print "Loaded"
print "Training ===>"
with tf.Graph().as_default():
session_conf = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
sess = tf.Session(config=session_conf)
with sess.as_default():
char_cnn = CharConvNet(
conv_layers=config.model.conv_layers,
fully_layers=config.model.fully_connected_layers,
l0=config.l0,
alphabet_size=config.alphabet_size,
no_of_classes=config.no_of_classes,
th=config.model.th)
global_step = tf.Variable(0, trainable=False)
# boundaries = []
# br = config.training.base_rate
# values = [br]
# for i in range(1, 10):
# values.append(br / (2 ** i))
# boundaries.append(15000 * i)
# values.append(br / (2 ** (i + 1)))
# print(values)
# print(boundaries)
train_data.get_length() / config.batch_size) + 1
num_batch_dev = dev_data.get_length()
print('Loaded')
print('Training')
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=True, # 如果指定的设备不存在，允许TF自动分配设备
log_device_placement=False) # 不打印设备分配日志
sess = tf.Session(config=session_conf)
with sess.as_default():
char_cnn = CharConvNet(
conv_layers=config.model.conv_layers,
fully_layers=config.model.fully_connected_layers,
max_length=config.max_length,
no_of_classes=config.no_of_classes,
th=config.model.th)
global_step = tf.Variable(0, trainable=False)
optimizer = tf.train.AdamOptimizer(config.learning_rate)
'''可以使用minimize()函数取代这两步'''
grads_and_vars = optimizer.compute_gradients(
char_cnn.loss) # 计算梯度，默认对所有的Variable计算梯度
train_op = optimizer.apply_gradients(
grads_and_vars, global_step=global_step) # 梯度更新
grad_summaries = []
for g, v in grads_and_vars:
if g is not None:
grad_hist_summary = tf.summary.histogram(