# -*- 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