TensorFlow实现DeepFM

DeepFM原理讲解

代码

mport tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, optimizers, losses, regularizers
from tensorflow.keras.layers import Embedding, Dropout, Flatten, Dense, Input

import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'


class FM(layers.Layer):
    """
	Wide part
	"""

    def __init__(self, k):
        """
		:param k: the dimension of the latent vector
		"""
        super(FM, self).__init__()
        self.k = k

    def build(self, input_shape):
        self.w0 = self.add_weight(name='w0', shape=(1,), initializer=tf.zeros_initializer())
        self.w = self.add_weight(name='w', shape=(input_shape[-1], 1),
                                 regularizer=regularizers.l2(0.01))
        self.V = self.add_weight(name='V', shape=(self.k, input_shape[-1]),
                                  regularizer=regularizers.l2(0.01))

    def call(self, inputs):
        # first order
        first_order = self.w0 + tf.matmul(inputs, self.w)
        # second order
        second_order = 0.5 * tf.reduce_sum(
            tf.pow(tf.matmul(inputs, tf.transpose(self.V)), 2) -
            tf.matmul(tf.pow(inputs, 2), tf.pow(tf.transpose(self.V), 2)), axis=1, keepdims=True)
        return first_order + second_order


class MLP(layers.Layer):
    """
	Deep part
	"""

    def __init__(self, hidden_units, dropout_deep):
        """
		:param hidden_units: list of hidden layer units's numbers
		:param dropout_deep: dropout number
		"""
        super(MLP, self).__init__()
        self.dnn_network = [Dense(units=unit, activation='relu') for unit in hidden_units]
        self.dropout = Dropout(dropout_deep)

    def call(self, inputs):
        x = inputs
        for dnn in self.dnn_network:
            x = dnn(x)
        x = self.dropout(x)
        return x


class DeepFM(keras.Model):
    def __init__(self, feature_columns, k, hidden_units=None, dropout_deep=0.5):
        super(DeepFM, self).__init__()
        if hidden_units is None:
            hidden_units = [200, 200, 200]
        self.dense_feature_columns, self.sparse_feature_columns = feature_columns
        # embedding layer
        self.embed_layers = {
     
            'embed_' + str(i): Embedding(input_dim=feat['feat_num'], input_length=1,
                                         output_dim=feat['embed_dim'], embeddings_initializer='random_uniform')
            for i, feat in enumerate(self.sparse_feature_columns)
        }
        self.fm = FM(k)
        self.mlp = MLP(hidden_units, dropout_deep)
        self.dense = Dense(1, activation=None)
        self.w1 = self.add_weight(name='wide_weight', shape=(1,))
        self.w2 = self.add_weight(name='deep_weight', shape=(1,))

    def call(self, inputs):
        dense_inputs, sparse_inputs = inputs
        stack = dense_inputs
        for i in range(sparse_inputs.shape[1]):
            embed_i = self.embed_layers['embed_{}'.format(i)](sparse_inputs[:, i])
            stack = tf.concat([stack, embed_i], axis=-1)
        wide_outputs = self.fm(stack)
        deep_outputs = self.mlp(stack)
        deep_outputs = self.dense(deep_outputs)
        outputs = tf.nn.sigmoid(tf.add(self.w1 * wide_outputs, self.w2 * deep_outputs))
        return outputs

    def summary(self):
        dense_inputs = Input(shape=(len(self.dense_feature_columns),), dtype=tf.float32)
        sparse_inputs = Input(shape=(len(self.sparse_feature_columns),), dtype=tf.int32)
        keras.Model(inputs=[dense_inputs, sparse_inputs], outputs=self.call([dense_inputs, sparse_inputs])).summary()


def main():
    dense = [{
     'name': 'c1'}, {
     'name': 'c2'}, {
     'name': 'c3'}, {
     'name': 'c4'}]
    sparse = [{
     'feat_num': 100, 'embed_dim': 256}, {
     'feat_num': 200, 'embed_dim': 256}]
    columns = [dense, sparse]
    model = DeepFM(columns, 10, [200, 200, 200], 0.5)
    model.summary()


main()