如何将此 Keras 代码转换为链器代码?(LSTM 自动编码器)

Question

在这里，我用 Keras 编写了 LSTM Autoencoder。我想将代码转换为链器。

import numpy as np
from keras.layers import Input, GRU
from keras.models import Model
input_feat = Input(shape=(30, 2000))
l = GRU( 100, return_sequences=True, activation="tanh", recurrent_activation="hard_sigmoid")(input_feat)
l = GRU(2000, return_sequences=True, activation="tanh", recurrent_activation="hard_sigmoid")(l)
model = Model(input_feat, l)
model.compile(optimizer="RMSprop", loss="mean_squared_error")
feat = np.load("feat.npy")
model.fit(feat, feat[:, ::-1, :], epochs=200, batch_size=250)

Feat 是 numpy，其维度是 （269， 30， 2000）。我可以运行上面的代码，结果是合理的。我在Chainer代码下面写过。

import numpy as np
from chainer import Chain, Variable, optimizers
import chainer.functions as F
import chainer.links as L
class GRUAutoEncoder(Chain):
    def __init__(self):
        super().__init__()
        with self.init_scope():
            self.encode = L.GRU(2000, 100)
            self.decode = L.GRU(100, 2000)
    def __call__(self, h, mode):
        if mode == "encode":
            h = F.tanh(self.encode(h))
            return h 
        if mode == "decode":
            h = F.tanh(self.decode(h))
            return h
    def reset(self):
        self.encode.reset_state()
        self.decode.reset_state()
def main():
    feat = np.load("feat.npy") #(269, 30, 2000)
    gru_autoencoder = GRUAutoEncoder()
    optimizer = optimizers.RMSprop(lr=0.01).setup(gru_autoencoder)
    N = len(feat)
    batch_size = 250
    for epoch in range(200):
        index = np.random.randint(0, N-batch_size+1)
        input_splices = feat[index:index+batch_size] #(250, 30, 2000)
        #Encoding
        input_vector = np.zeros((30, batch_size, 2000), dtype="float32")
        h = []
        for i in range(frame_rate):
            input_vector[i] = input_splices[:, i, :] #(250, 1, 2000)
            tmp = Variable(input_vector[i])
            h.append(gru_autoencoder(tmp, "encode")) #(250, 100)
        #Decoding
        output_vector = []
        for i in range(frame_rate):
            tmp = h[i]
            output_vector.append(gru_autoencoder(tmp, "decode"))
        x = input_vector[0]
        t = output_vector[0]
        for i in range(len(output_vector)):
            x = F.concat((x,input_vector[i]), axis=1)
            t = F.concat((t,output_vector[i]), axis=1)
        loss = F.mean_squared_error(x, t)
        gru_autoencoder.cleargrads()
        loss.backward()
        optimizer.update()
        gru_autoencoder.reset()
if __name__ == "__main__":
    main()

但是上述代码的结果并不合理。我认为 Chainer 代码有问题，但我找不到它在哪里。

在 Keras 代码中，

model.fit(feat, feat[:, ::-1, :])

因此，我试图在 Chainer 代码中反转output_vector，

output_vector.reverse()

但结果仍然不合理。

Answer 1

.. 注意：这个答案是[日语SO]的翻译。https://ja.stackoverflow.com/questions/52162/keras%E3%81%AE%E3%82%B3%E3%83%BC%E3%83%89%E3%82%92chainer%E3%81%AB%E6%9B%B8%E3%81%8D%E6%8F%9B%E3%81%88%E3%81%9F%E3%81%84lstm-autoencoder%E3%81%AE%E5%AE%9F%E8%A3%85/52213#52213）

1. 你应该避免使用L.GRU，而应该使用L.NStepGRU，因为为了L.GRU你必须编写"递归感知"代码。换句话说，您必须将L.GRU多次应用于一个时间序列，因此必须非常小心地对待"批处理"。 L.NStepGRU（n_layers=1）包装批处理，因此它将是用户友好的。
2. L.StepGRU 的实例采用两个输入参数：一个是初始状态，另一个是组成批处理的时间序列列表。通常，初始状态是 None .

因此，您的问题的完整答案如下。

### dataset.py
from chainer.dataset import DatasetMixin
import numpy as np

class MyDataset(DatasetMixin):
    N_SAMPLES = 269
    N_TIMESERIES = 30
    N_DIMS = 2000
    def __init__(self):
        super().__init__()
        self.data = np.random.randn(self.N_SAMPLES, self.N_TIMESERIES, self.N_DIMS) 
            .astype(np.float32)
    def __len__(self):
        return self.N_SAMPLES
    def get_example(self, i):
        return self.data[i, :, :]

### model.py
import chainer
from chainer import links as L
from chainer import functions as F
from chainer.link import Chain

class MyModel(Chain):
    N_IN_CHANNEL = 2000
    N_HIDDEN_CHANNEL = 100
    N_OUT_CHANNEL = 2000
    def __init__(self):
        super().__init__()
        self.encoder = L.NStepGRU(n_layers=1, in_size=self.N_IN_CHANNEL, out_size=self.N_HIDDEN_CHANNEL, dropout=0)
        self.decoder = L.NStepGRU(n_layers=1, in_size=self.N_HIDDEN_CHANNEL, out_size=self.N_OUT_CHANNEL, dropout=0)
    def to_gpu(self, device=None):
        self.encoder.to_gpu(device)
        self.decoder.to_gpu(device)
    def to_cpu(self):
        self.encoder.to_cpu()
        self.decoder.to_cpu()
    @staticmethod
    def flip_list(source_list):
        return [F.flip(source, axis=1) for source in source_list]
    def __call__(self, source_list):
        """
        .. note:
            This implementation makes use of "auto-encoding"
            by avoiding redundant copy in GPU device.
            In the typical implementation, this function should receive
            both of ``source_list`` and ``target_list``.
        """
        target_list = self.flip_list(source_list)
        _, h_list = self.encoder(hx=None, xs=source_list)
        _, predicted_list = self.decoder(hx=None, xs=h_list)
        diff_list = [F.mean_squared_error(target, predicted).reshape((1,)) for target, predicted in zip(target_list, predicted_list)]
        loss = F.sum(F.concat(diff_list, axis=0))
        chainer.report({'loss': loss}, self)
        return loss

### converter.py (referring examples/seq2seq/seq2seq.py)
from chainer.dataset import to_device

def convert(batch, device):
    """
    .. note:
        batch must be list(batch_size) of array
    """
    if device is None:
        return batch
    else:
        return [to_device(device, x) for x in batch]

### train.py
from chainer.iterators import SerialIterator
from chainer.optimizers import RMSprop
from chainer.training.updaters import StandardUpdater
from chainer.training.trainer import Trainer
dataset = MyDataset()
BATCH_SIZE = 32
iterator = SerialIterator(dataset, BATCH_SIZE)
model = MyModel()
optimizer = RMSprop()
optimizer.setup(model)
updater = StandardUpdater(iterator, optimizer, convert, device=0)
trainer = Trainer(updater, (100, 'iteration'))
from chainer.training.extensions import snapshot_object
trainer.extend(snapshot_object(model, "model_iter_{.updater.iteration}"), trigger=(10, 'iteration'))
from chainer.training.extensions import LogReport, PrintReport, ProgressBar
trainer.extend(LogReport(['epoch', 'iteration', 'main/loss'], (1, 'iteration')))
trainer.extend(PrintReport(['epoch', 'iteration', 'main/loss']), trigger=(1, 'iteration'))
trainer.extend(ProgressBar(update_interval=1))
trainer.run()