Ich habe ein Problem mit Tensorboard. Meine Codes laufen gut und als ich versuchte, den Graphen mit Tensorboard --logdir = logs/log1 zu visualisieren und dann den Browser in localhost: 6006 zu öffnen, sah ich die Seite ohne Inhalt (nur Tensorboard Zeichen und Tabs wie Event, Graph .. .) Hilfe sehr geschätzt. Keine Ahnung, wie das Problem gelöst werden kann. (ich verwende jupyter Notebook)Tensorboard zeigt keine Inhalte
Hier wird die Fehlermeldung, ich habe:
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
meine Codes sind unten:
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))
In dem obigen Code: mit tf.Session() als Session: folgenden Zeilen ist nicht eingekerbt wie alle Linien Sieht bis zum Ende der für die Schleife nach der Zeile eingerückt werden muss –