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!")
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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