Python> Verarbeitung beenden> sys / sys.exit () importieren

Betriebsumgebung

GeForce GTX 1070 (8GB)
ASRock Z170M Pro4S [Intel Z170chipset]
Ubuntu 14.04 LTS desktop amd64
TensorFlow v0.11
cuDNN v5.1 for Linux
CUDA v8.0
Python 2.7.6
IPython 5.1.0 -- An enhanced Interactive Python.

Ich wollte den Python-Code in der Mitte stoppen. Es war in Ordnung, es nacheinander mit Jupyter auszuführen, aber ich habe untersucht, wie man es mit einem Skript stoppt.

Referenz http://stackoverflow.com/questions/179369/how-do-i-abort-the-execution-of-a-python-script

• import sys
• sys.exit()

Das folgende Skript für Deep Learning heißt TensorFlow. Ich wollte nach print (input_ph.get_shape ()) fertig werden.

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import sys
import tensorflow as tf
import tensorflow.contrib.slim as slim

#Warteschlange mit Dateinamen erstellen
filename_queue = tf.train.string_input_producer(["input.csv"])

#CSV-Analyse
reader = tf.TextLineReader()
key, value = reader.read(filename_queue)
input1, output = tf.decode_csv(value, record_defaults=[[0.], [0.]])
inputs = tf.pack([input1])
output = tf.pack([output])

batch_size=4 # [4]
inputs_batch, output_batch = tf.train.shuffle_batch([inputs, output], batch_size, capacity=40, min_after_dequeue=batch_size)

input_ph = tf.placeholder("float", [None,1])
output_ph = tf.placeholder("float",[None,1])

#if 1 // debug
print(input_ph.get_shape())
sys.exit()
#endif

##Graphgenerierung von NN
hiddens = slim.stack(input_ph, slim.fully_connected, [1,7,7,7], 
  activation_fn=tf.nn.sigmoid, scope="hidden")
prediction = slim.fully_connected(hiddens, 1, activation_fn=tf.nn.sigmoid, scope="output")
loss = tf.contrib.losses.mean_squared_error(prediction, output_ph)

#train_op = slim.learning.create_train_op(loss, tf.train.AdamOptimizer(0.01))
train_op = slim.learning.create_train_op(loss, tf.train.AdamOptimizer(0.001))

def feed_dict(inputs, output):
    return {input_ph: inputs.eval(), output_ph: output.eval()}

init_op = tf.initialize_all_variables()

with tf.Session() as sess:
  coord = tf.train.Coordinator()
  threads = tf.train.start_queue_runners(sess=sess, coord=coord)

  try:
    sess.run(init_op)
    for i in range(30000): #[10000]
      _, t_loss = sess.run([train_op, loss], feed_dict=feed_dict(inputs_batch, output_batch))
      if (i+1) % 100 == 0:
        print("%d,%f" % (i+1, t_loss))
#        print("%d,%f,#step, loss" % (i+1, t_loss))
  finally:
    coord.request_stop()

  coord.join(threads)

Ergebnis

$ python linreg2_feeddict.py 
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcurand.so locally
(?, 1)