基于TensorFlow2.0构建W&D模型，Wide侧使用FTRL优化器，Deep侧使用Adam优化器，并使用自定义训练循环训练-DPDK中文网

下面是一个基于TensorFlow 2.0构建W&D模型，Wide侧使用FTRL优化器，Deep侧使用Adam优化器，并使用自定义训练循环进行训练的示例代码：

import tensorflow as tf
from tensorflow.keras.layers import Dense, Embedding, Concatenate
from tensorflow.keras.optimizers import Ftrl, Adam

# 定义Wide侧模型
def build_wide_model():
    input_wide = tf.keras.Input(shape=(wide_features,))
    wide_output = Dense(1, activation='sigmoid')(input_wide)
    model = tf.keras.Model(inputs=input_wide, outputs=wide_output)
    return model

# 定义Deep侧模型
def build_deep_model():
    input_deep = tf.keras.Input(shape=(deep_features,))
    embedding_layer = Embedding(input_dim=num_embeddings, output_dim=embedding_size)(input_deep)
    flattened_embedding = Flatten()(embedding_layer)
    deep_output = Dense(1, activation='sigmoid')(flattened_embedding)
    model = tf.keras.Model(inputs=input_deep, outputs=deep_output)
    return model

# 构建W&D模型
def build_wd_model():
    wide_model = build_wide_model()
    deep_model = build_deep_model()

    input_wide = tf.keras.Input(shape=(wide_features,))
    input_deep = tf.keras.Input(shape=(deep_features,))
    
    wide_output = wide_model(input_wide)
    deep_output = deep_model(input_deep)

    concat_output = Concatenate()([wide_output, deep_output])
    
    final_output = Dense(1, activation='sigmoid')(concat_output)

   model = tf.keras.Model(inputs=[input_wide, input_deep], outputs=final_output)
   return model

# 构建自定义训练循环
def train_custom_loop(model, optimizer, loss_fn, train_dataset):
    for epoch in range(num_epochs):
        epoch_loss = tf.keras.metrics.Mean()
        
        for batch_data in train_dataset:
            wide_data = batch_data['wide']
            deep_data = batch_data['deep']
            labels = batch_data['label']
            
            with tf.GradientTape() as tape:
                predictions = model([wide_data, deep_data])
                loss_value = loss_fn(labels, predictions)
            
            grads = tape.gradient(loss_value, model.trainable_variables)
            optimizer.apply_gradients(zip(grads, model.trainable_variables))
            
            epoch_loss.update_state(loss_value)
        
        print(f'Epoch {epoch+1}, Loss: {epoch_loss.result()}')

# 创建W&D模型实例
model = build_wd_model()

# 定义损失函数和优化器
loss_fn = tf.keras.losses.BinaryCrossentropy()
wide_optimizer = Ftrl(learning_rate=0.01)
deep_optimizer = Adam(learning_rate=0.001)

# 构建训练数据集（假设已经准备好了）
train_dataset = prepare_train_dataset()

# 使用自定义训练循环进行模型训练
train_custom_loop(model, [wide_optimizer, deep_optimizer], loss_fn, train_dataset)

这是一个基本的示例，你可能需要根据你的具体需求进行适当的修改和调整。

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