Why is the loss taking multiple values for one epoch? - tensorflow

Question

I am wondering about the smoothing option of tensorboard. When I set smoothing to 0, I get the following plot of the loss:
This seems strange to me, because I think I calculate the loss only once for each epoch. Why does it have multiple values for one epoch?
Code
The following code with tensorboard --logdir summary_dir created the image:
#!/usr/bin/env python
"""MNIST with Tensorflow."""
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import os
import numpy as np
epochs = 20000
model_checkpoint_path = 'checkpoints/mnist_tf_model.ckpt'
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.0001)
return tf.get_variable(initializer=initial, name='weights')
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.get_variable(initializer=initial, name='biases')
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
def eval_network(sess, summary_writer, dataset, correct_prediction, epoch):
correct_sum = 0
total_test = 0
training_summary = tf.get_default_graph().get_tensor_by_name("training_accuracy:0")
loss_summary = tf.get_default_graph().get_tensor_by_name("loss:0")
for i in range(dataset.labels.shape[0] / 1000):
feed_dict = {x: dataset.images[i * 1000:(i + 1) * 1000],
y_: dataset.labels[i * 1000:(i + 1) * 1000],
keep_prob: 1.0}
[test_correct, train_summ, loss_summ] = sess.run([correct_prediction,
training_summary,
loss_summary],
feed_dict=feed_dict)
summary_writer.add_summary(train_summ, epoch)
summary_writer.add_summary(loss_summ, epoch)
test_correct = correct_prediction.eval(feed_dict=feed_dict)
correct_sum += sum(test_correct)
total_test += len(test_correct)
return float(correct_sum) / total_test
def log_score(sess, summary_writer, filename, mnist, scoring, epoch):
with open(filename, "a") as myfile:
train = eval_network(sess, summary_writer, mnist.train, scoring, epoch)
test = eval_network(sess, summary_writer, mnist.test, scoring, epoch)
myfile.write("%i;%0.6f;%0.6f\n" % (epoch, train, test))
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
with tf.Session() as sess:
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
x_image = tf.reshape(x, [-1, 28, 28, 1])
with tf.variable_scope('conv1') as scope:
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1, name='ReLU1')
h_pool1 = max_pool_2x2(h_conv1)
with tf.variable_scope('conv2') as scope:
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2, name='ReLU2')
h_pool2 = max_pool_2x2(h_conv2)
with tf.variable_scope('fc1'):
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
with tf.variable_scope('dropout'):
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
with tf.variable_scope('softmax'):
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv),
reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.scalar_summary("training_accuracy", accuracy, name="training_accuracy")
tf.scalar_summary("loss", cross_entropy, name="loss")
summary_writer = tf.train.SummaryWriter('summary_dir', sess.graph)
sess.run(tf.initialize_all_variables())
for i in range(epochs):
batch = mnist.train.next_batch(50)
if i % 100 == 0:
log_score(sess, summary_writer,
'validation-curve-accuracy.csv',
mnist, correct_prediction, i)
train_step.run(feed_dict={x: batch[0],
y_: batch[1],
keep_prob: 0.5})
log_score(sess, summary_writer, 'validation-curve-accuracy.csv',
mnist, correct_prediction, epochs)

Answer 1

log_score evaluates the summaries and it's called every 100 minibatches.