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 modelmodel = 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 entropycost_function = -tf.reduce_sum(y*tf.log(model))# create a summary to monitor the cost functiontf.summary.scalar("cost_function", cost_function)with tf.name_scope("train") as scope:# gradient descentoptimizer = 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_logssummary_writer = tf.summary.FileWriter('/tmp/tensorflow_logs', graph=sess.graph)# training cyclefor iteration in range(training_iteration):avg_cost = 0.total_batch = int(mnist.train.num_examples/batch_size)# loop over all batchesfor i in range(total_batch):batch_xs, batch_ys = mnist.train.next_batch(batch_size)# fit training using batch datasess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})# compute the average lossavg_cost += sess.run(cost_function, feed_dict={x: batch_xs, y: batch_ys})/total_batch# write logs for each iterationsummary_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 stepif iteration % display_step == 0:print("Iteration:", '%04d' % (iteration + 1), "cost= ", "{:.9f}".format(avg_cost))print("Tuning completed!")# test the modelpredictions = tf.equal(tf.argmax(model, 1), tf.argmax(y, 1))# calculate accuracyaccuracy = tf.reduce_mean(tf.cast(predictions, "float"))print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))print("Success!")