Tensorboard kann keine Ereignisdateien finden

Question

Ich habe versucht, Tensorboard zu verwenden, um einen Bildklassifizierer mit DNN zu visualisieren. Ich bin mir sicher, dass der Verzeichnispfad korrekt ist, aber keine Daten angezeigt werden. Als ich versuchte, tensorboard --inspect --logdir='PATH/' Returns: Ereignisdateien innerhalb logdir ‚PATH /‘ gefunden

Ich denke an etwas nicht in Ordnung sein muss mit meiner Codierung.

Graph

batch_size = 500 

graph = tf.Graph() 
with graph.as_default(): 

    # Input data. For the training data, we use a placeholder that will be fed 
    # at run time with a training minibatch. 
    with tf.name_scope('train_input'): 
    tf_train_dataset = tf.placeholder(tf.float32, 
             shape=(batch_size, image_size * image_size), 
             name = 'train_x_input') 

    tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels), 
            name = 'train_y_input') 
    with tf.name_scope('validation_input'): 
    tf_valid_dataset = tf.constant(valid_dataset, name = 'valid_x_input') 
    tf_test_dataset = tf.constant(test_dataset, name = 'valid_y_input') 

    # Variables. 
    with tf.name_scope('layer'): 
    with tf.name_scope('weights'): 
     weights = tf.Variable(
      tf.truncated_normal([image_size * image_size, num_labels]), 
      name = 'W') 
     variable_summaries(weights) 
    with tf.name_scope('biases'): 
     biases = tf.Variable(tf.zeros([num_labels]), name = 'B') 
     variable_summaries(biases) 
    # Training computation. 
    with tf.name_scope('Wx_plus_b'): 
    logits = tf.matmul(tf_train_dataset, weights) + biases 
    tf.summary.histogram('logits', logits) 
    with tf.name_scope('loss'): 
    loss = tf.reduce_mean(
     tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits), 
     name = 'loss') 
    tf.summary.histogram('loss', loss) 
    tf.summary.scalar('loss_scalar', loss) 

    # Optimizer. 
    with tf.name_scope('optimizer'): 
    optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss) 

    # Predictions for the training, validation, and test data. 
    train_prediction = tf.nn.softmax(logits) 
    valid_prediction = tf.nn.softmax(tf.matmul(tf_valid_dataset, weights) + biases) 
    test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases) 

Run

num_steps = 1001 
t1 = time.time() 
with tf.Session(graph=graph) as session: 
    merged = tf.summary.merge_all() 
    writer = tf.summary.FileWriter('C:/Users/Dr_Chenxy/Documents/pylogs', session.graph) 
    tf.global_variables_initializer().run() 
    print("Initialized") 
    for step in range(num_steps): 
    # Pick an offset within the training data, which has been randomized. 
    # Note: we could use better randomization across epochs. 
    offset = (step * batch_size) % (train_labels.shape[0] - batch_size) # 1*128 % (200000 - 128) 
    # Generate a minibatch. 
    batch_data = train_dataset[offset:(offset + batch_size), :] # choose training set for this iteration 
    batch_labels = train_labels[offset:(offset + batch_size), :] # choose labels for this iteration 
    # Prepare a dictionary telling the session where to feed the minibatch. 
    # The key of the dictionary is the placeholder node of the graph to be fed, 
    # and the value is the numpy array to feed to it. 
    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels} 
    _, l, predictions = session.run([optimizer, loss, train_prediction], feed_dict=feed_dict) 
    if (step % 100 == 0): 
     print("Minibatch loss at step %d: %f" % (step, l)) 
     print("Minibatch accuracy: %.1f%%" % accuracy(predictions, batch_labels)) 
     print("Validation accuracy: %.1f%%" % accuracy(
     valid_prediction.eval(), valid_labels)) 
    print("Test accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels)) 
t2 = time.time() 
print('Running time', t2-t1, 'seconds') 

Answer 1

gelöst. Für alle, die an der Befehlszeile schlecht sind, besteht das Problem darin, dass Sie in der Befehlszeile Ihr Verzeichnis nicht mit quote ('') beschriften. Angenommen, Ihre Daten befinden sich unter 'X: \ X \ file.x' Gehen Sie in die Befehlszeile zu X: \ first. Dann geben: tensorboard --logdir=X/ NICHT tensorboard --logdir='.X/'

Answer 2

with tf.Session() as sess: 
    writer = tf.summary.FileWriter("output", sess.graph) 

Windows-OS.Tensorboard Ausgabeordner wird in Ordner erstellt, in dem file.py bei running.Therefore befindet, wenn Sie example.py von Windows-Dokumente ausführen Ordner, den Sie versuchen, dies in der Eingabeaufforderung: tensorboard --logdir=C:\Users\YourName\Documents\output