def train(self):
optimizer = tf.train.AdamOptimizer(learning_rate = self.config.learning_rate, \
beta1 = 0.9, beta2 = 0.999).minimize(self.loss)
#grads = optimizer.compute_gradients(self.loss)
#for i, (g, v) in enumerate(grads):
# if g is not None:
# grads[i] = (tf.clip_by_norm(g, 5), v)
#train_op = optimizer.apply_gradients(grads)
self.sess = tf.Session()
s = self.sess
writer = tf.summary.FileWriter("./log", graph = s.graph)
tf.summary.scalar("loss", self.loss)
merged_summary = tf.summary.merge_all()
cnt_total = 0
s.run(tf.global_variables_initializer())
for epoch in range(self.config.epoch_num):
print("In epoch %d " %epoch)
cnt = 0
for img, label, seq_len in self.datasrc.get_iter(16, self.config.batch_size):
loss, _, summary = s.run([self.loss, optimizer, merged_summary], feed_dict = { \
self.input : img,
self.output : tf.SparseTensorValue(*label),
self.seq_len : [self.config.split_num]*len(seq_len),
# self.seq_len : seq_len,
self.keep_prob : 1.0,
})
#print("loss %f" %loss)
writer.add_summary(summary, cnt_total)
sys.stdout.write("Current loss: %.3e, current batch: %d \r" %(loss,cnt))
cnt += 1
cnt_total += 1
if epoch % self.config.nsave == self.config.nsave - 1:
self.saver.save(s, "./log/model_epoch_%d.ckpt" %(epoch + 1))
print("")
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