我正在使用tensorflow.keras，想知道是否可以創(chuàng)建內(nèi)置Keras層的可重用塊。例如，我想在模型中的不同位置重復使用同一組層（能夠?qū)W習不同的權(quán)重）。我想在我的模型中的不同時間使用以下塊。keep_prob_=0.5input_features=Input(shape=(29, 1664))Imortant_features= SelfAttention(activation='tanh', kernel_regularizer=tf.keras.regularizers.l2(0.), kernel_initializer='glorot_uniform' (input_features)drop3=tf.keras.layers.Dropout(keep_prob_)(Imortant_features)Layer_norm_feat=tf.keras.layers.Add()([input_features, drop3])Layer_norm=tf.keras.layers.LayerNormalization(axis=-1)(Layer_norm_feat)ff_out=tf.keras.layers.Dense(Layer_norm.shape[2], activation='relu')(Layer_norm)ff_out=tf.keras.layers.Dense(Layer_norm.shape[2])(ff_out)drop4=tf.keras.layers.Dropout(keep_prob_)(ff_out)Layer_norm_input=tf.keras.layers.Add()([Layer_norm, drop4])Attention_block_out=tf.keras.layers.LayerNormalization(axis=-1)(Layer_norm_input)intraEpoch_att_block=tf.keras.Model(inputs=input_features, outputs=Attention_block_out)我已經(jīng)閱讀過有關(guān)在 Keras 中創(chuàng)建自定義層的內(nèi)容，但我發(fā)現(xiàn)文檔不夠清晰。我想重用該子模型，它能夠在 tensorflow.keras 中的單個功能 API 模型中學習不同的權(quán)重集。
1 回答
一只名叫tom的貓
TA貢獻1906條經(jīng)驗 獲得超3個贊
使用此代碼（我刪除了 SelfAttention，所以將其添加回來）：
import tensorflow as tf
class my_model(tf.keras.layers.Layer):
def __init__(self):
super(my_model, self).__init__()
keep_prob_=0.5
input_features=tf.keras.layers.Input(shape=(29, 1664))
drop3=tf.keras.layers.Dropout(keep_prob_)(input_features)
Layer_norm_feat=tf.keras.layers.Add()([input_features, drop3])
Layer_norm=tf.keras.layers.LayerNormalization(axis=-1)(Layer_norm_feat)
ff_out=tf.keras.layers.Dense(Layer_norm.shape[2], activation='relu')(Layer_norm)
ff_out=tf.keras.layers.Dense(Layer_norm.shape[2])(ff_out)
drop4=tf.keras.layers.Dropout(keep_prob_)(ff_out)
Layer_norm_input=tf.keras.layers.Add()([Layer_norm, drop4])
Attention_block_out=tf.keras.layers.LayerNormalization(axis=-1)(Layer_norm_input)
self.intraEpoch_att_block=tf.keras.Model(inputs=input_features, outputs=Attention_block_out)
def call(self, inp, training=False):
x = self.intraEpoch_att_block(inp)
return x
model1 = my_model()
model2 = my_model()
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