学习Chainer很长一段时间了，在学习caffe的时候，第一个Demo就是训练LeNet5，可是，在Chainer上，使用mnist训练集的Demo是MLP，不是LeNet5。于是自己尝试着改成用LeNet5训练，却是报错。详见：Invalid operation is performed in: Convolution2DFunction (Forward)
一直卡在这里，又一次，准备重新试试，却是又卡在这里。在重新看了Chainer的文档后，突发奇想，改了下面的代码
原来是这样的
train, test = chainer.datasets.get_mnist(withlabel=True)稍等改动后，变成这样
train, test = chainer.datasets.get_mnist(ndim=3, withlabel=True)不同点就是多传了一个参数ndim=3
于是就能跑了。
(venv) pikachu@pikachu-PC:~/PycharmProjects/LeNet$ optirun python lenet5_demo1.py -g 0 -r result/snapshot_iter_12000 -e 21
/home/pikachu/swcontest/LRCN/venv/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
GPU: 0
# unit: 1000
# Minibatch-size: 100
# epoch: 21
(1, 28, 28)
epoch main/loss validation/main/loss main/accuracy validation/main/accuracy elapsed_time
1 1.21789 1.64665 0.614184 0.9141 355.524
2 0.237945 1.54542 0.933001 0.9519 435.652
3 0.143741 1.51993 0.957601 0.9671 490.544
4 0.108724 1.50635 0.9672 0.9735 699.786
5 0.0872876 1.4972 0.973549 0.9776 3470.16
6 0.0745747 1.49964 0.977166 0.9763 3576.54
7 0.0649679 1.49016 0.980049 0.9819 3732.18
8 0.058112 1.48777 0.982265 0.9831 3806.33
9 0.0519419 1.48632 0.983882 0.9836 3826.74
10 0.0472876 1.48512 0.985599 0.9837 3835.67
11 0.0431381 1.48331 0.986548 0.9848 3842.56
12 0.0392124 1.48257 0.987849 0.9852 3848.72
13 0.0371112 1.48026 0.988915 0.9858 3855.14
14 0.0341503 1.48032 0.989432 0.9859 3861.11
15 0.0313045 1.47911 0.990248 0.9876 3867.27
16 0.0290763 1.47942 0.990982 0.9867 3873.29
17 0.0275939 1.47704 0.991282 0.9874 3879.52
18 0.0253959 1.47738 0.991748 0.9877 3885.51
19 0.0239291 1.477 0.992265 0.9875 3891.59
20 0.0223463 1.47749 0.992798 0.9871 3897.57
21 0.0208822 1.47678 0.993432 0.9874 3908.42
(venv) pikachu@pikachu-PC:~/PycharmProjects/LeNet$ 全部代码
#!/usr/bin/env python
import argparse
import numpy as np
import chainer
from chainer.backends import cuda
from chainer import Function, gradient_check, report, training, utils, Variable
from chainer import datasets, iterators, optimizers, serializers
from chainer import Link, Chain, ChainList
import chainer.functions as F
import chainer.links as L
from chainer.training import extensions
# Network definition
class MLP(chainer.Chain):
def __init__(self, n_units, n_out):
super(MLP, self).__init__()
with self.init_scope():
# the size of the inputs to each layer will be inferred
self.l1 = L.Linear(None, n_units) # n_in -> n_units
self.l2 = L.Linear(None, n_units) # n_units -> n_units
self.l3 = L.Linear(None, n_out) # n_units -> n_out
def __call__(self, x):
h1 = F.relu(self.l1(x))
h2 = F.relu(self.l2(h1))
return self.l3(h2)
class LeNet5(Chain):
def __init__(self):
super(LeNet5, self).__init__()
with self.init_scope():
self.conv1 = L.Convolution2D(in_channels=1, out_channels=6, ksize=5, stride=1)
self.conv2 = L.Convolution2D(in_channels=6, out_channels=16, ksize=5, stride=1)
self.conv3 = L.Convolution2D(in_channels=16, out_channels=120, ksize=4, stride=1)
self.fc4 = L.Linear(None, 84)
self.fc5 = L.Linear(84, 10)
def __call__(self, x):
h = F.sigmoid(self.conv1(x))
h = F.max_pooling_2d(h, 2, 2)
h = F.sigmoid(self.conv2(h))
h = F.max_pooling_2d(h, 2, 2)
h = F.sigmoid(self.conv3(h))
h = F.sigmoid(self.fc4(h))
if chainer.config.train:
return self.fc5(h)
return F.softmax(self.fc5(h))
def main():
parser = argparse.ArgumentParser(description='Chainer example: MNIST')
parser.add_argument('--batchsize', '-b', type=int, default=100,
help='Number of images in each mini-batch')
parser.add_argument('--epoch', '-e', type=int, default=20,
help='Number of sweeps over the dataset to train')
parser.add_argument('--frequency', '-f', type=int, default=0,
help='Frequency of taking a snapshot')
parser.add_argument('--gpu', '-g', type=int, default=-1,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--out', '-o', default='result',
help='Directory to output the result')
parser.add_argument('--resume', '-r', default='',
help='Resume the training from snapshot')
parser.add_argument('--unit', '-u', type=int, default=1000,
help='Number of units')
parser.add_argument('--noplot', dest='plot', action='store_false',
help='Disable PlotReport extension')
args = parser.parse_args()
print('GPU: {}'.format(args.gpu))
print('# unit: {}'.format(args.unit))
print('# Minibatch-size: {}'.format(args.batchsize))
print('# epoch: {}'.format(args.epoch))
print('')
# Set up a neural network to train
# Classifier reports softmax cross entropy loss and accuracy at every
# iteration, which will be used by the PrintReport extension below.
model = L.Classifier(LeNet5())
if args.gpu >= 0:
# Make a specified GPU current
chainer.backends.cuda.get_device_from_id(args.gpu).use()
model.to_gpu() # Copy the model to the GPU
# Setup an optimizer
optimizer = chainer.optimizers.Adam()
optimizer.setup(model)
# Load the MNIST dataset
train, test = chainer.datasets.get_mnist(ndim=3, withlabel=True)
print(train[0][0].shape)
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
test_iter = chainer.iterators.SerialIterator(test, args.batchsize,
repeat=False, shuffle=False)
# Set up a trainer
updater = training.updaters.StandardUpdater(
train_iter, optimizer, device=args.gpu)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
# Evaluate the model with the test dataset for each epoch
trainer.extend(extensions.Evaluator(test_iter, model, device=args.gpu))
# Dump a computational graph from 'loss' variable at the first iteration
# The "main" refers to the target link of the "main" optimizer.
trainer.extend(extensions.dump_graph('main/loss'))
# Take a snapshot for each specified epoch
frequency = args.epoch if args.frequency == -1 else max(1, args.frequency)
trainer.extend(extensions.snapshot(), trigger=(frequency, 'epoch'))
# Write a log of evaluation statistics for each epoch
trainer.extend(extensions.LogReport())
# Save two plot images to the result dir
if args.plot and extensions.PlotReport.available():
trainer.extend(
extensions.PlotReport(['main/loss', 'validation/main/loss'],
'epoch', file_name='loss.png'))
trainer.extend(
extensions.PlotReport(
['main/accuracy', 'validation/main/accuracy'],
'epoch', file_name='accuracy.png'))
# Print selected entries of the log to stdout
# Here "main" refers to the target link of the "main" optimizer again, and
# "validation" refers to the default name of the Evaluator extension.
# Entries other than 'epoch' are reported by the Classifier link, called by
# either the updater or the evaluator.
trainer.extend(extensions.PrintReport(
['epoch', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy', 'elapsed_time']))
# Print a progress bar to stdout
trainer.extend(extensions.ProgressBar())
if args.resume:
# Resume from a snapshot
chainer.serializers.load_npz(args.resume, trainer)
# Run the training
trainer.run()
if __name__ == '__main__':
main()