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第
7
章
个潜在的单点故障,实现了网络隔离且缩短了模型训练任务的启动时间。接下来,
让我们设置输入和模型层:
input_ids = tf.keras.layers.Input(shape=(max_seq_length,),
name='input_ids',
dtype='int32')
input_mask = tf.keras.layers.Input(shape=(max_seq_length,),
name='input_mask',
dtype='int32')
embedding_layer = transformer_model.distilbert(input_ids,
attention_mask=input_mask)[0]
X = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(50,
return_sequences=True,
dropout=0.1,
recurrent_dropout=0.1))(embedding_layer)
X = tf.keras.layers.GlobalMaxPool1D()(X)
X = tf.keras.layers.Dense(50, activation='relu')(X)
X = tf.keras.layers.Dropout(0.2)(X)
X = ...