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
,我不知道为什么。有人能提供一些见解吗?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!")
答案 0 :(得分:6)
将文件路径从/temp/...
更改为temp/...
并添加summary_writer.flush()
和summary_writer.close()
的组合使得日志成功写入。
答案 1 :(得分:0)
解决我类似问题的方法是在学习循环之前调用#
。根据文档中的示例代码(https://www.tensorflow.org/api_docs/python/tf/summary->传统TF 1.x图形执行的示例用法。
TF2.1,图形模式执行