a Sic @sPdZddlmZddlmZddlmZddlmZedGdddeZ d S) z:Leaky version of a Rectified Linear Unit activation layer.)backend)Layer)tf_utils) keras_exportzkeras.layers.LeakyReLUcsDeZdZdZd fdd ZddZfddZejd d Z Z S) LeakyReLUa4Leaky version of a Rectified Linear Unit. It allows a small gradient when the unit is not active: ``` f(x) = alpha * x if x < 0 f(x) = x if x >= 0 ``` Usage: >>> layer = tf.keras.layers.LeakyReLU() >>> output = layer([-3.0, -1.0, 0.0, 2.0]) >>> list(output.numpy()) [-0.9, -0.3, 0.0, 2.0] >>> layer = tf.keras.layers.LeakyReLU(alpha=0.1) >>> output = layer([-3.0, -1.0, 0.0, 2.0]) >>> list(output.numpy()) [-0.3, -0.1, 0.0, 2.0] Input shape: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the batch axis) when using this layer as the first layer in a model. Output shape: Same shape as the input. Args: alpha: Float >= 0. Negative slope coefficient. Default to 0.3. 333333?c s>tjfi||dur(td|d|_t||_dS)NzSThe alpha value of a Leaky ReLU layer cannot be None, Expecting a float. Received: T)super__init__ ValueErrorsupports_maskingrcast_to_floatxalpha)selfr kwargs __class__^/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/layers/activation/leaky_relu.pyr =szLeakyReLU.__init__cCstj||jdS)N)r )rrelur )rinputsrrrcallGszLeakyReLU.callcs4dt|ji}t}tt|t|S)Nr )floatr r get_configdictlistitems)rconfig base_configrrrrJs zLeakyReLU.get_configcCs|S)Nr)r input_shaperrrcompute_output_shapeOszLeakyReLU.compute_output_shape)r) __name__ __module__ __qualname____doc__r rrrshape_type_conversionr __classcell__rrrrrs !  rN) r#kerasrkeras.engine.base_layerr keras.utilsr tensorflow.python.util.tf_exportrrrrrrs