a Sic@sjdZddlmmZddlmZddlmZddl m Z ddl m Z ddZ e d Gd d d eZdS) zSoftmax activation layer.N)backend)Layer)tf_utils) keras_exportcCs|tjkrtjjSdS)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)tffloat16min) tensor_typer [/var/www/html/django/DPS/env/lib/python3.9/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"tjfi|d|_||_dS)NT)super__init__supports_maskingaxis)selfrkwargs __class__r r rOszSoftmax.__init__NcCs|dur,dt||jt|j}||7}t|jttfrzt|jdkrft |tj ||jddSt j ||jddSt j ||jdS)Ng?T)rkeepdimsr)r) rcastdtyper isinstancertuplelistlenexpreduce_logsumexprsoftmax)rinputsmaskadderr r r callTsz Softmax.callcs0d|ji}t}tt|t|S)Nr)rr get_configdictritems)rconfig base_configrr r r&js  zSoftmax.get_configcCs|S)Nr )r input_shaper r r compute_output_shapeoszSoftmax.compute_output_shape)r)N) __name__ __module__ __qualname____doc__rr%r&rshape_type_conversionr, __classcell__r r rr r -s    r )r0tensorflow.compat.v2compatv2rkerasrkeras.engine.base_layerr keras.utilsr tensorflow.python.util.tf_exportrr r r r r r s