o i e@sjdZddlmmZddlmZddlmZddl m Z ddl m Z ddZ e d Gd d d eZdS) zSoftmax activation layer.N)backend)Layer)tf_utils) keras_exportcCs|tjkr tjjdSdS)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. g@ge)tffloat16min) tensor_typer R/var/www/myenv/lib/python3.10/site-packages/keras/src/layers/activation/softmax.py_large_compatible_negatives  r zkeras.layers.SoftmaxcsFeZdZdZd fdd Zd ddZfdd Zejd d Z Z S)Softmaxa Softmax 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`. The mask specifies 1 to keep and 0 to mask. Defaults to `None`. Returns: Softmaxed output with the same shape as `inputs`. c s"tjdi|d|_||_dS)NTr )super__init__supports_maskingaxis)selfrkwargs __class__r r rSs zSoftmax.__init__NcCs|durdt||jt|j}||7}t|jttfr=t|jdkr3t |tj ||jddSt j ||jddSt j ||jdS)Ng?T)rkeepdimsr)r) rcastdtyper isinstancertuplelistlenexpreduce_logsumexprsoftmax)rinputsmaskadderr r r callXsz Softmax.callcs0d|ji}t}tt|t|S)Nr)rr get_configdictritems)rconfig base_configrr r r&ns  zSoftmax.get_configcCs|SNr )r input_shaper r r compute_output_shapesszSoftmax.compute_output_shape)rr+) __name__ __module__ __qualname____doc__rr%r&rshape_type_conversionr- __classcell__r r rr r 0s!  r )r1tensorflow.compat.v2compatv2r keras.srcrkeras.src.engine.base_layerrkeras.src.utilsr tensorflow.python.util.tf_exportrr r r r r r s