在下面的代码中，l2令人惊讶地返回与l1相同的值，但由于在l2之前的列表中请求了优化器，我预计损失将是训练后的新损失。我可以不从图表中同时请求多个值并期望输出一致吗？
if ((isset($_POST["MM_update"])) && ($_POST["MM_update"] == "form1")) {
$updateSQL = sprintf("UPDATE cursosxcliente SET idCliente=%s, idCurso=%s, tiempo=%s, valor_curso=%s, congelado=%s, notas_cliente=%s, Fecha_inicio=%s, Fecha_exp=%s,Fecha_inicio_congelacion=%s,Fecha_fin_congelacion=%s WHERE idCursoXCliente=%s",
GetSQLValueString($_POST['idCliente'], "int"),
GetSQLValueString($_POST['idCurso'], "text"),
GetSQLValueString($_POST['tiempo'], "text"),
GetSQLValueString($_POST['valor_curso'], "int"),
GetSQLValueString($_POST['congelado'], "int"),
GetSQLValueString($_POST['notas_cliente'], "text"),
GetSQLValueString($_POST['Fecha_inicio'], "text"),
GetSQLValueString($_POST['Fecha_exp'], "text"),
GetSQLValueString($_POST['Fecha_inicio_congelacion'], "text"),
GetSQLValueString($_POST['Fecha_fin_congelacion'], "text"),
GetSQLValueString($_POST['idCursoXCliente'], "int"));
$hoy = date("Y-m-d");
$decongelar = explode("-",$row_actualizarCursoCliente['Fecha_fin_congelacion']);
if ($descongelar[0] == $hoy[0] && $descongelar[1] == $hoy[1] && $descongelar[2] == $hoy[2] && $row_actualizarCursoCliente['congelado']=="1") {
$updateSQLs = sprintf("UPDATE cursosxcliente SET congelado=0,Fecha_inicio_congelacion=default,Fecha_fin_congelacion=default WHERE idCursoXCliente=%s",
GetSQLValueString($_POST['congelado'], "int"),
GetSQLValueString($_POST['Fecha_inicio_congelacion'], "text"),
GetSQLValueString($_POST['Fecha_fin_congelacion'], "text"));
}
mysql_select_db($database_connectBD_fc, $connectBD_fc);
$Result1 = mysql_query($updateSQL,$updateSQLs, $connectBD_fc) or die(mysql_error());
$updateGoTo = "exito-act-curs-cliente.php";
if (isset($_SERVER['QUERY_STRING'])) {
$updateGoTo .= (strpos($updateGoTo, '?')) ? "&" : "?";
$updateGoTo .= $_SERVER['QUERY_STRING'];
}
header(sprintf("Location: %s", $updateGoTo));
}
答案 0 :(得分：10)
否 - 您在列表中请求它们的顺序对评估顺序没有影响。对于具有副作用的操作（如优化程序），如果要保证特定的排序，则需要使用with_dependencies或类似的控制流构造来强制执行。通常，忽略副作用，TensorFlow会在计算后立即从图中抓取节点，从而将结果返回给您 - 显然，在优化器之前计算损失，因为优化程序需要损失是其投入之一。 （请记住，'loss'不是变量;它是张量;因此它实际上并不受优化器步骤的影响。）
sess.run([loss, optimizer], ...)
和
sess.run([optimizer, loss], ...)
是等价的。
答案 1 :(得分：4)
我已经使用三种session.run测试了在tensorflow中实现的逻辑回归。运行：
一起
res1，res2，res3 = sess.run（[op1，op2，op3]）
分别
res1 = sess.run（op1）
res2 = sess.run（op2）
res3 = sess.run（op3）
带有依赖关系
使用tf.control_dependencies（[op1]）：
op2_after = tf.identity（op1）
op3_after = tf.identity（op1）
res1，res2，res3 = session.run（[op1，op2_after，op3_after]）
将批量大小设置为10000，结果为：
1: 0.05+ secs < 2: 0.11+ secs < 3: 0.25+ secs
1和3之间的主要区别只是一个小批量。使用3代替1可能不值得。
这是测试代码（它是由其他人编写的LR示例......）。
以下是data
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 2 13:38:14 2017
@author: inse7en
"""
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
import time
pickle_file = '/Users/inse7en/Downloads/notMNIST.pickle'
with open(pickle_file, 'rb') as f:
save = pickle.load(f)
train_dataset = save['train_dataset']
train_labels = save['train_labels']
valid_dataset = save['valid_dataset']
valid_labels = save['valid_labels']
test_dataset = save['test_dataset']
test_labels = save['test_labels']
del save # hint to help gc free up memory
print('Training set', train_dataset.shape, train_labels.shape)
print('Validation set', valid_dataset.shape, valid_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)
image_size = 28
num_labels = 10
def reformat(dataset, labels):
dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)
# Map 2 to [0.0, 1.0, 0.0 ...], 3 to [0.0, 0.0, 1.0 ...]
labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)
return dataset, labels
train_dataset, train_labels = reformat(train_dataset, train_labels)
valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)
test_dataset, test_labels = reformat(test_dataset, test_labels)
print('Training set', train_dataset.shape, train_labels.shape)
print('Validation set', valid_dataset.shape, valid_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)
# This is to expedite the process
train_subset = 10000
# This is a good beta value to start with
beta = 0.01
graph = tf.Graph()
with graph.as_default():
# Input data.
# They're all constants.
tf_train_dataset = tf.constant(train_dataset[:train_subset, :])
tf_train_labels = tf.constant(train_labels[:train_subset])
tf_valid_dataset = tf.constant(valid_dataset)
tf_test_dataset = tf.constant(test_dataset)
# Variables
# They are variables we want to update and optimize.
weights = tf.Variable(tf.truncated_normal([image_size * image_size, num_labels]))
biases = tf.Variable(tf.zeros([num_labels]))
# Training computation.
logits = tf.matmul(tf_train_dataset, weights) + biases
# Original loss function
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))
# Loss function using L2 Regularization
regularizer = tf.nn.l2_loss(weights)
loss = tf.reduce_mean(loss + beta * regularizer)
# Optimizer.
optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
# Predictions for the training, validation, and test data.
train_prediction = tf.nn.softmax(logits)
valid_prediction = tf.nn.softmax(tf.matmul(tf_valid_dataset, weights) + biases)
test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)
num_steps = 50
def accuracy(predictions, labels):
return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))
/ predictions.shape[0])
with tf.Session(graph=graph) as session:
# This is a one-time operation which ensures the parameters get initialized as
# we described in the graph: random weights for the matrix, zeros for the
# biases.
tf.initialize_all_variables().run()
print('Initialized')
for step in range(num_steps):
# Run the computations. We tell .run() that we want to run the optimizer,
# and get the loss value and the training predictions returned as numpy
# arrays.
#_, l, predictions = session.run([optimizer, loss, train_prediction])
start_time = time.time()
with tf.control_dependencies([optimizer]):
loss_after_optimizer = tf.identity(loss)
predictions_after = tf.identity(train_prediction)
regularizers_after = tf.identity(regularizer)
_, l, predictions,regularizers = session.run([optimizer, loss_after_optimizer, predictions_after, regularizers_after])
print("--- with dependencies: %s seconds ---" % (time.time() - start_time))
#start_time = time.time()
#opt = session.run(optimizer)
#l = session.run(loss)
#predictions = session.run(train_prediction)
#regularizers = session.run(regularizer)
#print("--- run separately: %s seconds ---" % (time.time() - start_time))
#start_time = time.time()
#_, l, predictions,regularizers = session.run([optimizer, loss, train_prediction, regularizer])
#print("--- all together: %s seconds ---" % (time.time() - start_time))
#if (step % 100 == 0):
#print('Loss at step {}: {}'.format(step, l))
#print('Training accuracy: {:.1f}'.format(accuracy(predictions,
#train_labels[:train_subset, :])))
# Calling .eval() on valid_prediction is basically like calling run(), but
# just to get that one numpy array. Note that it recomputes all its graph
# dependencies.
# You don't have to do .eval above because we already ran the session for the
# train_prediction
#print('Validation accuracy: {:.1f}'.format(accuracy(valid_prediction.eval(),
#valid_labels)))
#print('Test accuracy: {:.1f}'.format(accuracy(test_prediction.eval(), test_labels)))
#print(regularizer)
答案 2 :(得分：3)
作为Dave points out，Session.run()的参数顺序对评估顺序没有影响，并且示例中的loss张量不依赖于{{ 1}} op。要添加依赖项，您可以使用tf.control_dependencies()在获取损失之前为运行的优化程序添加显式依赖项：
optimizer