使用trax进行多元回归

编程问答 2022-08-14

问题描述

如何使用Trax设置多元回归问题？

我从下面的代码中从AssertionError: Invalid shape (16,2); expected (16,).对象中获得了L2Loss。

以下是我尝试使sentiment analysis example适应回归问题：

import os
import trax
from trax import layers as tl
from trax.supervised import training
import numpy
import random


#train_stream = trax.data.TFDS('imdb_reviews',keys=('text','label'),train=True)()
#eval_stream = trax.data.TFDS('imdb_reviews',train=False)()


def generate_samples():
    # (text,lat/lon)
    data= [
        ("Aberdeen MS",numpy.array((33.824742,-88.554591)) ),("Aberdeen SD",numpy.array((45.463186,-98.471033))),("Aberdeen WA",numpy.array((46.976432,-123.795781))),("Amite City LA",numpy.array((30.733723,-90.5208))),("Amory MS",numpy.array((33.984789,-88.48001))),("Amouli AS",numpy.array((-14.26556,-170.589772))),("Amsterdam NY",numpy.array((42.953149,-74.19505)))
    ]
    for i in range(1024*8):
        yield random.choice(data)


train_stream = generate_samples()
eval_stream = generate_samples()

model = tl.Serial(
    tl.Embedding(vocab_size=8192,d_feature=256),tl.Mean(axis=1),# Average on axis 1 (length of sentence).
    tl.Dense(2),# Regress to lat/lon
#    tl.Logsoftmax()   # Produce log-probabilities.
)

# You can print model structure.
print(model)



print(next(train_stream))  # See one example.

data_pipeline = trax.data.Serial(
    trax.data.Tokenize(vocab_file='en_8k.subword',keys=[0]),trax.data.Shuffle(),#    trax.data.FilterByLength(max_length=2048,length_keys=[0]),trax.data.BucketByLength(boundaries=[   8,128,],batch_sizes=[256,64,4],trax.data.AddLossWeights()
  )

train_batches_stream = data_pipeline(train_stream)
eval_batches_stream = data_pipeline(eval_stream)
example_batch = next(train_batches_stream)
print(f'shapes = {[x.shape for x in example_batch]}')  # Check the shapes.:wq



# Training task.
train_task = training.TrainTask(
    labeled_data=train_batches_stream,#    loss_layer=tl.CrossEntropyLoss(),loss_layer=tl.L2Loss(),optimizer=trax.optimizers.Adam(0.01),n_steps_per_checkpoint=500,)

# Evaluaton task.
eval_task = training.EvalTask(
    labeled_data=eval_batches_stream,metrics=[tl.L2Loss(),n_eval_batches=20  # For less variance in eval numbers.
)
# Training loop saves checkpoints to output_dir.
output_dir = os.path.expanduser('~/output_dir/')
training_loop = training.Loop(model,train_task,eval_tasks=[eval_task],output_dir=output_dir)
# Run 2000 steps (batches).
training_loop.run(2000)

解决方法

问题可能可能在generate_samples()生成器中：这仅产生1024*8（=8192）个样本。如果我替换行

for i in range(1024*8):

作者

while True:

这样可以生成无限数量的样本，您的示例可以在我的机器上运行。

由于generate_samples()仅产生8192个样本，因此train_batches_stream仅产生32个批次，每个批次256个样本，因此您最多只能训练{ {1}}个步骤。但是，您要求执行32步骤。

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