def test_save_load(self):
fpath = "models/test_m1"
self.sess.save(fpath)
sess = Session.load(fpath)
def train(epochs, ds, model=None, batch_size=64, record_epochs=1):
#加载/构建session
sess = None
if model is None:
sess = Session.load(model_path)
else:
sess = Session(model, loss=losses.Mse(), optimizer=optimizers.Fixed())
train_x = ds['train_x']
train_y = ds['train_y']
test_x = ds['test_x']
test_y = ds['test_y']
batchs = int(train_x.shape[0] / batch_size)
print("epochs:%d, batchs=%d" % (epochs, batchs))
#记录训练历史
history = {
'loss': [],
'val_loss': [],
'epochs': [],
'val_x': test_x,
'val_y': test_y,
'val_pred': None
}
print("start training ")
t_start = time.time()
steps = epochs * batchs
epoch = 1
#循环训练
for step in range(steps):
start = (step % batchs) * batch_size
end = start + batch_size
batch_x = train_x[start:end]
batch_y = train_y[start:end]
loss = sess.batch_train(batch_x, batch_y)
cur_epoch = int(step / batchs) + 1
#每轮打印一次
if step > 0 and step % batchs == 0:
print((('epoch:%05d/%d loss=%f' % (cur_epoch, epochs, loss)) +
' ' * 50)[:50],
end='\r')
#记录
if step % batchs == 0 and (cur_epoch - epoch == record_epochs
or cur_epoch == epochs):
epoch = cur_epoch
y_pred = sess.model.predict(test_x)
val_loss = sess.loss(test_y, y_pred)
history['loss'].append(loss)
history['val_loss'].append(val_loss)
history['epochs'].append(epoch)
history['val_pred'] = y_pred
print((('epoch:%05d/%d loss=%f, val_loss=%f' %
(cur_epoch, epochs, loss, val_loss)) + ' ' * 50)[:50],
end='\r')
print("")
sess.save(model_path)
print("training finished cost:%f" % (time.time() - t_start))
return history