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# -*- coding: utf-8 -*
# from __future__ import print_function
import sys
import tensorflow as tf
# import tensorflow_datasets as tfds
import numpy as np
import json
tf.enable_eager_execution()
def test():
# mirrored_strategy = tf.distribute.MirroredStrategy()
# # 在config中加入镜像策略
# config = tf.estimator.RunConfig(train_distribute=mirrored_strategy, eval_distribute=mirrored_strategy)
# 把config加到模型里
regressor = tf.estimator.LinearRegressor(
feature_columns=[tf.feature_column.numeric_column('feats')],
optimizer='SGD'
# ,config=config
)
def input_fn():
dataset = tf.data.Dataset.from_tensors(({"feats":[1.]}, [1.]))
return dataset.repeat(1000).batch(10)
# 正常训练，正常评估
regressor.train(input_fn=input_fn
, steps=20
)
regressor.evaluate(input_fn=input_fn
# , steps=10
)
def parse_from_json(config_path):
""" parse feature columns from feature config path
Args:
config_path: string, a feature config path
"""
total = 0
correct = 0
with open(config_path, "r") as f:
config = json.load(f)
feature_names = set()
features = config["features"]
for feature in features:
feature_name = feature['feature_name']
if '#' in feature_name:
feature_name = feature_name.split('#')[0]
feature_names.add(feature_name)
return feature_names
#convert model's format from *.pb to *.pbtxt
def parse_model_2_txt(saved_model_dir ,output_file):
from tensorflow.python.saved_model import loader_impl
from google.protobuf import text_format
saved_model = loader_impl._parse_saved_model(saved_model_dir)
with open(output_file, 'w') as f:
f.write(text_format.MessageToString(saved_model))
# parse_model_2_txt('/Users/machi/git/internal/starship_galaxy/model_zoo/scheduler/2022q2combo/old', '/Users/machi/git/internal/starship_galaxy/model_zoo/scheduler/2022q2combo/old/saved_model.pbtxt')
import os
def build_serving_input_new():
import pickle
with tf.gfile.Open('feature_desc.pkl', mode='rb') as f:
feature_dec = pickle.load(f)
sep_placeholder = {}
for name, desc in feature_dec.items():
if 'sg_poi_click_time_gap_seq_2d' in name:
print(desc)
# return sep_placeholder
def read_schema(file):
d = {}
with open(file) as f:
for line in f:
line = line.strip()
fds = line.split(' ')
d[fds[0]] = fds[1]
return d
def sparse_tensor():
indices_tf = tf.constant([[0, 0], [0, 1], [1, 1], [2, 2]], dtype=tf.int64)
values_tf = tf.constant([1, 2, 3, 4], dtype=tf.int32)
dense_shape_tf = tf.constant([3, 3], dtype=tf.int64)
sparse_tf = tf.SparseTensor(indices=indices_tf,
values=values_tf,
dense_shape=dense_shape_tf)
dense_tf = tf.sparse_tensor_to_dense(sparse_tf)
# print(dense_tf)
user_tf = tf.constant([1, 2, 3], dtype=tf.int32, shape=[3, 1])
# 一行为一个session，每一行包含不同个数的样本。以下示例中，共有3个session，第1个session包含3个样本，第2个session包含2个样本，第3个session行包含1个样本
# b为non_common特征
b = tf.constant([[1, 2, 1], [0, 3, 2], [0, 0, 4]])
# a为common特征，3个session有3个值
a = tf.constant([1, 2, 3], shape=[3, 1])
# 将a扩展为和b相同维度
a = tf.tile(a, tf.constant([1, 3]))
print(a)
# 获取b中非0元素的下标
indices = tf.where(tf.not_equal(b, 0))
print(indices)
# 将非0元素的下标处的a和b的值拼接起来，即样本展开后的结果
c = tf.concat(values=[tf.expand_dims(tf.gather_nd(a, indices), axis=1), tf.expand_dims(tf.gather_nd(b, indices), axis=1)], axis=1)
print(c)
def kkv_attention(query, key, value, mask=None):
# Transpose key and value matrices
key_transpose = tf.transpose(key, perm=[0, 2, 1])
value_transpose = tf.transpose(value, perm=[0, 2, 1])
# Compute dot product between query and key
logits = tf.matmul(query, key_transpose)
# Apply mask (if provided) to logits
if mask is not None:
logits += mask
# Apply softmax activation to obtain attention scores
attention_scores = tf.nn.softmax(logits, axis=-1)
# Apply attention scores to value to obtain context vector
context_vector = tf.matmul(attention_scores, value_transpose)
# Transpose back the output
context_vector = tf.transpose(context_vector, perm=[0, 2, 1])
return context_vector, attention_scores
# write kkv attention function
def write_kkv_attention(query, key, value, mask=None):
# Transpose key and value matrices
# key_transpose = tf.transpose(key, perm=[0, 2, 1])
# value_transpose = tf.transpose(value, perm=[0, 2, 1])
# Compute dot product between query and key
logits = tf.matmul(query, key)
# Apply mask (if provided) to logits
if mask is not None:
logits += mask
# Apply softmax activation to obtain attention scores
attention_scores = tf.nn.softmax(logits, axis=-1)
# Apply attention scores to value to obtain context vector
context_vector = tf.matmul(attention_scores, value)
# Transpose back the output
# context_vector = tf.transpose(context_vector, perm=[0, 2, 1])
return context_vector, attention_scores
# test write_kkv_attention
def test_write_kkv_attention():
# define query and key matrices
query = tf.constant([[-0.1250, 0.0000, -0.5000, 0.5000, 0.0000]])
key = tf.constant([[ -0.1250, 0.0000, -0.5000, 0.5000, 0.0000],
[-0.5000, 0.0000, 0.5000, 0.5000, 0.0000],
[-0.2500, -0.5000, 0.0000, 0.5000, 0.2500],
[ 0.0000, 0.0000, 0.0000, 0.5000, 0.5000],
[ 0.5000, 0.5000, 0.0000, -0.5000, 0.5000]])
value = tf.constant([[-0.5000, 0.0000, 0.5000, 0.5000, 0.0000],
[-0.5000, 0.0000, 0.5000, 0.5000, 0.0000],
[-0.5000, 0.0000, 0.5000, 0.5000, 0.0000],
[ 0.0000, 0.0000, 0.5000, 0.5000, 0.5000],
[ 0.5000, 0.5000, 0.0000, -0.5000, 0.5000]])
mask = None
# call write_kkv_attention and obtain context vector and attention scores
context_vector, attention_scores = write_kkv_attention(query, key, value,mask)
# print results
print context_vector
print attention_scores
print '123', 1 |