def remove_training_directory(self, train_dir):
try:
logging.info("%s: Removing existing train directory.",
task_as_string(self.task))
gfile.DeleteRecursively(train_dir)
except:
logging.error(
"%s: Failed to delete directory " + train_dir +
" when starting a new model. Please delete it manually and" +
" try again.", task_as_string(self.task))
def remove_training_directory(self, train_dir):
"""Removes the training directory."""
if tf.gfile.Exists(train_dir):
try:
logging.info("{}: Removing existing train dir.".format(
task_as_string(self.task)))
gfile.DeleteRecursively(train_dir)
except:
logging.error(
"{}: Failed to delete dir {} when starting a new model. Delete it manually and try again."
.format(task_as_string(self.task), train_dir))
def create_captcha_dataset(size=100,
data_dir='./data/',
height=60,
width=160,
image_format='.png'):
if gfile.Exists(data_dir):
gfile.DeleteRecursively(data_dir)
gfile.MakeDirs(data_dir)
captcha = ImageCaptcha(width=width, height=height)
for _ in range(size):
text = gen_random_text(CAPTCHA_CHARSET, CAPTCHA_LENGTH)
captcha.write(text, data_dir + text + image_format)
return None
def create_captcha_dataset(size=100,
data_dir='./data/',
height=60,
width=160,
image_format='.png'):
"""创建并保存验证码数据集"""
# 清空存储目录并重新创建
if gfile.Exists(data_dir):
gfile.DeleteRecursively(data_dir)
gfile.MakeDirs(data_dir)
# 创建ImageCaptcha实例
captcha = ImageCaptcha(width=width, height=height)
for _ in range(size):
# 生成随机验证码
text = gen_random_text(CAPTCHA_CHARSET, CAPTCHA_LEN)
captcha.write(text, data_dir + text + image_format)
def recover_session(self):
# Recover session
saver = None
latest_checkpoint = tf.train.latest_checkpoint(self.train_dir)
if self.config.start_new_model:
logging.info("'start_new_model' flag is set. Removing existing train dir.")
try:
gfile.DeleteRecursively(self.train_dir)
except:
logging.error(
"Failed to delete directory " + self.train_dir +
" when starting a new model. Please delete it manually and" +
" try again.")
elif not latest_checkpoint:
logging.info("No checkpoint file found. Building a new model.")
else:
meta_filename = latest_checkpoint + ".meta"
if not gfile.Exists(meta_filename):
logging.info("No meta graph file found. Building a new model.")
else:
logging.info("Restoring from meta graph file %s", meta_filename)
saver = tf.train.import_meta_graph(meta_filename)
return saver
def main(unused_argv):
logging.set_verbosity(tf.logging.INFO)
print("tensorflow version: %s" % tf.__version__)
is_chief = (FLAGS.task == 0)
# Recover session
saver = None
latest_checkpoint = tf.train.latest_checkpoint(FLAGS.train_dir)
if FLAGS.start_new_model:
logging.info("'start_new_model' flag is set. Removing existing train dir.")
try:
gfile.DeleteRecursively(FLAGS.train_dir)
except:
logging.error(
"Failed to delete directory " + FLAGS.train_dir +
" when starting a new model. Please delete it manually and" +
" try again.")
elif not latest_checkpoint:
logging.info("No checkpoint file found. Building a new model.")
else:
meta_filename = latest_checkpoint + ".meta"
if not gfile.Exists(meta_filename):
logging.info("No meta graph file found. Building a new model.")
else:
logging.info("Restoring from meta graph file %s", meta_filename)
saver = tf.train.import_meta_graph(meta_filename)
if not saver:
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
FLAGS.feature_names, FLAGS.feature_sizes)
if FLAGS.frame_features:
reader = readers.YT8MFrameFeatureReader(
feature_names=feature_names,
feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(
feature_names=feature_names,
feature_sizes=feature_sizes)
model = find_class_by_name(FLAGS.model,
[frame_level_models, video_level_models])()
label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])()
optimizer_class = find_class_by_name(FLAGS.optimizer, [tf.train])
build_graph(reader=reader,
model=model,
optimizer_class=optimizer_class,
clip_gradient_norm=FLAGS.clip_gradient_norm,
train_data_pattern=FLAGS.train_data_pattern,
label_loss_fn=label_loss_fn,
base_learning_rate=FLAGS.base_learning_rate,
learning_rate_decay=FLAGS.learning_rate_decay,
learning_rate_decay_examples=FLAGS.learning_rate_decay_examples,
regularization_penalty=FLAGS.regularization_penalty,
num_readers=FLAGS.num_readers,
batch_size=FLAGS.batch_size,
num_epochs=FLAGS.num_epochs)
logging.info("built graph")
saver = tf.train.Saver(max_to_keep=0, keep_checkpoint_every_n_hours=0.25)
train_loop(is_chief=is_chief,
train_dir=FLAGS.train_dir,
saver=saver,
master=FLAGS.master)
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.tight_layout()
plt.show()
import os
import tensorflow.gfile as gfile
save_dir = "./mnist/model/"
if gfile.Exists(save_dir):
gfile.DeleteRecursively(save_dir)
gfile.MakeDirs(save_dir)
model_name = 'keras_mnist.h5'
model_path = os.path.join(save_dir, model_name)
model.save(model_path)
print('Saved trained model at %s ' % model_path)
from keras.models import load_model
mnist_model = load_model(model_path)
loss_and_metrics = mnist_model.evaluate(X_test, Y_test, verbose=2)
print("Test Loss: {}".format(loss_and_metrics[0]))
print("Test Accuracy: {}%".format(loss_and_metrics[1] * 100))
# 数据可视化
fig = plt.figure(),
plt.subplot(2, 1, 1),
plt.plot(history.history['acc']),
plt.plot(history.history['val_acc']),
plt.title('Model Accuracy'),
plt.ylabel('accuracy'),
plt.xlabel('epoch'),
plt.legend(['train', 'test'], loc='lower right'),
plt.subplot(2, 1, 2),
plt.plot(history.history['loss']),
plt.plot(history.history['val_loss']),
plt.title('Model Loss'),
plt.ylabel('loss'),
plt.xlabel('epoch'),
plt.legend(['train', 'test'], loc='upper right'),
plt.tight_layout(),
plt.show()
# 保存模型
save_path = './model/'
if gfile.Exists(save_path):
gfile.DeleteRecursively(save_path)
gfile.MakeDirs(save_path)
model_name = 'softmax.h5'
model_path = os.path.join(save_path, model_name)
model.save(model_path)