Python 培训后如何保存/恢复模型？
在Tensorflow中训练模型后：Python 培训后如何保存/恢复模型？,python,tensorflow,Python,Tensorflow,在Tensorflow中训练模型后： 如何保存经过培训的模型 以后如何恢复此保存的模型 对于TensorFlow版本
对于TensorFlow版本<0.11.0RC1： 保存的检查点包含模型中
变量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})
以下是for
变量保存程序的设置模型有两部分，模型定义由Supervisor
以graph.pbtxt
的形式保存在模型目录中，张量的数值保存在检查点文件中，如model.ckpt-1003418
可以使用tf.import\u graph\u def
恢复模型定义，并使用Saver
恢复权重
但是，Saver
使用特殊的集合来保存附加到模型图的变量列表，并且此集合没有使用import\u Graph\u def初始化，因此您目前不能同时使用这两个集合（这在我们的路线图上）。现在，您必须使用Ryan Sepassi的方法——手动构造一个具有相同节点名称的图，并使用Saver
将权重加载到其中
（或者，您可以通过使用import\u graph\u def
，手动创建变量，并使用tf.为每个变量向集合（tf.GraphKeys.variables，variable）
添加变量，然后使用Saver
）进行破解）正如Yaroslav所说，您可以通过导入图形从图形定义和检查点进行黑客还原，手动创建变量，然后使用保护程序
我实现这个是为了我个人使用，所以我想在这里分享代码
链接：
（当然，这是一种黑客行为，不能保证以这种方式保存的模型在TensorFlow的未来版本中仍然可读。）您也可以签入，它提供了save
和restore
方法，可以帮助您轻松管理模型。它有一些参数，您还可以控制备份模型的频率 如果它是一个内部保存的模型，您只需为所有变量指定一个恢复器即可
restorer = tf.train.Saver(tf.all_variables())
并使用它还原当前会话中的变量：
restorer.restore(self._sess, model_file)
对于外部模型，需要指定从its变量名到变量名的映射。可以使用命令查看模型变量名称
python /path/to/tensorflow/tensorflow/python/tools/inspect_checkpoint.py --file_name=/path/to/pretrained_model/model.ckpt
可以在tensorflow源的“/tensorflow/python/tools”文件夹中找到inspect_checkpoint.py脚本
要指定映射，可以使用my，它包含一组用于训练和重新训练不同模型的类和脚本。它包括一个重新培训ResNet模型的示例，位于TensorFlow版本0.11.0RC1中（及之后），您可以通过调用tf.train.export\u meta\u graph
和tf.train.import\u meta\u graph
直接保存和恢复模型
保存模型
恢复模型
如本期所述：
而不是
saver.restore('my_model_final.ckpt')
你也可以采取更简单的方法
步骤1：初始化所有变量
步骤2：将会话保存在modelSaver
中并保存它
步骤3：恢复模型
步骤4：检查变量
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)
在大多数情况下，使用tf.train.Saver
从磁盘保存和恢复是您的最佳选择：
... # build your model
saver = tf.train.Saver()
with tf.Session() as sess:
... # train the model
saver.save(sess, "/tmp/my_great_model")
with tf.Session() as sess:
saver.restore(sess, "/tmp/my_great_model")
... # use the model
还可以保存/恢复图形结构本身（有关详细信息，请参阅）。默认情况下，Saver
将图形结构保存到.meta
文件中。您可以调用import\u meta\u graph（）
将其还原。它恢复图形结构并返回一个保存程序
，您可以使用该保存程序恢复模型的状态：
saver = tf.train.import_meta_graph("/tmp/my_great_model.meta")
with tf.Session() as sess:
saver.restore(sess, "/tmp/my_great_model")
... # use the model
然而，在某些情况下，您需要更快的速度。例如，如果实现提前停止，则希望在训练期间每次模型改进时保存检查点（在验证集上测量），然后如果一段时间内没有进展，则希望回滚到最佳模型。如果每次模型改进时都将其保存到磁盘，则会大大降低训练速度。诀窍是将变量状态保存到内存中，然后稍后再恢复：
... # build your model
# get a handle on the graph nodes we need to save/restore the model
graph = tf.get_default_graph()
gvars = graph.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
assign_ops = [graph.get_operation_by_name(v.op.name + "/Assign") for v in gvars]
init_values = [assign_op.inputs[1] for assign_op in assign_ops]
with tf.Session() as sess:
... # train the model
# when needed, save the model state to memory
gvars_state = sess.run(gvars)
# when needed, restore the model state
feed_dict = {init_value: val
for init_value, val in zip(init_values, gvars_state)}
sess.run(assign_ops, feed_dict=feed_dict)
快速解释：当您创建变量X
时，TensorFlow会自动创建赋值操作X/Assign
，以设置变量的初始值。我们不需要创建占位符和额外的分配操作（这只会使图形变得混乱），而只需要使用这些现有的分配操作。每个赋值运算的第一个输入是对它应该初始化的变量的引用，第二个输入（assign\u op.inputs[1]
）是初始值。因此，为了设置我们想要的任何值（而不是初始值），我们需要使用feed\u dict
并替换初始值。是的，TensorFlow允许您为任何操作输入值，而不仅仅是占位符，因此这很好。以下是我的简单解决方案，用于两种基本情况，它们不同于您是要从文件加载图形还是在运行时生成图形
这个答案适用于Tensorflow 0.12+（包括1.0）
用代码重建图形
拯救
加载
还从文件加载图形
使用此技术时，请确保所有层/变量都显式设置了唯一的名称。否则Tensorflow将使名称本身唯一，因此它们与文件中存储的名称不同。这在以前的技术中不是问题，becau
model_saver = tf.train.Saver()
# Train the model and save it in the end
model_saver.save(session, "saved_models/CNN_New.ckpt")
with tf.Session(graph=graph_cnn) as session:
model_saver.restore(session, "saved_models/CNN_New.ckpt")
print("Model restored.")
print('Initialized')
W1 = session.run(W1)
print(W1)
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)
... # build your model
saver = tf.train.Saver()
with tf.Session() as sess:
... # train the model
saver.save(sess, "/tmp/my_great_model")
with tf.Session() as sess:
saver.restore(sess, "/tmp/my_great_model")
... # use the model
saver = tf.train.import_meta_graph("/tmp/my_great_model.meta")
with tf.Session() as sess:
saver.restore(sess, "/tmp/my_great_model")
... # use the model
... # build your model
# get a handle on the graph nodes we need to save/restore the model
graph = tf.get_default_graph()
gvars = graph.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
assign_ops = [graph.get_operation_by_name(v.op.name + "/Assign") for v in gvars]
init_values = [assign_op.inputs[1] for assign_op in assign_ops]
with tf.Session() as sess:
... # train the model
# when needed, save the model state to memory
gvars_state = sess.run(gvars)
# when needed, restore the model state
feed_dict = {init_value: val
for init_value, val in zip(init_values, gvars_state)}
sess.run(assign_ops, feed_dict=feed_dict)
graph = ... # build the graph
saver = tf.train.Saver() # create the saver after the graph
with ... as sess: # your session object
saver.save(sess, 'my-model')
graph = ... # build the graph
saver = tf.train.Saver() # create the saver after the graph
with ... as sess: # your session object
saver.restore(sess, tf.train.latest_checkpoint('./'))
# now you can use the graph, continue training or whatever
graph = ... # build the graph
for op in [ ... ]: # operators you want to use after restoring the model
tf.add_to_collection('ops_to_restore', op)
saver = tf.train.Saver() # create the saver after the graph
with ... as sess: # your session object
saver.save(sess, 'my-model')
with ... as sess: # your session object
saver = tf.train.import_meta_graph('my-model.meta')
saver.restore(sess, tf.train.latest_checkpoint('./'))
ops = tf.get_collection('ops_to_restore') # here are your operators in the same order in which you saved them to the collection
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)
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
# 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)
# 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)
import tensorflow as tf
# define the tensorflow network and do some trains
x = tf.placeholder("float", name="x")
w = tf.Variable(2.0, name="w")
b = tf.Variable(0.0, name="bias")
h = tf.multiply(x, w)
y = tf.add(h, b, name="y")
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# save the model
export_path = './savedmodel'
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={'x_input': tensor_info_x},
outputs={'y_output': tensor_info_y},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
prediction_signature
},
)
builder.save()
import tensorflow as tf
sess=tf.Session()
signature_key = tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
input_key = 'x_input'
output_key = 'y_output'
export_path = './savedmodel'
meta_graph_def = tf.saved_model.loader.load(
sess,
[tf.saved_model.tag_constants.SERVING],
export_path)
signature = meta_graph_def.signature_def
x_tensor_name = signature[signature_key].inputs[input_key].name
y_tensor_name = signature[signature_key].outputs[output_key].name
x = sess.graph.get_tensor_by_name(x_tensor_name)
y = sess.graph.get_tensor_by_name(y_tensor_name)
y_out = sess.run(y, {x: 3.0})
# -------------------------
# ----- Toy Context -----
# -------------------------
import tensorflow as tf
class Net(tf.keras.Model):
"""A simple linear model."""
def __init__(self):
super(Net, self).__init__()
self.l1 = tf.keras.layers.Dense(5)
def call(self, x):
return self.l1(x)
def toy_dataset():
inputs = tf.range(10.0)[:, None]
labels = inputs * 5.0 + tf.range(5.0)[None, :]
return (
tf.data.Dataset.from_tensor_slices(dict(x=inputs, y=labels)).repeat().batch(2)
)
def train_step(net, example, optimizer):
"""Trains `net` on `example` using `optimizer`."""
with tf.GradientTape() as tape:
output = net(example["x"])
loss = tf.reduce_mean(tf.abs(output - example["y"]))
variables = net.trainable_variables
gradients = tape.gradient(loss, variables)
optimizer.apply_gradients(zip(gradients, variables))
return loss
# ----------------------------
# ----- Create Objects -----
# ----------------------------
net = Net()
opt = tf.keras.optimizers.Adam(0.1)
dataset = toy_dataset()
iterator = iter(dataset)
ckpt = tf.train.Checkpoint(
step=tf.Variable(1), optimizer=opt, net=net, iterator=iterator
)
manager = tf.train.CheckpointManager(ckpt, "./tf_ckpts", max_to_keep=3)
# ----------------------------
# ----- Train and Save -----
# ----------------------------
ckpt.restore(manager.latest_checkpoint)
if manager.latest_checkpoint:
print("Restored from {}".format(manager.latest_checkpoint))
else:
print("Initializing from scratch.")
for _ in range(50):
example = next(iterator)
loss = train_step(net, example, opt)
ckpt.step.assign_add(1)
if int(ckpt.step) % 10 == 0:
save_path = manager.save()
print("Saved checkpoint for step {}: {}".format(int(ckpt.step), save_path))
print("loss {:1.2f}".format(loss.numpy()))
# ---------------------
# ----- Restore -----
# ---------------------
# In another script, re-initialize objects
opt = tf.keras.optimizers.Adam(0.1)
net = Net()
dataset = toy_dataset()
iterator = iter(dataset)
ckpt = tf.train.Checkpoint(
step=tf.Variable(1), optimizer=opt, net=net, iterator=iterator
)
manager = tf.train.CheckpointManager(ckpt, "./tf_ckpts", max_to_keep=3)
# Re-use the manager code above ^
ckpt.restore(manager.latest_checkpoint)
if manager.latest_checkpoint:
print("Restored from {}".format(manager.latest_checkpoint))
else:
print("Initializing from scratch.")
for _ in range(50):
example = next(iterator)
# Continue training or evaluate etc.
# Create some variables.
v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer)
v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer)
inc_v1 = v1.assign(v1+1)
dec_v2 = v2.assign(v2-1)
# Add an op to initialize the variables.
init_op = tf.global_variables_initializer()
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Later, launch the model, initialize the variables, do some work, and save the
# variables to disk.
with tf.Session() as sess:
sess.run(init_op)
# Do some work with the model.
inc_v1.op.run()
dec_v2.op.run()
# Save the variables to disk.
save_path = saver.save(sess, "/tmp/model.ckpt")
print("Model saved in path: %s" % save_path)
tf.reset_default_graph()
# Create some variables.
v1 = tf.get_variable("v1", shape=[3])
v2 = tf.get_variable("v2", shape=[5])
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Later, launch the model, use the saver to restore variables from disk, and
# do some work with the model.
with tf.Session() as sess:
# Restore variables from disk.
saver.restore(sess, "/tmp/model.ckpt")
print("Model restored.")
# Check the values of the variables
print("v1 : %s" % v1.eval())
print("v2 : %s" % v2.eval())
import tensorflow as tf
from tensorflow.saved_model import tag_constants
with tf.Graph().as_default():
with tf.Session() as sess:
...
# Saving
inputs = {
"batch_size_placeholder": batch_size_placeholder,
"features_placeholder": features_placeholder,
"labels_placeholder": labels_placeholder,
}
outputs = {"prediction": model_output}
tf.saved_model.simple_save(
sess, 'path/to/your/location/', inputs, outputs
)
graph = tf.Graph()
with restored_graph.as_default():
with tf.Session() as sess:
tf.saved_model.loader.load(
sess,
[tag_constants.SERVING],
'path/to/your/location/',
)
batch_size_placeholder = graph.get_tensor_by_name('batch_size_placeholder:0')
features_placeholder = graph.get_tensor_by_name('features_placeholder:0')
labels_placeholder = graph.get_tensor_by_name('labels_placeholder:0')
prediction = restored_graph.get_tensor_by_name('dense/BiasAdd:0')
sess.run(prediction, feed_dict={
batch_size_placeholder: some_value,
features_placeholder: some_other_value,
labels_placeholder: another_value
})
import os
import shutil
import numpy as np
import tensorflow as tf
from tensorflow.python.saved_model import tag_constants
def model(graph, input_tensor):
"""Create the model which consists of
a bidirectional rnn (GRU(10)) followed by a dense classifier
Args:
graph (tf.Graph): Tensors' graph
input_tensor (tf.Tensor): Tensor fed as input to the model
Returns:
tf.Tensor: the model's output layer Tensor
"""
cell = tf.nn.rnn_cell.GRUCell(10)
with graph.as_default():
((fw_outputs, bw_outputs), (fw_state, bw_state)) = tf.nn.bidirectional_dynamic_rnn(
cell_fw=cell,
cell_bw=cell,
inputs=input_tensor,
sequence_length=[10] * 32,
dtype=tf.float32,
swap_memory=True,
scope=None)
outputs = tf.concat((fw_outputs, bw_outputs), 2)
mean = tf.reduce_mean(outputs, axis=1)
dense = tf.layers.dense(mean, 5, activation=None)
return dense
def get_opt_op(graph, logits, labels_tensor):
"""Create optimization operation from model's logits and labels
Args:
graph (tf.Graph): Tensors' graph
logits (tf.Tensor): The model's output without activation
labels_tensor (tf.Tensor): Target labels
Returns:
tf.Operation: the operation performing a stem of Adam optimizer
"""
with graph.as_default():
with tf.variable_scope('loss'):
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=labels_tensor, name='xent'),
name="mean-xent"
)
with tf.variable_scope('optimizer'):
opt_op = tf.train.AdamOptimizer(1e-2).minimize(loss)
return opt_op
if __name__ == '__main__':
# Set random seed for reproducibility
# and create synthetic data
np.random.seed(0)
features = np.random.randn(64, 10, 30)
labels = np.eye(5)[np.random.randint(0, 5, (64,))]
graph1 = tf.Graph()
with graph1.as_default():
# Random seed for reproducibility
tf.set_random_seed(0)
# Placeholders
batch_size_ph = tf.placeholder(tf.int64, name='batch_size_ph')
features_data_ph = tf.placeholder(tf.float32, [None, None, 30], 'features_data_ph')
labels_data_ph = tf.placeholder(tf.int32, [None, 5], 'labels_data_ph')
# Dataset
dataset = tf.data.Dataset.from_tensor_slices((features_data_ph, labels_data_ph))
dataset = dataset.batch(batch_size_ph)
iterator = tf.data.Iterator.from_structure(dataset.output_types, dataset.output_shapes)
dataset_init_op = iterator.make_initializer(dataset, name='dataset_init')
input_tensor, labels_tensor = iterator.get_next()
# Model
logits = model(graph1, input_tensor)
# Optimization
opt_op = get_opt_op(graph1, logits, labels_tensor)
with tf.Session(graph=graph1) as sess:
# Initialize variables
tf.global_variables_initializer().run(session=sess)
for epoch in range(3):
batch = 0
# Initialize dataset (could feed epochs in Dataset.repeat(epochs))
sess.run(
dataset_init_op,
feed_dict={
features_data_ph: features,
labels_data_ph: labels,
batch_size_ph: 32
})
values = []
while True:
try:
if epoch < 2:
# Training
_, value = sess.run([opt_op, logits])
print('Epoch {}, batch {} | Sample value: {}'.format(epoch, batch, value[0]))
batch += 1
else:
# Final inference
values.append(sess.run(logits))
print('Epoch {}, batch {} | Final inference | Sample value: {}'.format(epoch, batch, values[-1][0]))
batch += 1
except tf.errors.OutOfRangeError:
break
# Save model state
print('\nSaving...')
cwd = os.getcwd()
path = os.path.join(cwd, 'simple')
shutil.rmtree(path, ignore_errors=True)
inputs_dict = {
"batch_size_ph": batch_size_ph,
"features_data_ph": features_data_ph,
"labels_data_ph": labels_data_ph
}
outputs_dict = {
"logits": logits
}
tf.saved_model.simple_save(
sess, path, inputs_dict, outputs_dict
)
print('Ok')
# Restoring
graph2 = tf.Graph()
with graph2.as_default():
with tf.Session(graph=graph2) as sess:
# Restore saved values
print('\nRestoring...')
tf.saved_model.loader.load(
sess,
[tag_constants.SERVING],
path
)
print('Ok')
# Get restored placeholders
labels_data_ph = graph2.get_tensor_by_name('labels_data_ph:0')
features_data_ph = graph2.get_tensor_by_name('features_data_ph:0')
batch_size_ph = graph2.get_tensor_by_name('batch_size_ph:0')
# Get restored model output
restored_logits = graph2.get_tensor_by_name('dense/BiasAdd:0')
# Get dataset initializing operation
dataset_init_op = graph2.get_operation_by_name('dataset_init')
# Initialize restored dataset
sess.run(
dataset_init_op,
feed_dict={
features_data_ph: features,
labels_data_ph: labels,
batch_size_ph: 32
}
)
# Compute inference for both batches in dataset
restored_values = []
for i in range(2):
restored_values.append(sess.run(restored_logits))
print('Restored values: ', restored_values[i][0])
# Check if original inference and restored inference are equal
valid = all((v == rv).all() for v, rv in zip(values, restored_values))
print('\nInferences match: ', valid)
$ python3 save_and_restore.py
Epoch 0, batch 0 | Sample value: [-0.13851789 -0.3087595 0.12804556 0.20013677 -0.08229901]
Epoch 0, batch 1 | Sample value: [-0.00555491 -0.04339041 -0.05111827 -0.2480045 -0.00107776]
Epoch 1, batch 0 | Sample value: [-0.19321944 -0.2104792 -0.00602257 0.07465433 0.11674127]
Epoch 1, batch 1 | Sample value: [-0.05275984 0.05981954 -0.15913513 -0.3244143 0.10673307]
Epoch 2, batch 0 | Final inference | Sample value: [-0.26331693 -0.13013336 -0.12553 -0.04276478 0.2933622 ]
Epoch 2, batch 1 | Final inference | Sample value: [-0.07730117 0.11119192 -0.20817074 -0.35660955 0.16990358]
Saving...
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:SavedModel written to: b'/some/path/simple/saved_model.pb'
Ok
Restoring...
INFO:tensorflow:Restoring parameters from b'/some/path/simple/variables/variables'
Ok
Restored values: [-0.26331693 -0.13013336 -0.12553 -0.04276478 0.2933622 ]
Restored values: [-0.07730117 0.11119192 -0.20817074 -0.35660955 0.16990358]
Inferences match: True
import tensorflow as tf
import os
tf.enable_eager_execution()
checkpoint_directory = "/tmp/training_checkpoints"
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model)
status = checkpoint.restore(tf.train.latest_checkpoint(checkpoint_directory))
for _ in range(num_training_steps):
optimizer.minimize( ... ) # Variables will be restored on creation.
status.assert_consumed() # Optional sanity checks.
checkpoint.save(file_prefix=checkpoint_prefix)
self.saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
...
self.saver.save(sess, filename)
saver = tf.train.import_meta_graph(filename)
name = 'name given when you saved the file'
with tf.Session() as sess:
saver.restore(sess, name)
print(sess.run('W1:0')) #example to retrieve by variable name
saver = tf.train.Saver()
saver.save(sess, 'path of save/fileName.ckpt')
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint('path of save/')
sess.run(....)
# Save the model
model.save('path_to_my_model.h5')
new_model = tensorflow.keras.models.load_model('path_to_my_model.h5')
tensorflow (1.13.1)
tensorflow-gpu (1.13.1)
model.save("model.h5")
model = tf.keras.models.load_model("model.h5")
tf.keras.models.save_model(model_name, filepath, save_format)
model = tf.keras.models.load_model(filepath)
import tensorflow as tf
from tensorflow import keras
mnist = tf.keras.datasets.mnist
#import data
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
# create a model
def create_model():
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
# compile the model
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
# Create a basic model instance
model=create_model()
model.fit(x_train, y_train, epochs=1)
loss, acc = model.evaluate(x_test, y_test,verbose=1)
print("Original model, accuracy: {:5.2f}%".format(100*acc))
# Save entire model to a HDF5 file
model.save('./model_path/my_model.h5')
# Recreate the exact same model, including weights and optimizer.
new_model = keras.models.load_model('./model_path/my_model.h5')
loss, acc = new_model.evaluate(x_test, y_test)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))
model.fit(x_train, y_train, epochs=5)
loss, acc = model.evaluate(x_test, y_test,verbose=1)
print("Original model, accuracy: {:5.2f}%".format(100*acc))
# Save the weights
model.save_weights('./checkpoints/my_checkpoint')
# Restore the weights
model = create_model()
model.load_weights('./checkpoints/my_checkpoint')
loss,acc = model.evaluate(x_test, y_test)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))
# include the epoch in the file name. (uses `str.format`)
checkpoint_path = "training_2/cp-{epoch:04d}.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)
cp_callback = tf.keras.callbacks.ModelCheckpoint(
checkpoint_path, verbose=1, save_weights_only=True,
# Save weights, every 5-epochs.
period=5)
model = create_model()
model.save_weights(checkpoint_path.format(epoch=0))
model.fit(train_images, train_labels,
epochs = 50, callbacks = [cp_callback],
validation_data = (test_images,test_labels),
verbose=0)
latest = tf.train.latest_checkpoint(checkpoint_dir)
new_model = create_model()
new_model.load_weights(latest)
loss, acc = new_model.evaluate(test_images, test_labels)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))
import tensorflow as tf
from tensorflow import keras
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
# Custom Loss1 (for example)
@tf.function()
def customLoss1(yTrue,yPred):
return tf.reduce_mean(yTrue-yPred)
# Custom Loss2 (for example)
@tf.function()
def customLoss2(yTrue, yPred):
return tf.reduce_mean(tf.square(tf.subtract(yTrue,yPred)))
def create_model():
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy', customLoss1, customLoss2])
return model
# Create a basic model instance
model=create_model()
# Fit and evaluate model
model.fit(x_train, y_train, epochs=1)
loss, acc,loss1, loss2 = model.evaluate(x_test, y_test,verbose=1)
print("Original model, accuracy: {:5.2f}%".format(100*acc))
model.save("./model.h5")
new_model=tf.keras.models.load_model("./model.h5",custom_objects={'customLoss1':customLoss1,'customLoss2':customLoss2})
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Lambda
from tensorflow.keras import Model
def my_fun(a):
out = tf.tile(a, (1, tf.shape(a)[0]))
return out
a = Input(shape=(10,))
#out = tf.tile(a, (1, tf.shape(a)[0]))
out = Lambda(lambda x : my_fun(x))(a)
model = Model(a, out)
x = np.zeros((50,10), dtype=np.float32)
print(model(x).numpy())
model.save('my_model.h5')
#load the model
new_model=tf.keras.models.load_model("my_model.h5")
new_model = tf.keras.models.load_model("./model.h5"})
import tensorflow as tf
model.save("model_name")
model = tf.keras.models.load_model('model_name')