o Me@sjdZddlmmZddlmZddlmZddl m Z ddl m Z ddZ e d Gd d d eZdS) zSoftmax activation layer.N)backend)Layer)tf_utils) keras_exportcCs|tjkr tjjSdS)aLarge negative number as Tensor. This function is necessary because the standard value for epsilon in this module (-1e9) cannot be represented using tf.float16 Args: tensor_type: a dtype to determine the type. Returns: a large negative number. ge)tfZfloat16min)Z tensor_typerOD:\Projects\ConvertPro\env\Lib\site-packages\keras/layers/activation/softmax.py_large_compatible_negatives r zkeras.layers.SoftmaxcsFeZdZdZd fdd Zd ddZfdd Zejd d Z Z S)SoftmaxaSoftmax activation function. Example without mask: >>> inp = np.asarray([1., 2., 1.]) >>> layer = tf.keras.layers.Softmax() >>> layer(inp).numpy() array([0.21194157, 0.5761169 , 0.21194157], dtype=float32) >>> mask = np.asarray([True, False, True], dtype=bool) >>> layer(inp, mask).numpy() array([0.5, 0. , 0.5], dtype=float32) 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 shape as the input. Args: axis: Integer, or list of Integers, axis along which the softmax normalization is applied. Call arguments: inputs: The inputs, or logits to the softmax layer. mask: A boolean mask of the same shape as `inputs`. Defaults to `None`. The mask specifies 1 to keep and 0 to mask. Returns: softmaxed output with the same shape as `inputs`. c s"tjdi|d|_||_dS)NTr)super__init__Zsupports_maskingaxis)selfrkwargs __class__rr rOs zSoftmax.__init__NcCs|durdt||jt|j}||7}t|jttfr=t|jdkr3t |tj ||jddSt j ||jddSt j ||jdS)Ng?T)rZkeepdimsr)r) rcastZdtyper isinstancertuplelistlenexpZreduce_logsumexprZsoftmax)rZinputsmaskZadderrrr callTsz Softmax.callcs0d|ji}t}tt|t|S)Nr)rr get_configdictritems)rconfigZ base_configrrr rjs  zSoftmax.get_configcCs|SNr)rZ input_shaperrr compute_output_shapeoszSoftmax.compute_output_shape)r r!) __name__ __module__ __qualname____doc__rrrrZshape_type_conversionr" __classcell__rrrr r -s  r )r&Ztensorflow.compat.v2compatv2rZkerasrZkeras.engine.base_layerrZ keras.utilsrZ tensorflow.python.util.tf_exportrr r rrrr s