本文主要是介绍DeepFM算法代码，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
以下代码均采用Tensorflow1.15版本
数据集私聊我
import tensorflow as tf
import numpy as np
import pandas as pd# 定义特征列
def get_feature_columns():# 假设 Criteo 数据集有 10 个数值特征和 10 个类别特征numerical_feature_columns = [tf.feature_column.numeric_column("num_feature_{}".format(i)) for i in range(10)]categorical_feature_columns = [tf.feature_column.categorical_column_with_hash_bucket("cat_feature_{}".format(i), hash_bucket_size=100) for i in range(10)]return numerical_feature_columns + categorical_feature_columns# 定义 DeepFM 模型
def deep_fm_model(features, labels, mode):# 嵌入层embedding_list = []for column in get_feature_columns():if isinstance(column, tf.feature_column.categorical_column_with_hash_bucket):embedding = tf.feature_column.embedding_column(column, dimension=8)embedding_list.append(embedding)# FM 部分fm_input = tf.concat([tf.feature_column.input_layer(features, column) for column in get_feature_columns()], axis=1)linear_part = tf.layers.dense(fm_input, 1)sum_square = tf.square(tf.reduce_sum(fm_input, axis=1))square_sum = tf.reduce_sum(tf.square(fm_input), axis=1)fm_part = 0.5 * tf.reduce_sum(sum_square - square_sum, axis=1, keepdims=True)# Deep 部分deep_input = tf.concat([tf.feature_column.input_layer(features, column) for column in get_feature_columns()], axis=1)deep_hidden_1 = tf.layers.dense(deep_input, 128, activation=tf.nn.relu)deep_hidden_2 = tf.layers.dense(deep_hidden_1, 64, activation=tf.nn.relu)deep_output = tf.layers.dense(deep_hidden_2, 1)# 合并combined_output = linear_part + fm_part + deep_output# 预测和损失if mode == tf.estimator.ModeKeys.PREDICT:predictions = {'predictions': combined_output}return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)loss = tf.losses.mean_squared_error(labels, combined_output)# 优化器optimizer = tf.train.AdamOptimizer(learning_rate=0.001)# 训练和评估操作if mode == tf.estimator.ModeKeys.TRAIN:train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)if mode == tf.estimator.ModeKeys.EVAL:eval_metric_ops = {'mse': tf.metrics.mean_squared_error(labels, combined_output)}return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)# 输入函数
def input_fn(data_path, batch_size):data = pd.read_csv(data_path)labels = data['label']features = data.drop('label', axis=1)dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))dataset = dataset.shuffle(buffer_size=1000).batch(batch_size).repeat()iterator = dataset.make_one_shot_iterator()features, labels = iterator.get_next()return features, labels# 训练和评估
def train_and_evaluate():# 创建 Estimatorestimator = tf.estimator.Estimator(model_fn=deep_fm_model,model_dir='your_model_dir')# 训练estimator.train(input_fn=lambda: input_fn('train_data_path.csv', batch_size=128),steps=1000)# 评估estimator.evaluate(input_fn=lambda: input_fn('eval_data_path.csv', batch_size=128))if __name__ == '__main__':train_and_evaluate()
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