keras模型代码

import tensorflow as tf
from tensorflow.keras.layers import Layer
from tensorflow.keras.layers import Input, Dense

class FM_layer(Layer):
    def __init__(self, k, w_reg, v_reg):
        super().__init__()
        self.k = k
        self.w_reg = w_reg
        self.v_reg = v_reg

    def build(self, input_shape):
        self.w0 = self.add_weight(name='w0', shape=(1,),
                                  initializer=tf.zeros_initializer(),
                                  trainable=True,)
        self.w = self.add_weight(name='w', shape=(input_shape[-1], 1),
                                 initializer=tf.random_normal_initializer(),
                                 trainable=True,
                                 regularizer=tf.keras.regularizers.l2(self.w_reg))
        self.v = self.add_weight(name='v', shape=(input_shape[-1], self.k),
                                 initializer=tf.random_normal_initializer(),
                                 trainable=True,
                                 regularizer=tf.keras.regularizers.l2(self.v_reg))

    def call(self, inputs, **kwargs):
        linear_part = tf.matmul(inputs, self.w) + self.w0   #shape:(batchsize, 1)

        inter_part1 = tf.pow(tf.matmul(inputs, self.v), 2)  #shape:(batchsize, self.k)
        inter_part2 = tf.matmul(tf.pow(inputs, 2), tf.pow(self.v, 2)) #shape:(batchsize, self.k)
        inter_part = 0.5*tf.reduce_sum(inter_part1 - inter_part2, axis=-1, keepdims=True) #shape:(batchsize, 1)

        output = linear_part + inter_part
        return output

class Dense_layer(Layer):
    def __init__(self, hidden_units, output_dim, activation):
        super().__init__()
        self.hidden_units = hidden_units
        self.output_dim = output_dim
        self.activation = activation

        self.hidden_layer = [Dense(i, activation=self.activation) for i in self.hidden_units]
        self.output_layer = Dense(self.output_dim, activation=None)

    def call(self, inputs):
        x = inputs
        for layer in self.hidden_layer:
            x = layer(x)
        output = self.output_layer(x)
        return output


from layer import FM_layer, Dense_layer

import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.layers import Embedding

class DeepFM(Model):
    def __init__(self, feature_columns, k, w_reg, v_reg, hidden_units, output_dim, activation):
        super().__init__()
        self.dense_feature_columns, self.sparse_feature_columns = feature_columns

        # 将类别特征从onehot_dim转换成embed_dim
        self.embed_layers = {
            'embed_' + str(i): Embedding(feat['feat_onehot_dim'], feat['embed_dim'])
             for i, feat in enumerate(self.sparse_feature_columns)
        }

        self.FM = FM_layer(k, w_reg, v_reg)
        self.Dense = Dense_layer(hidden_units, output_dim, activation)

    def call(self, inputs):
        dense_inputs, sparse_inputs = inputs[:, :13], inputs[:, 13:]

        # embedding
        sparse_embed = tf.concat([self.embed_layers['embed_{}'.format(i)](sparse_inputs[:, i])
                                  for i in range(sparse_inputs.shape[1])], axis=1)
        x = tf.concat([dense_inputs, sparse_embed], axis=-1)

        fm_output = self.FM(x)
        dense_output = self.Dense(x)
        output = tf.nn.sigmoid(0.5*(fm_output + dense_output))
        return output

#pandas 是基于NumPy的一种工具，该工具是为解决数据分析任务而创建的。
import pandas as pd 
#读取数据集文件
    #因为没有表头，若想要把第一行作为数据需要加上header=None
data = pd.read_csv("./credit-a.csv",header=None)
#data.head()  #可以输出前5行数据查看 
data.iloc[:,-1].value_counts()        
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版权声明：本文为CSDN博主「有梦想的Programmer」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/qq_45703999/article/details/113919122
import tensorflow as tf
#构建顺序模型并为其添加层
model=tf.keras.Sequential(
    [
        #第二层：Dense隐藏层，第1个隐藏层需要指定输入数据维度为15，单元个数可自定义
        tf.keras.layers.Dense(4,input_shape=(15,),activation="relu"),
        #第三层：Dense隐藏层，无需指定输入数据维度
        tf.keras.layers.Dense(4,activation="relu"),
        #第四层：Dense输出层，单元个数为1(因为目标值就是1维),用sigmoid函数计算出最终概率
        tf.keras.layers.Dense(1,activation="sigmoid")
    ]
)
info = model.summary()  #可查看当前模型信息
print(info)
# 为指定模型训练用的优化算法和损失函数
model.compile(
    optimizer="adam", #利用adam优化算法
    loss="binary_crossentropy", #利用binary_crossentropy作损失函数
    metrics=["acc"]   #显示在运行过程中的正确率情况
)
# 开始训练，指定数据集的input_x、output_y、训练次数epochs=100
his = model.fit(x=x,y=y,epochs=100)  

#his 变量记录了训练好的模型的信息
his.history.keys()     #history是一个字典数据

import matplotlib.pyplot as plt
# 绘制loss曲线
plt.plot(his.epoch,his.history.get('loss'))
# 绘制acc曲线
plt.plot(his.epoch,his.history.get('acc'))

# 用训练好的模型做一次测试（这里取训练数据集中前10行+中间15列数据作为测试数据）
test = data.iloc[:10,:-1]
model.predict(test)