在Tensorflow中训练一个模型后:
如何拯救训练过的模型? 您以后如何恢复这个保存的模型?
在Tensorflow中训练一个模型后:
如何拯救训练过的模型? 您以后如何恢复这个保存的模型?
当前回答
在@Vishnuvardhan Janapati的回答之后,这里是另一种在TensorFlow 2.0.0下保存和重载自定义层/度量/损失模型的方法
import tensorflow as tf
from tensorflow.keras.layers import Layer
from tensorflow.keras.utils.generic_utils import get_custom_objects
# custom loss (for example)
def custom_loss(y_true,y_pred):
return tf.reduce_mean(y_true - y_pred)
get_custom_objects().update({'custom_loss': custom_loss})
# custom loss (for example)
class CustomLayer(Layer):
def __init__(self, ...):
...
# define custom layer and all necessary custom operations inside custom layer
get_custom_objects().update({'CustomLayer': CustomLayer})
通过这种方式,一旦您执行了这些代码,并使用tf.keras.models保存了您的模型。Save_model或model。save或ModelCheckpoint回调,您可以重新加载您的模型,而不需要精确的自定义对象,就像这样简单
new_model = tf.keras.models.load_model("./model.h5"})
其他回答
我正在改进我的回答,以添加更多关于保存和恢复模型的细节。
在Tensorflow 0.11版本中(及之后):
保存模型:
import tensorflow as tf
#Prepare to feed input, i.e. feed_dict and placeholders
w1 = tf.placeholder("float", name="w1")
w2 = tf.placeholder("float", name="w2")
b1= tf.Variable(2.0,name="bias")
feed_dict ={w1:4,w2:8}
#Define a test operation that we will restore
w3 = tf.add(w1,w2)
w4 = tf.multiply(w3,b1,name="op_to_restore")
sess = tf.Session()
sess.run(tf.global_variables_initializer())
#Create a saver object which will save all the variables
saver = tf.train.Saver()
#Run the operation by feeding input
print sess.run(w4,feed_dict)
#Prints 24 which is sum of (w1+w2)*b1
#Now, save the graph
saver.save(sess, 'my_test_model',global_step=1000)
恢复模型:
import tensorflow as tf
sess=tf.Session()
#First let's load meta graph and restore weights
saver = tf.train.import_meta_graph('my_test_model-1000.meta')
saver.restore(sess,tf.train.latest_checkpoint('./'))
# Access saved Variables directly
print(sess.run('bias:0'))
# This will print 2, which is the value of bias that we saved
# Now, let's access and create placeholders variables and
# create feed-dict to feed new data
graph = tf.get_default_graph()
w1 = graph.get_tensor_by_name("w1:0")
w2 = graph.get_tensor_by_name("w2:0")
feed_dict ={w1:13.0,w2:17.0}
#Now, access the op that you want to run.
op_to_restore = graph.get_tensor_by_name("op_to_restore:0")
print sess.run(op_to_restore,feed_dict)
#This will print 60 which is calculated
这里已经很好地解释了这一点和一些更高级的用例。
一个快速完整的教程,保存和恢复Tensorflow模型
这里所有的答案都很棒,但我想补充两点。
首先,详细说明@user7505159的答案,“。添加到要恢复的文件名的开头可能很重要。
例如,您可以保存没有“的图形。/"在文件名中如下所示:
# Some graph defined up here with specific names
saver = tf.train.Saver()
save_file = 'model.ckpt'
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.save(sess, save_file)
但是为了恢复图形,您可能需要在前面加上一个"。/"到file_name:
# Same graph defined up here
saver = tf.train.Saver()
save_file = './' + 'model.ckpt' # String addition used for emphasis
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, save_file)
你并不总是需要“。/”,但是它会根据你的环境和TensorFlow版本而导致问题。
它还想提到sess.run(tf.global_variables_initializer())在恢复会话之前可能很重要。
如果在尝试恢复保存的会话时收到关于未初始化变量的错误,请确保在保存程序之前包含sess.run(tf.global_variables_initializer())。恢复(sess, save_file)行。这样你就不用头疼了。
对于TensorFlow版本< 0.11.0RC1:
保存的检查点包含模型中的变量值,而不是模型/图本身,这意味着当您恢复检查点时,图应该是相同的。
这里有一个线性回归的例子,其中有一个训练循环,保存变量检查点,还有一个评估部分,将恢复之前运行中保存的变量并计算预测。当然,如果你愿意,你也可以恢复变量并继续训练。
x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)
w = tf.Variable(tf.zeros([1, 1], dtype=tf.float32))
b = tf.Variable(tf.ones([1, 1], dtype=tf.float32))
y_hat = tf.add(b, tf.matmul(x, w))
...more setup for optimization and what not...
saver = tf.train.Saver() # defaults to saving all variables - in this case w and b
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
if FLAGS.train:
for i in xrange(FLAGS.training_steps):
...training loop...
if (i + 1) % FLAGS.checkpoint_steps == 0:
saver.save(sess, FLAGS.checkpoint_dir + 'model.ckpt',
global_step=i+1)
else:
# Here's where you're restoring the variables w and b.
# Note that the graph is exactly as it was when the variables were
# saved in a prior training run.
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
...no checkpoint found...
# Now you can run the model to get predictions
batch_x = ...load some data...
predictions = sess.run(y_hat, feed_dict={x: batch_x})
下面是变量文档,涵盖了保存和恢复。这是保存程序的文档。
你可以使用Tensorflow中的saver对象来保存你训练过的模型。该对象提供保存和恢复模型的方法。
在TensorFlow中保存一个训练好的模型:
tf.train.Saver.save(sess, save_path, global_step=None, latest_filename=None,
meta_graph_suffix='meta', write_meta_graph=True,
write_state=True, strip_default_attrs=False,
save_debug_info=False)
在TensorFlow中恢复已保存的模型:
tf.train.Saver.restore(sess, save_path, latest_filename=None,
meta_graph_suffix='meta', clear_devices=False,
import_scope=None)
你也可以用更简单的方法。
步骤1:初始化所有变量
W1 = tf.Variable(tf.truncated_normal([6, 6, 1, K], stddev=0.1), name="W1")
B1 = tf.Variable(tf.constant(0.1, tf.float32, [K]), name="B1")
Similarly, W2, B2, W3, .....
步骤2:在模型Saver中保存会话并保存它
model_saver = tf.train.Saver()
# Train the model and save it in the end
model_saver.save(session, "saved_models/CNN_New.ckpt")
步骤3:恢复模型
with tf.Session(graph=graph_cnn) as session:
model_saver.restore(session, "saved_models/CNN_New.ckpt")
print("Model restored.")
print('Initialized')
步骤4:检查变量
W1 = session.run(W1)
print(W1)
在不同的python实例中运行时,使用
with tf.Session() as sess:
# Restore latest checkpoint
saver.restore(sess, tf.train.latest_checkpoint('saved_model/.'))
# Initalize the variables
sess.run(tf.global_variables_initializer())
# Get default graph (supply your custom graph if you have one)
graph = tf.get_default_graph()
# It will give tensor object
W1 = graph.get_tensor_by_name('W1:0')
# To get the value (numpy array)
W1_value = session.run(W1)