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!")
Advertisement
Answer
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.