我在试着让tensorboard运行一个更大的模型,我已经发布了the following question。在
在做了一些研究之后,我发现了以下问题:
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# Initializing the variables
init = tf.global_variables_initializer()
# Create a summary to monitor cost tensor
tf.summary.scalar("loss", cost)
# Create a summary to monitor accuracy tensor
tf.summary.scalar("accuracy", acc)
# Merge all summaries into a single op
merged_summary_op = tf.summary.merge_all()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# op to write logs to Tensorboard
summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())
# Training cycle
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(mnist.train.num_examples/batch_size)
# Loop over all batches
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
# Run optimization op (backprop), cost op (to get loss value)
# and summary nodes
_, c, summary = sess.run([optimizer, cost, merged_summary_op],
feed_dict={x: batch_xs, y: batch_ys}) ## Here crashs the program !!!!!!!!!!!
# Write logs at every iteration
summary_writer.add_summary(summary, epoch * total_batch + i)
# Compute average loss
avg_cost += c / total_batch
# Display logs per epoch step
if (epoch+1) % display_step == 0:
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
我得到的是:
^{pr2}$
目前没有回答
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