我正在尝试弄清楚如何在Keras的函数式API中使用遮蔽层。
使用非函数式的Keras语法，我可以轻松创建一个GRU模型，像这样遮蔽所有零值：
model = tf.keras.Sequential()model.add( tf.keras.layers.Masking( mask_value = 0.0, input_shape = ( nTimeSteps, nVariables ) ) )model.add( tf.keras.layers.GRU( 32 ) )model.add( tf.keras.layers.Dense( 10, activation = "softmax" ) )opt = tf.keras.optimizers.SGD( learning_rate = 0.001 )model.compile( loss = 'categorical_crossentropy', optimizer = opt, metrics = ['accuracy'] )我尝试使用函数式API复制这个模型，目前看起来是这样的：
x = tf.keras.layers.Masking( mask_value = 0.0, input_shape = ( nTimeSteps, nVariables ) )x = tf.keras.layers.GRU( 32 )( x )z = tf.keras.layers.Dense( numberOfOutputs, activation = "softmax" )( x )model = tf.keras.Model( inputs = x, outputs = z )opt = tf.keras.optimizers.SGD( learning_rate = 0.001 )model.compile( loss = 'categorical_crossentropy', optimizer = opt, metrics = ['accuracy'] )然而，这不起作用 – 它会产生以下错误：
AttributeError: 'Masking' object has no attribute 'shape'在函数式API中使用遮蔽层正确的做法是什么？
回答：
你缺少了函数式API格式中的Input层。这里是一个示例代码：
nsamples = 10nTimeSteps, nVariables = 6, 4numberOfOutputs = 2X = np.random.randint(0,6, (nsamples ,nTimeSteps, nVariables))y = np.random.randint(0,numberOfOutputs, nsamples)inp = tf.keras.Input(shape = ( nTimeSteps, nVariables ))x = tf.keras.layers.Masking( mask_value = 0.0 )(inp)x = tf.keras.layers.GRU( 32 )( x )z = tf.keras.layers.Dense( numberOfOutputs, activation = "softmax" )( x )model = tf.keras.Model( inputs = inp, outputs = z )opt = tf.keras.optimizers.SGD( learning_rate = 0.001 )model.compile( loss = 'sparse_categorical_crossentropy', optimizer = opt, metrics = ['accuracy'] )model.fit(X,y, epochs=3)