目录
简单版:
有个要可视化的变量,var首先
tf.summary.scalar('min', tf.reduce_min(var))
tf.summary.histogram('histogram', var)
然后合并
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train',sess.graph)
最后在循环中不断添加每一步的训练日志
for i in range(FLAGS.max_steps):
summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
train_writer.add_summary(summary, i)
详细版:
1、概念
我们可以使用TensorBoard做什么:
模型结构、损失函数曲线、精度曲线等等,都可以在TensorBoard中可视化的表现出来。
2、安装
使用PIP安装TensorFlow时就会自动安装TensorBoard
3、序列化summary数据
TensorBoard的运行需要依靠读取运行TensorFlow时生成的summary数据。那么如何获得summary数据呢?
首先,建立TensorFlow图,它是summary 数据的来源。之后我们需要在图中指定哪些nodes需要导出summary数据,方法是对指定的nodes添加一个tf.summary.scalar
。需要说明的是,我们所创建的summary nodes只是图(graph)的外面的分支,TensorFlow中的其他操作并不依靠这部分summary nodes。另外根据TensorFlow的原理,只有图运转起来时才能生成summaries data,一个一个管理summaries data是非常枯燥麻烦的,所以可以使用 tf.summary.merge_all 把所有的summary汇总在一起。
通过上面这一步,我们已经可以生成一个序列化的Summary对象,为了把这些数据写到硬盘上,还需要借助 tf.summary.FileWriter 函数。
我们既可以每个周期都存一下 summary data,也可以N个周期存一下,这取决于我们对数据的需求。
下面举个例子。
def variable_summaries(var):
"""Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.summary.scalar('mean', mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
tf.summary.scalar('stddev', stddev)
tf.summary.scalar('max', tf.reduce_max(var))
tf.summary.scalar('min', tf.reduce_min(var))
tf.summary.histogram('histogram', var)
def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
"""Reusable code for making a simple neural net layer.
It does a matrix multiply, bias add, and then uses relu to nonlinearize.
It also sets up name scoping so that the resultant graph is easy to read,
and adds a number of summary ops.
"""
# Adding a name scope ensures logical grouping of the layers in the graph.
with tf.name_scope(layer_name):
# This Variable will hold the state of the weights for the layer
with tf.name_scope('weights'):
weights = weight_variable([input_dim, output_dim])
variable_summaries(weights)
with tf.name_scope('biases'):
biases = bias_variable([output_dim])
variable_summaries(biases)
with tf.name_scope('Wx_plus_b'):
preactivate = tf.matmul(input_tensor, weights) + biases
tf.summary.histogram('pre_activations', preactivate)
activations = act(preactivate, name='activation')
tf.summary.histogram('activations', activations)
return activations
hidden1 = nn_layer(x, 784, 500, 'layer1')
with tf.name_scope('dropout'):
keep_prob = tf.placeholder(tf.float32)
tf.summary.scalar('dropout_keep_probability', keep_prob)
dropped = tf.nn.dropout(hidden1, keep_prob)
# Do not apply softmax activation yet, see below.
y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)
with tf.name_scope('cross_entropy'):
# The raw formulation of cross-entropy,
#
# tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.softmax(y)),
# reduction_indices=[1]))
#
# can be numerically unstable.
#
# So here we use tf.losses.sparse_softmax_cross_entropy on the
# raw logit outputs of the nn_layer above.
with tf.name_scope('total'):
cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y)
tf.summary.scalar('cross_entropy', cross_entropy)
with tf.name_scope('train'):
train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(
cross_entropy)
with tf.name_scope('accuracy'):
with tf.name_scope('correct_prediction'):
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
with tf.name_scope('accuracy'):
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)
# Merge all the summaries and write them out to /tmp/mnist_logs (by default)
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train',
sess.graph)
test_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/test')
tf.global_variables_initializer().run()
初始化好FileWriters后,我们还需要在训练过程中不断的把summaries写入FilesWriters
# Train the model, and also write summaries.
# Every 10th step, measure test-set accuracy, and write test summaries
# All other steps, run train_step on training data, & add training summaries
def feed_dict(train):
"""Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""
if train or FLAGS.fake_data:
xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data)
k = FLAGS.dropout
else:
xs, ys = mnist.test.images, mnist.test.labels
k = 1.0
return {x: xs, y_: ys, keep_prob: k}
for i in range(FLAGS.max_steps):
if i % 10 == 0: # Record summaries and test-set accuracy
summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))
test_writer.add_summary(summary, i)
print('Accuracy at step %s: %s' % (i, acc))
else: # Record train set summaries, and train
summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
train_writer.add_summary(summary, i)
等到训练结束,我们就可以用tensorboard查看训练中的summaries了。
4、加载TensorBoard
tensorboard --logdir=path/to/log-directory
log-directory是FileWriter初始化时设定的目录
最后我们打开浏览器,输入 localhost:6006 就可以看到TensorBoard了。