def _init_train(self):
self.train_graph = tf.Graph()
with self.train_graph.as_default():
self.train_in_seq = tf.placeholder(tf.int32,
shape=[self.batch_size, None])
self.train_in_seq_len = tf.placeholder(tf.int32,
shape=[self.batch_size])
self.train_target_seq = tf.placeholder(
tf.int32, shape=[self.batch_size, None])
self.train_target_seq_len = tf.placeholder(tf.int32,
shape=[self.batch_size])
output = seq2seq.seq2seq(self.train_in_seq, self.train_in_seq_len,
self.train_target_seq,
self.train_target_seq_len,
len(self.train_reader.vocabs),
self.num_units, self.layers, self.dropout)
self.train_output = tf.argmax(tf.nn.softmax(output), 2)
self.loss = seq2seq.seq_loss(output, self.train_target_seq,
self.train_target_seq_len)
params = tf.trainable_variables()
gradients = tf.gradients(self.loss, params)
clipped_gradients, _ = tf.clip_by_global_norm(gradients, 0.5)
self.train_op = tf.train.AdamOptimizer(
learning_rate=self.learning_rate).apply_gradients(
zip(clipped_gradients, params))
if self.param_histogram:
for v in tf.trainable_variables():
tf.summary.histogram('train_' + v.name, v)
tf.summary.scalar('loss', self.loss)
self.train_summary = tf.summary.merge_all()
self.train_init = tf.global_variables_initializer()
self.train_saver = tf.train.Saver()
self.train_session = tf.Session(graph=self.train_graph,
config=self.gpu_session_config())
def _init_train(self):
#Generate training map
self.train_graph = tf.Graph()
with self.train_graph.as_default():
self.train_in_seq = tf.placeholder(tf.int32,
shape=[self.batch_size, None])
self.train_in_seq_len = tf.placeholder(tf.int32,
shape=[self.batch_size])
self.train_target_seq = tf.placeholder(
tf.int32, shape=[self.batch_size, None])
self.train_target_seq_len = tf.placeholder(tf.int32,
shape=[self.batch_size])
self.train_target_seq_new_weight = tf.placeholder(
tf.float32,
shape=[self.batch_size, None,
len(self.train_vocabs)])
#Call the method of seq2seq, and analyze the method comprehensively in seq2seq
output = seq2seq.seq2seq(self.train_in_seq, self.train_in_seq_len,
self.train_target_seq,
self.train_target_seq_len,
len(self.train_reader.vocabs),
self.num_units, self.layers, self.dropout)
#For the output after encoding and decoding, choose the output with the maximum probability
self.train_output = tf.argmax(tf.nn.softmax(output), 2)
#Extract the loss value and modify the train here_ target_ Replace SEQ with train_ target_ seq_ new_ weight
self.loss = seq2seq.seq_loss(output,
self.train_target_seq_new_weight,
self.train_target_seq_len)
#Get trainable_ Name and value of variables
params = tf.trainable_variables()
#Miss derivation of params
gradients = tf.gradients(self.loss, params)
'''
tf.clip_by_global_norm(t_list, clip_norm, use_norm=None, name=None)
The ratio of the sum of the weight gradients is used to intercept the values of multiple tensors
t_ List is a gradient tensor, clip_ Norm is the ratio of interceptions. This function returns the intercepted gradient tensor and a global norm of all tensors.
t_ The update formula of list [i] is as follows:t_list[i] * clip_norm / max(global_norm, clip_norm)
global_norm = sqrt(sum([l2norm(t)**2 for t in t_list]))
global_ Norm is the sum of squares of all gradients, if clip_ norm > global_ Norm, no interception.
'''
#To prevent gradients from disappearing or exploding
clipped_gradients, _ = tf.clip_by_global_norm(gradients, 0.5)
#Take care of the optimizer and iterate over these parameters
self.train_op = tf.train.AdamOptimizer(
learning_rate=self.learning_rate).apply_gradients(
zip(clipped_gradients, params))
if self.param_histogram:
for v in tf.trainable_variables():
tf.summary.histogram('train_' + v.name, v)
#Checking loss changes in tensorboard
tf.summary.scalar('loss', self.loss)
self.train_summary = tf.summary.merge_all()
#The next is init and Saver operations
self.train_init = tf.global_variables_initializer()
self.train_saver = tf.train.Saver()
#Put the graph in the training session
self.train_session = tf.Session(graph=self.train_graph,
config=self.gpu_session_config())
def _init_train(self):
self.train_graph = tf.Graph()
with self.train_graph.as_default():
# 设定训练输入数据，输出数据及长度
self.train_in_seq = tf.placeholder(tf.int32,
shape=[self.batch_size, None])
self.train_in_seq_len = tf.placeholder(tf.int32,
shape=[self.batch_size])
self.train_target_seq = tf.placeholder(
tf.int32, shape=[self.batch_size, None])
self.train_target_seq_len = tf.placeholder(tf.int32,
shape=[self.batch_size])
# 利用seq2seq模型进行训练获得输出结果
output = seq2seq.seq2seq(self.train_in_seq, self.train_in_seq_len,
self.train_target_seq,
self.train_target_seq_len,
len(self.train_reader.vocabs),
self.num_units, self.layers, self.dropout)
self.train_output = tf.argmax(tf.nn.softmax(output), 2)
# 获取模型训练的损失
self.loss = seq2seq.seq_loss(output, self.train_target_seq,
self.train_target_seq_len)
# 获取训练的变量列表
params = tf.trainable_variables()
gradients = tf.gradients(self.loss, params)
# clipped_gradients防止更新太快
clipped_gradients, _ = tf.clip_by_global_norm(gradients, 0.5)
# train_op：优化
self.train_op = tf.train.AdamOptimizer(
learning_rate=self.learning_rate).apply_gradients(
zip(clipped_gradients, params))
tf.summary.scalar('loss', self.loss)
self.train_summary = tf.summary.merge_all()
self.train_init = tf.global_variables_initializer()
self.train_saver = tf.train.Saver()
self.train_session = tf.Session(graph=self.train_graph,
config=self.gpu_session_config())