import tensorflow as tf from tensorflow.keras import layers 从Keras的layers.advanced_activations模块中导入LeakyReLU函数: 然而,需要注意的是,在较新版本的Keras(特别是在TensorFlow 2.x中)中,keras.layers.advanced_activations模块可能已经被弃用或移动。对于LeakyReLU激活函数,你应该直接从tensorflow.keras.layers...
# 需要导入模块: from keras.layers import advanced_activations [as 别名]# 或者: from keras.layers.advanced_activations importLeakyReLU[as 别名]defbuild_discriminator(self):defd_layer(layer_input, filters, f_size=4, normalization=True):"""Discriminator layer"""d = Conv2D(filters, kernel_size=f...
# 需要导入模块: from keras.layers import advanced_activations [as 别名]# 或者: from keras.layers.advanced_activations importELU[as 别名]defget_activation_layer(activation):ifactivation =='LeakyReLU':returnLeakyReLU()ifactivation =='PReLU':returnPReLU()ifactivation =='ELU':returnELU()ifactivation...
advanced_activations层是一种激活函数层,常见的激活函数有ReLU、LeakyReLU、sigmoid、tanh等。这些激活函数可以引入非线性,增强模型的表达能力。 添加dropout层的代码示例(使用Keras库): 代码语言:txt 复制 from tensorflow.keras.layers import Dropout # 假设存在advanced_activations层的情况下 # 先添加advanced_...
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# 需要导入模块: from keras.layers import advanced_activations [as 别名]# 或者: from keras.layers.advanced_activations importThresholdedReLU[as 别名]defget_activation_layer(activation):ifactivation =='LeakyReLU':returnLeakyReLU()ifactivation =='PReLU':returnPReLU()ifactivation =='ELU':returnELU()...
self.add(ELU())elifself._config.mlp_activation =='leaky_relu': self.add(LeakyReLU())elifself._config.mlp_activation =='prelu': self.add(PReLU())else: self.add(Activation(self._config.mlp_activation)) self.add(Dropout(0.5)) 开发者ID:mateuszmalinowski,项目名称:visual_turing_test-tutorial...