CODEWITHSUNDEEP
pythontensorflowtensorboard
zesla
zesla

Reputation: 11833

tensorboard shows no contents

I'm having problem with tensorboard. My codes runs fine and when I tried to visualize the graph with tensorboard --logdir = logs/log1 and then open the browser typing in localhost:6006 I saw the page with no content (only tensorboard sign and tabs like event, graph ...) Help very much appreciated. No idea how to solve the problem. (i'm using jupyter notebook)

Here is the error messages I got:

WARNING:tensorflow:IOError [Errno 2] No such file or directory:       '/home/tiger/anaconda3/envs/tensorflow/lib/python3.5/site-   packages/tensorflow/tensorboard/TAG' on path /home/tiger/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/tensorboard/TAG
WARNING:tensorflow:Unable to read TensorBoard tag
Starting TensorBoard  on port 6006
(You can navigate to http://0.0.0.0:6006)
127.0.0.1 - - [03/Jun/2016 21:20:49] "GET / HTTP/1.1" 200 -
WARNING:tensorflow:IOError [Errno 2] No such file or directory: '/home/tiger/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/tensorboard/lib/css/global.css' on path /home/tiger/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/tensorboard/lib/css/global.css
127.0.0.1 - - [03/Jun/2016 21:20:49] code 404, message Not Found
127.0.0.1 - - [03/Jun/2016 21:20:49] "GET /lib/css/global.css HTTP/1.1" 404 -
127.0.0.1 - - [03/Jun/2016 21:20:50] "GET /external/lodash/lodash.min.js HTTP/1.1" 200 -
.......
WARNING:tensorflow:IOError [Errno 2] No such file or directory: '/home/tiger/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/tensorboard/favicon.ico' on path /home/tiger/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/tensorboard/favicon.ico

my codes are below:

n_features = x_train.shape[1]
n_samples = x_train.shape[0]
n_labels = 10
n_hidden = 200
epoch_train = 200
learning_rate = 0.01
batch_size = 20

x_tr = tf.placeholder(tf.float32, shape=(None, n_features), name='x')
y_tr = tf.placeholder(tf.float32, shape=(None, n_labels), name='y')

w1 = tf.Variable(tf.truncated_normal([n_features,n_hidden]),name='weight1')
b1 = tf.Variable (tf.zeros([n_hidden]), name='bias1')
w2 = tf.Variable (tf.truncated_normal([n_hidden, n_labels]),name ='weight2')
b2 = tf.Variable(tf.zeros([n_labels]), name='bias2')

w1_hist = tf.histogram_summary('weight1', w1)
w2_hist = tf.histogram_summary('weight2', w2)
b1_hist = tf.histogram_summary('bias1', b1)
b2_hist = tf.histogram_summary('bias2', b2)
y_hist = tf.histogram_summary('y', y_tr)

with tf.name_scope('hidden') as scope:    
    z1 = tf.matmul(x_tr, w1)+b1
    a1 = tf.nn.relu (z1)

with tf.name_scope('output') as scope:    
    z2 = tf.matmul(a1, w2)+b2
    a2 = tf.nn.softmax (z2)

with tf.name_scope('cost') as scope:
    loss = tf.reduce_mean (tf.nn.softmax_cross_entropy_with_logits(z2, y_tr))
    cost_summ = tf.scalar_summary ('cost', loss)

with tf.name_scope('train') as scope:
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)

def acc (pred, y):
     return (np.mean(np.argmax(pred, 1)==np.argmax(y,1)))


with tf.Session() as session:

session.run(tf.initialize_all_variables())

merged = tf.merge_summary([y_hist, w1_hist, w2_hist, b1_hist, b2_hist, cost_summ])
writer = tf.train.SummaryWriter ('logs/log1', session.graph)

for epoch in range (epoch_train):

    offset = epoch*batch_size % (x_train.shape[0]-batch_size)
    x_tr_batch = x_train[offset:offset+batch_size, :]
    y_tr_batch = y_train[offset:offset+batch_size, :]
    feed_dict = {x_tr:x_tr_batch, y_tr:y_tr_batch}

    _, cost, prediction = session.run ([optimizer, loss, a2], feed_dict=feed_dict)

    summary = session.run (merged, feed_dict=feed_dict)
    writer.add_summary(summary,epoch)

    if epoch % 20 ==0:
        print ('training accuracy:', acc(prediction, y_tr_batch))
        print ('cost at epoch {} is:'.format(epoch), cost)
pred_ts = session.run (a2, feed_dict = {x_tr:x_test})
print ('test accuracy is:', acc(pred_ts, y_test))

Upvotes: 3

Views: 8016

Answers (4)

Sachin Goyal
Sachin Goyal

Reputation: 41

You can access the tensorboard by writing the following command: tensorboard --logdir="Your model temp directory" --host=127.0.0.1

Now open 127.0.0.1:6006 in your browser and you will have contents on the tensorboard. I hope that would solve your problem.

Upvotes: 4

rmeertens
rmeertens

Reputation: 4451

If you do see the tabs, but don't see the tab "embeddings", you might have an older version of tensorflow/tensorboard. Try upgrading to 1.0, this worked for me!

Upvotes: 0

weitang114
weitang114

Reputation: 1303

The errors you get don't bother me. I guess the problem is that you wrote nothing. You can try adding this line writer.flush() after the line of add_summary().

Upvotes: 0

Wei Liu
Wei Liu

Reputation: 1014

Looks like the problem is related to this issue. Make sure you start tensorboard after running your program. And also see what other people did to solve it. It might be a bug in tensorboard. Do you want to build tensorflow from source?

Upvotes: 0

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