Документы
Из документов:
Сохранить
# 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)
Восстановить
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())
Tensorflow 2
Это все еще бета, поэтому я бы посоветовал против пока. Если вы все еще хотите пойти по этому пути, вот руководство по tf.saved_modelиспользованию
Тензор потока <2
simple_save
Много хорошего ответа, для полноты я добавлю свои 2 цента: simple_save . Также автономный пример кода с использованием tf.data.DatasetAPI.
Python 3; Тензор потока 1.14
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
)
Восстановление:
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
})
Автономный пример
Оригинальное сообщение в блоге
Следующий код генерирует случайные данные ради демонстрации.
- Мы начнем с создания заполнителей. Они будут хранить данные во время выполнения. Из них мы создаем,
Datasetа затем ее Iterator. Мы получаем сгенерированный тензор итератора, input_tensorкоторый называется входом для нашей модели.
- Сама модель построена из
input_tensor: двунаправленного RNN на основе GRU, за которым следует плотный классификатор. Потому что почему бы и нет.
- Потеря
softmax_cross_entropy_with_logitsоптимизирована с Adam. После 2 эпох (по 2 партии в каждой) мы сохраняем «обученную» модель с помощью tf.saved_model.simple_save. Если вы запустите код как есть, то модель будет сохранена в папке, которая называется simple/в вашем текущем рабочем каталоге.
- В новом графике мы затем восстановим сохраненную модель с помощью
tf.saved_model.loader.load. Мы берем заполнители и логиты с graph.get_tensor_by_nameи Iteratorинициализирующую операцию с graph.get_operation_by_name.
- Наконец, мы запускаем логический вывод для обоих пакетов в наборе данных и проверяем, что обе сохраненные и восстановленные модели дают одинаковые значения. Они делают!
Код:
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)
Это напечатает:
$ 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