Here, I have LSTM Autoencoder written in Keras. I want to convert the code to Chainer.
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 is numpy whose dimension is (269, 30, 2000). I could run above code and the result was reasonable. I had written below Chainer code.
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()
But the result of above code was not reasonable. I think the Chainer code has something wrong but I cannot find where it is.
In Keras code,
model.fit(feat, feat[:, ::-1, :])
So, I tried to reverse output_vector in Chainer code,
output_vector.reverse()
but the result was not still reasonable.
.. note: This answer is a translation of [Japanese SO].(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)
L.GRU and should use L.NStepGRU, because for L.GRU you have to write "recurrence-aware" code. In other words, you have to apply L.GRU multiple times to one timeseries, therefore "batch" must be treated with great care. L.NStepGRU (with n_layers=1) wraps the batch-processing, so it would be user-friendly.L.StepGRU takes two input arguments: one is initial state, and the other is a list of timeserieses, which composes a batch. Conventionally, the initial state is None.Therefore, the whole answer for your question is as follows.
### 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()