多模态：结构化数据（表格数据）+文本数据（或图片、音频）进行特征融合
多目标：共享分特征处理部分，然后分别再次全连接激活，输出多个训练目标
本文针对如上两个特点，基于TensorFlow2.0，实现了该网络，如下代码供参考。
# -*- coding: utf-8 -*-
"""
@Time : 2022/7/29 16:21
@Author: Breeze
@File : vsn+transfomer.py
"""
import pandas as pd
import os
import sys
import json_WORK_DIR = os.path.split(os.path.realpath(__file__))[0]
sys.path.append(os.path.join(_WORK_DIR, '..'))
import time
import numpy as np
import tensorflow as tf
import tensorflow.keras.backend as K
from tensorflow.keras.layers import Layerfrom focal_loss import sparse_categorical_focal_loss
seed = 2022
np.random.seed(seed)
tf.random.set_seed(seed)
print(tf.__version__)# 画出模型，需要GraphViz包。
from tensorflow.keras.utils import plot_model# plot_model(NN, to_file='NN.png')
def WEIGHT_COLUMN_NAME_map(x):
return 1 if x >= 1 else 0with open('added_tokens.json', encoding='utf-8') as f:
kvs = json.load(f) def get_default_value(x):
if kvs.get(x):
return kvs.get(x) - 28995
return 0 class GatedLinearUnit(tf.keras.layers.Layer):
def __init__(self, units):
super(GatedLinearUnit, self).__init__()
self.linear = tf.keras.layers.Dense(units)
self.sigmoid = tf.keras.layers.Dense(units, activation='sigmoid') def get_config(self):
config = super(GatedLinearUnit, self).get_config()
config["linear"] = self.linear
config["sigmoid"] = self.sigmoid
return config def call(self, inputs, training=None):
return self.linear(inputs) * self.sigmoid(inputs) class GatedResidualNetwork(tf.keras.layers.Layer):
def __init__(self, units, dropout_rate):
super(GatedResidualNetwork, self).__init__()
self.units = units
self.elu_dense = tf.keras.layers.Dense(units, activation='elu')
self.linear_dense = tf.keras.layers.Dense(units)
self.dropout = tf.keras.layers.Dropout(dropout_rate)
self.gated_linear_unit = GatedLinearUnit(units)
self.layer_norm = tf.keras.layers.LayerNormalization()
self.project = tf.keras.layers.Dense(units) def get_config(self):
config = super(GatedResidualNetwork, self).get_config()
config["units"] = self.units
config["elu_dense"] = self.elu_dense
config["linear_dense"] = self.linear_dense
config["dropout"] = self.dropout
config["gated_linear_unit"] = self.gated_linear_unit
config["layer_norm"] = self.layer_norm
config["project"] = self.project
return config def call(self, inputs, training=None):
x = self.elu_dense(inputs)
x = self.linear_dense(x)
x = self.dropout(x, training=training)
if inputs.shape[-1] != self.units:
inputs = self.project(inputs)
else:
self.project = None
x = inputs + self.gated_linear_unit(x) x = self.layer_norm(x)
return x class VariableSelection(tf.keras.layers.Layer):
def __init__(self,
category_features,
numeric_features,
encoding_size,
dropout_rate,
category_feature_vocabulary,
project_dim
):
super(VariableSelection, self).__init__()
# Create embedding layer for each category features
self.category_nums = len(category_features)
self.embeddings = []
# share embedding layers in multi-object learning
for feature_name in category_features:
feature_value = category_feature_vocabulary[feature_name]
print('构建embedding', feature_name, len(feature_value) + 1, encoding_size)
self.embeddings.append(
tf.keras.layers.Embedding(
input_dim=len(feature_value) + 1,
output_dim=encoding_size)
) # Project the numeric feature to encoding_size using linear transformation
self.project_layers = []
for feature_name in numeric_features:
self.project_layers.append(tf.keras.layers.Dense(units=encoding_size)) self.features = category_features + numeric_features
self.category_features = category_features
self.numeric_features = numeric_features num_features = len(category_features) + len(numeric_features)
# Create a GRN for each feature independently
self.grns = list()
for idx in range(num_features):
grn = GatedResidualNetwork(encoding_size, dropout_rate)
self.grns.append(grn) # Create a GRN for the concatenation of all the features
self.grn_concat = GatedResidualNetwork(encoding_size, dropout_rate)
self.softmax = tf.keras.layers.Dense(units=num_features, activation="softmax") # project output to project_dim
# tf.nn.sigmoid(output)
# self.transfer_layer = tf.keras.layers.Dense(project_dim, activation="sigmoid")
self.transfer_layer = tf.keras.layers.Dense(project_dim, activation="softmax") def get_config(self):
config = super(VariableSelection, self).get_config()
config["grns"] = self.grns
config["grn_concat"] = self.grn_concat
config["softmax"] = self.softmax
return config def call(self, inputs, training=None, mask=None):
encoded_features = []
# print('inputs:', inputs)
for i, feature_name in enumerate(self.features):
if feature_name in self.category_features:
feature_embedding = self.embeddings[i]
input_feature = inputs[:, i]
# print('encoded_feature', i, feature_name, feature_embedding.input_dim,feature_embedding.output_dim)
encoded_feature = feature_embedding(input_feature)
else:
encoded_feature = tf.expand_dims(inputs[:, i], -1)
encoded_feature = self.project_layers[i - self.category_nums](encoded_feature)
encoded_features.append(encoded_feature) v = tf.keras.layers.concatenate(encoded_features)
# actually equal to attention fusion network
v = self.grn_concat(v, training=training)
# 在最后增加一维数据
v = tf.expand_dims(self.softmax(v), axis=-1) x = []
for idx, input in enumerate(encoded_features):
x.append(self.grns[idx](input, training=training)) x = tf.stack(x, axis=1) # 横向维度扩展
# output shape [batch_size, encoding_size]
outputs = tf.squeeze(tf.matmul(v, x, transpose_a=True), axis=1)
# transfer to project_dim
outputs = self.transfer_layer(outputs, training=training)
return outputs class MultiHeadSelfAttention(tf.keras.layers.Layer):
"""多头Attention"""
def __init__(self, embed_dim, num_heads=8):
super(MultiHeadSelfAttention, self).__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
if embed_dim % num_heads != 0:
raise ValueError(
f"embedding dimension = {embed_dim} should be divisible by number of heads = {num_heads}"
)
self.projection_dim = embed_dim // num_heads
self.query_dense = tf.keras.layers.Dense(embed_dim)
self.key_dense = tf.keras.layers.Dense(embed_dim)
self.value_dense = tf.keras.layers.Dense(embed_dim)
self.combine_heads = tf.keras.layers.Dense(embed_dim) def attention(self, query, key, value):
score = tf.matmul(query, key, transpose_b=True)
dim_key = tf.cast(tf.shape(key)[-1], tf.float32)
scaled_score = score / tf.math.sqrt(dim_key)
weights = tf.nn.softmax(scaled_score, axis=-1)
output = tf.matmul(weights, value)
return output, weights def separate_heads(self, x, batch_size):
x = tf.reshape(x, (batch_size, -1, self.num_heads, self.projection_dim))
return tf.transpose(x, perm=[0, 2, 1, 3]) def call(self, inputs):
# x.shape = [batch_size, seq_len, embedding_dim]
batch_size = tf.shape(inputs)[0]
query = self.query_dense(inputs) # (batch_size, seq_len, embed_dim)
key = self.key_dense(inputs) # (batch_size, seq_len, embed_dim)
value = self.value_dense(inputs) # (batch_size, seq_len, embed_dim)
query = self.separate_heads(
query, batch_size
) # (batch_size, num_heads, seq_len, projection_dim)
key = self.separate_heads(
key, batch_size
) # (batch_size, num_heads, seq_len, projection_dim)
value = self.separate_heads(
value, batch_size
) # (batch_size, num_heads, seq_len, projection_dim)
attention, weights = self.attention(query, key, value)
attention = tf.transpose(
attention, perm=[0, 2, 1, 3]
) # (batch_size, seq_len, num_heads, projection_dim)
concat_attention = tf.reshape(
attention, (batch_size, -1, self.embed_dim)
) # (batch_size, seq_len, embed_dim)
output = self.combine_heads(
concat_attention
) # (batch_size, seq_len, embed_dim)
return output class TransformerBlock(tf.keras.layers.Layer):
"""Transformer的Encoder部分"""
def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):
super(TransformerBlock, self).__init__()
self.att = MultiHeadSelfAttention(embed_dim, num_heads)
self.ffn = tf.keras.Sequential(
[tf.keras.layers.Dense(ff_dim, activation="relu"), tf.keras.layers.Dense(embed_dim), ]
)
self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.dropout1 = tf.keras.layers.Dropout(rate)
self.dropout2 = tf.keras.layers.Dropout(rate) def call(self, inputs, training):
attn_output = self.att(inputs)
attn_output = self.dropout1(attn_output, training=training)
out1 = self.layernorm1(inputs + attn_output)
ffn_output = self.ffn(out1)
ffn_output = self.dropout2(ffn_output, training=training)
return self.layernorm2(out1 + ffn_output) class TokenAndPositionEmbedding(tf.keras.layers.Layer):
"""Transformer输入的编码层"""
def __init__(self, maxlen, vocab_size, embed_dim):
super(TokenAndPositionEmbedding, self).__init__()
self.token_emb = tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embed_dim)
self.pos_emb = tf.keras.layers.Embedding(input_dim=maxlen, output_dim=embed_dim) def call(self, x):
maxlen = tf.shape(x)[-1]
positions = tf.range(start=0, limit=maxlen, delta=1)
positions = self.pos_emb(positions)
x = self.token_emb(x)
return x + positions class Trainer(tf.keras.Model):
def __init__(self, pretrain_path,
train_data_path,
test_data_path,
save_dir,
labels=[],
category_features=[],
numeric_features=[],
encoding_size=10,
dropout_rate=0.5,
category_feature_vocabulary=[],
project_dim=10,
max_len=128
):
# `super(YourClass, self).__init__()`. Always start with this line.
super(Trainer, self).__init__()
self.max_len = max_len
self.save_dir = save_dir
self.embed_dim = 32 # Embedding size for each token
self.num_heads = 2 # Number of attention heads
self.ff_dim = 32 # Hidden layer size in feed forward network inside transformer
self.vocab_size = 400
self.optimizer = None def click_map(x):
# input_embedding = tf.nn.embedding_lookup(W, input_x)
words = str(x).split(',')
words_idx = list(map(lambda word:get_default_value(word),words)) pad_sequences =tf.keras.preprocessing.sequence.pad_sequences([words_idx],
maxlen=max_len,
dtype='float64',
padding='post',
truncating='pre',
value=0.) return pad_sequences[0].reshape(1,max_len)
# 数据的加载
train_data = pd.read_csv(train_data_path)
self.x_train = train_data[category_features + numeric_features].values
self.x_train_txt = train_data['last_click'].apply(click_map).values
# df1 = self.x_train_txt.apply(pd.Series, index=[f'col{i}' for i in range(max_len)])
self.x_train_txt = np.array([e for e in self.x_train_txt ]).reshape(len(train_data), max_len)
self.x_train = np.concatenate([self.x_train, self.x_train_txt], axis=1)
self.y_train = train_data[labels[0]].values
self.y2_train = train_data[labels[1]].apply(WEIGHT_COLUMN_NAME_map).values
# print(train_data.info()) test_data = pd.read_csv(test_data_path)
self.x_test = test_data[category_features + numeric_features]
self.x_test_txt = test_data['last_click'].apply(click_map).values
self.x_test_txt = np.array([e for e in self.x_test_txt]).reshape(len(self.x_test_txt), max_len)
self.x_test = np.concatenate([self.x_test, self.x_test_txt], axis=1) self.y_test = test_data[labels[0]].values
self.y2_test = test_data[labels[1]].apply(WEIGHT_COLUMN_NAME_map).values
# print(test_data.info()) del train_data, test_data
#
self.train_loss_1 = tf.keras.metrics.Mean(name='train_loss_1')
self.train_auc_1 = tf.keras.metrics.AUC(name='train_auc_1')
self.test_loss_1 = tf.keras.metrics.Mean(name='test_loss_1')
self.test_auc_1 = tf.keras.metrics.AUC(name='test_auc_1') self.train_loss_2 = tf.keras.metrics.Mean(name='train_loss_2')
self.train_auc_2 = tf.keras.metrics.AUC(name='train_auc_2')
self.test_loss_2 = tf.keras.metrics.Mean(name='test_loss_2')
self.test_auc_2 = tf.keras.metrics.AUC(name='test_auc_2')
# 共享特征提取模块
self.model = VariableSelection(category_features,
numeric_features,
encoding_size,
dropout_rate,
category_feature_vocabulary,
project_dim) # 单词+位置编码
self.embedding_layer = TokenAndPositionEmbedding(self.max_len, self.vocab_size, embed_dim=self.embed_dim)
self.transformer_block = TransformerBlock(embed_dim=self.embed_dim, num_heads=self.num_heads, ff_dim=self.ff_dim)
# 目标1网络模块
self.h11 = tf.keras.layers.Dense(units=16, activation="relu"
, name='out1'
, kernel_regularizer=tf.keras.regularizers.l1(0.01)
, bias_regularizer=tf.keras.regularizers.l1(0.01)
, activity_regularizer=tf.keras.regularizers.l1(0.01)
)
self.h12 = tf.keras.layers.Dense(8, activation='relu')
self.h13 = tf.keras.layers.Dense(8, activation='relu')
# sigmoid 对应 cross_entropy， softmax sparse_categorical_focal_loss
self.h14 = tf.keras.layers.Dense(project_dim, activation='softmax', name='out1') # 目标2网络模块
self.h21 = tf.keras.layers.Dense(units=16, activation="relu"
, name='out2'
, kernel_regularizer=tf.keras.regularizers.l1(0.01)
, bias_regularizer=tf.keras.regularizers.l1(0.01)
, activity_regularizer=tf.keras.regularizers.l1(0.01)
)
self.h22 = tf.keras.layers.Dense(8, activation='relu')
self.h23 = tf.keras.layers.Dense(8, activation='relu')
self.h24 = tf.keras.layers.Dense(project_dim, activation='softmax', name='out2') # transformer部分隐藏层
self.tf_l1 = tf.keras.layers.GlobalAveragePooling1D()
self.tf_d1 = tf.keras.layers.Dropout(0.1)
self.tf_l2 = tf.keras.layers.Dense(20, activation="relu")
self.tf_d2 = tf.keras.layers.Dropout(0.1)
self.tf_l3 = tf.keras.layers.Dense(10, activation="relu") def call(self, inputs, training=None, mask=None):
#
click_inputs = inputs[:, -self.max_len:]
# click_inputs_transpose = tf.transpose(click_inputs)
embeddings = self.embedding_layer(click_inputs)
transformer_out = self.transformer_block(embeddings, training) transformer_out = self.tf_l1(transformer_out)
transformer_out = self.tf_d1(transformer_out)
transformer_out = self.tf_l2(transformer_out)
transformer_out = self.tf_d2(transformer_out)
transformer_out = self.tf_l3(transformer_out) vsn_out = self.model(inputs[:,:-self.max_len], training, mask)
out = tf.concat([transformer_out, vsn_out], axis=1) out1 = self.h11(out, training=training)
out1 = self.h12(out1, training=training)
out1 = self.h13(out1, training=training)
out1 = self.h14(out1, training=training) out2 = self.h21(out, training=training)
out2 = self.h22(out2, training=training)
out2 = self.h23(out2, training=training)
out2 = self.h24(out2, training=training)
return out1, out2 # 使用autograph机制转换成静态图加速
# @tf.function
def train_step(self, x, y1, y2, steps):
# print('train_step:',x, y1, y2)
with tf.GradientTape() as tape:
# print('GradientTape:', x, y)
pred1, pred2 = self(x, training=True)
# loss = tf.keras.losses.sparse_categorical_crossentropy(y, pred)
loss1 = sparse_categorical_focal_loss(y1, pred1, gamma=2)
loss2 = sparse_categorical_focal_loss(y2, pred2, gamma=2)
# # Compute gradients
gradients = tape.gradient(loss1 + loss2, self.trainable_variables,
unconnected_gradients=tf.UnconnectedGradients.ZERO) # Update weights
self.optimizer.apply_gradients(grads_and_vars=zip(gradients, self.trainable_variables)) # Update metrics (includes the metric that tracks the loss)
self.train_loss_1.update_state(loss1)
self.train_auc_1.update_state(y1, pred1[:, 1]) self.train_loss_2.update_state(loss2)
self.train_auc_2.update_state(y2, pred2[:, 1])
return loss1 + loss2 # @tf.function # 使用autograph机制转换成静态图加速
def test_step(self, x, y1, y2):
pred1, pred2 = self.call(x, training=False)
# loss = tf.keras.losses.sparse_categorical_crossentropy(y, pred)
loss_1 = sparse_categorical_focal_loss(y1, pred1, gamma=2)
loss_2 = sparse_categorical_focal_loss(y2, pred2, gamma=2) # transfer predictions
self.test_auc_1.update_state(y1, pred1[:, 1])
self.test_loss_1.update_state(loss_1)
self.test_auc_2.update_state(y2, pred2[:, 1])
self.test_loss_2.update_state(loss_2) # @tf.function
def train(self,
epochs,
batch_size=16,
lr=2e-5,
evaluation_steps=100
):
print('start train model ...')
self.optimizer = tf.keras.optimizers.Adam(lr) train_data = tf.data.Dataset.from_tensor_slices((self.x_train, (self.y_train, self.y2_train))) \
.shuffle(len(self.y_train)).batch(batch_size)
test_data = tf.data.Dataset.from_tensor_slices((self.x_test, (self.y_test, self.y2_test))).batch(batch_size) def benchmark(dataset, num_epochs=2):
start_time = time.perf_counter()
for epoch_num in range(num_epochs):
for sample in dataset:
# Performing a training step
time.sleep(0.01)
print("Execution time:", time.perf_counter() - start_time)
# benchmark(train_data,20)
# benchmark(test_data,20) best_auc = 0.
step = 0
for epoch in range(epochs):
self.train_loss_1.reset_states()
self.train_loss_2.reset_states()
self.train_auc_1.reset_states()
self.train_auc_2.reset_states() start = time.time()
cnt = 0
for x, (y1, y2) in train_data:
cnt += 1
# print('cnt is:', cnt, x, y)
self.train_step(x, y1, y2, cnt)
step += 1
if step > 0 and step % (evaluation_steps // 5) == 0:
print("Epoch {} step {} train Loss1 {} Loss2 {}".format(epoch + 1, step
, self.train_loss_1.result()
, self.train_loss_2.result()))
print("Epoch {} step {} train auc1 {} auc2 {}".format(epoch + 1, step
, self.train_auc_1.result()
, self.train_auc_2.result()))
# evaluate
if step > 0 and step % evaluation_steps == 0:
self.test_auc_1.reset_states()
self.test_auc_2.reset_states()
self.test_loss_1.reset_states()
self.test_loss_2.reset_states()
for x, (y1, y2) in test_data:
self.test_step(x, y1, y2)
cur_auc = self.test_auc_1.result()
cur_auc2 = self.test_auc_2.result()
print("Epoch {} Step {} test loss1 {} loss2 {}".format(epoch + 1, step
, self.test_loss_1.result()
, self.test_loss_2.result()))
print("Epoch {} Step {} test AUC1 {} AUC2 {}".format(epoch + 1, step, cur_auc, cur_auc2 ))
if cur_auc > best_auc:
best_auc = cur_auc
print("save model...")
# tf.saved_model.save(self, self.save_dir)
print("best auc {}".format(best_auc))
# time exhaust
delta_t = time.time() - start
h = int(delta_t // 3600)
m = int((delta_t - 3600 * h) // 60)
s = int(delta_t % 60)
print("Epoch {} time exhaust: {}h-{}m-{}s".format(epoch + 1, h, m, s)) if __name__ == '__main__':
# Target feature name.
TARGET_FEATURE_NAME = "target_label_2"
# Weight column name.
WEIGHT_COLUMN_NAME = "paid_installment_no" import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--pretrained_path', default='./data/pretrained_model/vsn')
parser.add_argument('--train_data_path', default='./train_data.csv')
parser.add_argument('--test_data_path', default='./test_data.csv')
parser.add_argument('--label', default=[TARGET_FEATURE_NAME, WEIGHT_COLUMN_NAME]) parser.add_argument('--save_dir', default='./data/model')
parser.add_argument('--lr', default=0.002, type=float)
parser.add_argument('--batch_size', default=1024, type=int)
parser.add_argument('--epochs', default=10, type=int)
parser.add_argument('--evaluation_steps', default=500, type=int)
parser.add_argument('--num_warmup_steps', default=20, type=int)
parser.add_argument('--weight_decay_rate', default=0.01, type=float) args = parser.parse_args()
top_n_obj_cols_a = {}
with open('./data/obj_feature_value_A_biz.txt', encoding='utf-8') as f:
for line in f.readlines():
key, value_dict = line.split("|")
kv = json.loads(value_dict.replace("'", "\""))
top_n_obj_cols_a[key] = list(kv.values())
# print(key,top_n_obj_cols[key]) from data_util import digt_cols, obj_cols
category_features = obj_cols
numeric_features = digt_cols
encoding_size = 10
dropout_rate = 0.2
category_feature_vocabulary = top_n_obj_cols_a
project_dim = 2
trainer = Trainer(args.pretrained_path,
train_data_path=args.train_data_path,
test_data_path=args.test_data_path,
save_dir=args.save_dir,
labels=args.label,
category_features=category_features,
numeric_features=numeric_features,
encoding_size=encoding_size,
dropout_rate=dropout_rate,
category_feature_vocabulary=category_feature_vocabulary,
project_dim=project_dim
) trainer.train(epochs=args.epochs, batch_size=args.batch_size, lr=args.lr, evaluation_steps=args.evaluation_steps)
