无法在 tensorflow 1.1 中从上一个会话加载 int 变量
Cannot load int variable from previous session in tensorflow 1.1
我读过很多类似的问题，但就是无法正常工作。
我的模型训练得很好，每个时期都在制作检查点文件。我想要它，以便程序可以在重新加载后从纪元 x 继续，并且还可以在每次迭代时打印该纪元上的内容。我可以简单地将数据保存在检查点文件之外，但是我也想这样做是为了让我相信其他所有内容也都已正确存储。
不幸的是，当我重新启动时，epoch/global_step 变量中的值始终为 0。
import tensorflow as tf
import numpy as np
import tensorflow as tf
import numpy as np
# more imports
def extract_number(f): # used to get latest checkpint file
s = re.findall("epoch(\d+).ckpt",f)
return (int(s[0]) if s else -1,f)
def restore(init_op, sess, saver): # called to restore or just initialise model
list = glob(os.path.join("./params/e*"))
if list:
file = max(list,key=extract_number)
saver.restore(sess, file[:-5])
sess.run(init_op)
return
with tf.Graph().as_default() as g:
# build models
total_batch = data.train.num_examples / batch_size
epochLimit = 51
saver = tf.train.Saver()
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
saver = tf.train.Saver()
init_op = tf.global_variables_initializer()
restore(init_op, sess, saver)
epoch = global_step.eval()
while epoch < epochLimit:
total_batch = data.train.num_examples / batch_size
for i in range(int(total_batch)):
sys.stdout.flush()
voxels = newData.eval()
batch_z = np.random.uniform(-1, 1, [batch_size, z_size]).astype(np.float32)
sess.run(opt_G, feed_dict={z:batch_z, train:True})
sess.run(opt_D, feed_dict={input:voxels, z:batch_z, train:True})
with open("out/loss.csv", 'a') as f:
batch_loss_G = sess.run(loss_G, feed_dict={z:batch_z, train:False})
batch_loss_D = sess.run(loss_D, feed_dict={input:voxels, z:batch_z, train:False})
msgOut = "Epoch: [{0}], i: [{1}], G_Loss[{2:.8f}], D_Loss[{3:.8f}]".format(epoch, i, batch_loss_G, batch_loss_D)
print(msgOut)
epoch=epoch+1
sess.run(global_step.assign(epoch))
saver.save(sess, "params/epoch{0}.ckpt".format(epoch))
batch_z = np.random.uniform(-1, 1, [batch_size, z_size]).astype(np.float32)
voxels = sess.run(x_, feed_dict={z:batch_z})
v = voxels[0].reshape([32, 32, 32]) > 0
util.save_binvox(v, "out/epoch{0}.vox".format(epoch), 32)
我还在底部使用 assign 更新了全局步骤变量。有任何想法吗？任何帮助将不胜感激。
当您在恢复后调用 sess.run(init_op) 时，这会将所有变量重置为其初始值。评论那条线，事情应该会起作用。
由于多种原因，我的原始代码是错误的，因为我尝试了很多东西。第一响应者 Alexandre Passos 给出了一个有效的观点，但我相信改变游戏的也是范围的使用（也许？）。
如果对任何人有帮助，以下是有效的更新代码：
import tensorflow as tf
import numpy as np
# more imports
def extract_number(f): # used to get latest checkpint file
s = re.findall("epoch(\d+).ckpt",f)
return (int(s[0]) if s else -1,f)
def restore(sess, saver): # called to restore or just initialise model
list = glob(os.path.join("./params/e*"))
if list:
file = max(list,key=extract_number)
saver.restore(sess, file[:-5])
return saver, True, sess
saver = tf.train.Saver()
init_op = tf.global_variables_initializer()
sess.run(init_op)
return saver, False , sess
batch_size = 100
learning_rate = 0.0001
beta1 = 0.5
z_size = 100
save_interval = 1
data = dataset.read()
total_batch = data.train.num_examples / batch_size
def fill_queue():
for i in range(int(total_batch*epochLimit)):
sess.run(enqueue_op, feed_dict={batch: data.train.next_batch(batch_size)}) # runnig in seperate thread to feed a FIFOqueue
with tf.variable_scope("glob"):
global_step = tf.get_variable(name='global_step', initializer=0,trainable=False)
# build models
epochLimit = 51
saver = tf.train.Saver()
with tf.Session() as sess:
saver,rstr,sess = restore(sess, saver)
with tf.variable_scope("glob", reuse=True):
epocht = tf.get_variable(name='global_step', trainable=False, dtype=tf.int32)
epoch = epocht.eval()
while epoch < epochLimit:
total_batch = data.train.num_examples / batch_size
for i in range(int(total_batch)):
sys.stdout.flush()
voxels = newData.eval()
batch_z = np.random.uniform(-1, 1, [batch_size, z_size]).astype(np.float32)
sess.run(opt_G, feed_dict={z:batch_z, train:True})
sess.run(opt_D, feed_dict={input:voxels, z:batch_z, train:True})
with open("out/loss.csv", 'a') as f:
batch_loss_G = sess.run(loss_G, feed_dict={z:batch_z, train:False})
batch_loss_D = sess.run(loss_D, feed_dict={input:voxels, z:batch_z, train:False})
msgOut = "Epoch: [{0}], i: [{1}], G_Loss[{2:.8f}], D_Loss[{3:.8f}]".format(epoch, i, batch_loss_G, batch_loss_D)
print(msgOut)
epoch=epoch+1
sess.run(global_step.assign(epoch))
saver.save(sess, "params/epoch{0}.ckpt".format(epoch))
batch_z = np.random.uniform(-1, 1, [batch_size, z_size]).astype(np.float32)
voxels = sess.run(x_, feed_dict={z:batch_z})
v = voxels[0].reshape([32, 32, 32]) > 0
util.save_binvox(v, "out/epoch{0}.vox".format(epoch), 32)
我读过很多类似的问题，但就是无法正常工作。
我的模型训练得很好，每个时期都在制作检查点文件。我想要它，以便程序可以在重新加载后从纪元 x 继续，并且还可以在每次迭代时打印该纪元上的内容。我可以简单地将数据保存在检查点文件之外，但是我也想这样做是为了让我相信其他所有内容也都已正确存储。
不幸的是，当我重新启动时，epoch/global_step 变量中的值始终为 0。
import tensorflow as tf
import numpy as np
import tensorflow as tf
import numpy as np
# more imports
def extract_number(f): # used to get latest checkpint file
s = re.findall("epoch(\d+).ckpt",f)
return (int(s[0]) if s else -1,f)
def restore(init_op, sess, saver): # called to restore or just initialise model
list = glob(os.path.join("./params/e*"))
if list:
file = max(list,key=extract_number)
saver.restore(sess, file[:-5])
sess.run(init_op)
return
with tf.Graph().as_default() as g:
# build models
total_batch = data.train.num_examples / batch_size
epochLimit = 51
saver = tf.train.Saver()
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
saver = tf.train.Saver()
init_op = tf.global_variables_initializer()
restore(init_op, sess, saver)
epoch = global_step.eval()
while epoch < epochLimit:
total_batch = data.train.num_examples / batch_size
for i in range(int(total_batch)):
sys.stdout.flush()
voxels = newData.eval()
batch_z = np.random.uniform(-1, 1, [batch_size, z_size]).astype(np.float32)
sess.run(opt_G, feed_dict={z:batch_z, train:True})
sess.run(opt_D, feed_dict={input:voxels, z:batch_z, train:True})
with open("out/loss.csv", 'a') as f:
batch_loss_G = sess.run(loss_G, feed_dict={z:batch_z, train:False})
batch_loss_D = sess.run(loss_D, feed_dict={input:voxels, z:batch_z, train:False})
msgOut = "Epoch: [{0}], i: [{1}], G_Loss[{2:.8f}], D_Loss[{3:.8f}]".format(epoch, i, batch_loss_G, batch_loss_D)
print(msgOut)
epoch=epoch+1
sess.run(global_step.assign(epoch))
saver.save(sess, "params/epoch{0}.ckpt".format(epoch))
batch_z = np.random.uniform(-1, 1, [batch_size, z_size]).astype(np.float32)
voxels = sess.run(x_, feed_dict={z:batch_z})
v = voxels[0].reshape([32, 32, 32]) > 0
util.save_binvox(v, "out/epoch{0}.vox".format(epoch), 32)
我还在底部使用 assign 更新了全局步骤变量。有任何想法吗？任何帮助将不胜感激。
当您在恢复后调用 sess.run(init_op) 时，这会将所有变量重置为其初始值。评论那条线，事情应该会起作用。
由于多种原因，我的原始代码是错误的，因为我尝试了很多东西。第一响应者 Alexandre Passos 给出了一个有效的观点，但我相信改变游戏的也是范围的使用（也许？）。
如果对任何人有帮助，以下是有效的更新代码：
import tensorflow as tf
import numpy as np
# more imports
def extract_number(f): # used to get latest checkpint file
s = re.findall("epoch(\d+).ckpt",f)
return (int(s[0]) if s else -1,f)
def restore(sess, saver): # called to restore or just initialise model
list = glob(os.path.join("./params/e*"))
if list:
file = max(list,key=extract_number)
saver.restore(sess, file[:-5])
return saver, True, sess
saver = tf.train.Saver()
init_op = tf.global_variables_initializer()
sess.run(init_op)
return saver, False , sess
batch_size = 100
learning_rate = 0.0001
beta1 = 0.5
z_size = 100
save_interval = 1
data = dataset.read()
total_batch = data.train.num_examples / batch_size
def fill_queue():
for i in range(int(total_batch*epochLimit)):
sess.run(enqueue_op, feed_dict={batch: data.train.next_batch(batch_size)}) # runnig in seperate thread to feed a FIFOqueue
with tf.variable_scope("glob"):
global_step = tf.get_variable(name='global_step', initializer=0,trainable=False)
# build models
epochLimit = 51
saver = tf.train.Saver()
with tf.Session() as sess:
saver,rstr,sess = restore(sess, saver)
with tf.variable_scope("glob", reuse=True):
epocht = tf.get_variable(name='global_step', trainable=False, dtype=tf.int32)
epoch = epocht.eval()
while epoch < epochLimit:
total_batch = data.train.num_examples / batch_size
for i in range(int(total_batch)):
sys.stdout.flush()
voxels = newData.eval()
batch_z = np.random.uniform(-1, 1, [batch_size, z_size]).astype(np.float32)
sess.run(opt_G, feed_dict={z:batch_z, train:True})
sess.run(opt_D, feed_dict={input:voxels, z:batch_z, train:True})
with open("out/loss.csv", 'a') as f:
batch_loss_G = sess.run(loss_G, feed_dict={z:batch_z, train:False})
batch_loss_D = sess.run(loss_D, feed_dict={input:voxels, z:batch_z, train:False})
msgOut = "Epoch: [{0}], i: [{1}], G_Loss[{2:.8f}], D_Loss[{3:.8f}]".format(epoch, i, batch_loss_G, batch_loss_D)
print(msgOut)
epoch=epoch+1
sess.run(global_step.assign(epoch))
saver.save(sess, "params/epoch{0}.ckpt".format(epoch))
batch_z = np.random.uniform(-1, 1, [batch_size, z_size]).astype(np.float32)
voxels = sess.run(x_, feed_dict={z:batch_z})
v = voxels[0].reshape([32, 32, 32]) > 0
util.save_binvox(v, "out/epoch{0}.vox".format(epoch), 32)