TensorFlow FileWriter未写入文件 [英] TensorFlow FileWriter not writing to file

问题描述

我正在训练一个简单的TensorFlow模型.训练方面工作正常，但没有日志写入/tmp/tensorflow_logs，我不确定为什么.谁能提供一些见识?谢谢

# import MNIST
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

import tensorflow as tf

# set parameters
learning_rate = 0.01
training_iteration = 30
batch_size = 100
display_step = 2

# TF graph input
x = tf.placeholder("float", [None, 784])
y = tf.placeholder("float", [None, 10])

# create a model

# set model weights
# 784 is the dimension of a flattened MNIST image
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

with tf.name_scope("Wx_b") as scope:
    # construct linear model
    model = tf.nn.softmax(tf.matmul(x, W) + b) #softmax

# add summary ops to collect data
w_h = tf.summary.histogram("weights", W)
b_h = tf.summary.histogram("biases", b)

with tf.name_scope("cost_function") as scope:
    # minimize error using cross entropy
    cost_function = -tf.reduce_sum(y*tf.log(model))
    # create a summary to monitor the cost function
    tf.summary.scalar("cost_function", cost_function)

with tf.name_scope("train") as scope:
    # gradient descent
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)

init = tf.global_variables_initializer()

# merge all summaries into a single operator
merged_summary_op = tf.summary.merge_all()

# launch the graph
with tf.Session() as sess:
    sess.run(init)

    # set the logs writer to the folder /tmp/tensorflow_logs
    summary_writer = tf.summary.FileWriter('/tmp/tensorflow_logs', graph=sess.graph)

    # training cycle
    for iteration in range(training_iteration):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples/batch_size)
        # loop over all batches
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            # fit training using batch data
            sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})
            # compute the average loss
            avg_cost += sess.run(cost_function, feed_dict={x: batch_xs, y: batch_ys})/total_batch
            # write logs for each iteration
            summary_str = sess.run(merged_summary_op, feed_dict={x: batch_xs, y: batch_ys})
            summary_writer.add_summary(summary_str, iteration*total_batch + i)
        # display logs per iteration step
        if iteration % display_step == 0:
            print("Iteration:", '%04d' % (iteration + 1), "cost= ", "{:.9f}".format(avg_cost))

    print("Tuning completed!")

    # test the model
    predictions = tf.equal(tf.argmax(model, 1), tf.argmax(y, 1))
    # calculate accuracy
    accuracy = tf.reduce_mean(tf.cast(predictions, "float"))
    print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))


print("Success!")

解决方案

将文件路径从/temp/...更改为temp/...并添加summary_writer.flush()和summary_writer.close()的组合可以成功写入日志.

I am training a simple TensorFlow model. The training aspect works fine, but no logs are being written to /tmp/tensorflow_logs and I'm not sure why. Could anyone provide some insight? Thank you

# import MNIST
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

import tensorflow as tf

# set parameters
learning_rate = 0.01
training_iteration = 30
batch_size = 100
display_step = 2

# TF graph input
x = tf.placeholder("float", [None, 784])
y = tf.placeholder("float", [None, 10])

# create a model

# set model weights
# 784 is the dimension of a flattened MNIST image
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

with tf.name_scope("Wx_b") as scope:
    # construct linear model
    model = tf.nn.softmax(tf.matmul(x, W) + b) #softmax

# add summary ops to collect data
w_h = tf.summary.histogram("weights", W)
b_h = tf.summary.histogram("biases", b)

with tf.name_scope("cost_function") as scope:
    # minimize error using cross entropy
    cost_function = -tf.reduce_sum(y*tf.log(model))
    # create a summary to monitor the cost function
    tf.summary.scalar("cost_function", cost_function)

with tf.name_scope("train") as scope:
    # gradient descent
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)

init = tf.global_variables_initializer()

# merge all summaries into a single operator
merged_summary_op = tf.summary.merge_all()

# launch the graph
with tf.Session() as sess:
    sess.run(init)

    # set the logs writer to the folder /tmp/tensorflow_logs
    summary_writer = tf.summary.FileWriter('/tmp/tensorflow_logs', graph=sess.graph)

    # training cycle
    for iteration in range(training_iteration):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples/batch_size)
        # loop over all batches
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            # fit training using batch data
            sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})
            # compute the average loss
            avg_cost += sess.run(cost_function, feed_dict={x: batch_xs, y: batch_ys})/total_batch
            # write logs for each iteration
            summary_str = sess.run(merged_summary_op, feed_dict={x: batch_xs, y: batch_ys})
            summary_writer.add_summary(summary_str, iteration*total_batch + i)
        # display logs per iteration step
        if iteration % display_step == 0:
            print("Iteration:", '%04d' % (iteration + 1), "cost= ", "{:.9f}".format(avg_cost))

    print("Tuning completed!")

    # test the model
    predictions = tf.equal(tf.argmax(model, 1), tf.argmax(y, 1))
    # calculate accuracy
    accuracy = tf.reduce_mean(tf.cast(predictions, "float"))
    print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))


print("Success!")

解决方案

A combination of changing the file path from /temp/... to temp/... and adding summary_writer.flush() and summary_writer.close() made the logs be written successfully.

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