown_dimensionGs0 zReshape._fix_unknown_dimensioncCspt|}d|ddvrB|dg}|tdd|jD7}n$|dg}|||dd|j7}t|S)Nrrcss|]}|dkr|ndVqdS)Nr ).0sr r r ~sz/Reshape.compute_output_shape..)tf TensorShapeas_listrrr!)r rrr r rcompute_output_shapeys    zReshape.compute_output_shapecCs<t|t|df|j}ts8|||j|S)Nr)r&reshapeshaperexecuting_eagerly set_shaper))r inputsresultr r rcallsz Reshape.callcs0d|ji}t}tt|t|S)Nr)rr get_configdictritems)r config base_configr r rr1s  zReshape.get_config) __name__ __module__ __qualname____doc__rr!r)r0r1 __classcell__r r r rrs  2 r) r9numpyrtensorflow.compat.v2compatv2r&keras.engine.base_layerr tensorflow.python.util.tf_exportrrr r r rs