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Last active June 3, 2019 07:48
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Pythonでディープラーニング入門 サンプルコード
# Run the training
trainer.run()
# ここから書き足す
# Save the trained model
chainer.serializers.save_npz("trained_mnist.model", model)
if __name__ == '__main__':
main()
import chainer
import chainer.functions as F
import chainer.links as L
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
class MLP(chainer.Chain):
def __init__(self, n_units, n_out):
super(MLP, self).__init__()
with self.init_scope():
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)
model = L.Classifier(MLP(1000, 10))
chainer.serializers.load_npz('trained_mnist.model', model)
image = Image.open("number.png").convert('L')
plt.imshow(image, cmap='gray')
plt.title('input data')
plt.show()
image = np.asarray(image).astype(np.float32) / 255
image = image.reshape((1, -1))
result = model.predictor(chainer.Variable(image))
print('predicted', ':', np.argmax(result.data))
for i in range(10):
print (str(i) , ":" , str(result.data[0,i]))
(base) C:\Users\Secollege160405\Desktop\mnist>python predict_mnist.py
predicted : 3
0 : -17.519121
1 : -6.611537
2 : -3.5029051
3 : 28.767323
4 : -16.44517
5 : 0.56008315
6 : -28.957706
7 : -5.5690756
8 : -3.870232
9 : -4.1418304
(base) C:\Users\Secollege160405\Desktop\mnist>
epoch main/loss validation/main/loss main/accuracy validation/main/accuracy elapsed_time
1 0.189386 0.088241 0.943733 0.9717 188.366
2 0.0725646 0.0875341 0.977683 0.9714 378.107
3 0.049227 0.0849267 0.984167 0.9735 564.709
#!/usr/bin/env python
import argparse
import chainer
import chainer.functions as F
import chainer.links as L
from chainer import training
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 forward(self, x):
h1 = F.relu(self.l1(x))
h2 = F.relu(self.l2(h1))
return self.l3(h2)
# さっきのコマンドでつけたオプション -g -e などの内容
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=-1, 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('')
# Model の生成
# 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(MLP(args.unit, 10)) # 10 は最終的にアウトプットする値の個数
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)
# 以降で Iterator を生成
# Load the MNIST dataset
train, test = chainer.datasets.get_mnist()
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
test_iter = chainer.iterators.SerialIterator(test, args.batchsize, repeat=False, shuffle=False)
# Trainer の生成
# 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()
> python train_mnist.py -g -1 -e 3
GPU: -1
# unit: 1000
# Minibatch-size: 100
# epoch: 3
C:\ProgramData\Anaconda3\lib\site-packages\chainer\optimizers\adam.py:111: Runti
meWarning: invalid value encountered in sqrt
param.data -= hp.eta * (self.lr * m / (numpy.sqrt(vhat) + hp.eps) +
epoch main/loss validation/main/loss main/accuracy validation/main/acc
uracy elapsed_time
C:\ProgramData\Anaconda3\lib\site-packages\chainer\optimizers\adam.py:111: Runti
meWarning: invalid value encountered in sqrt
param.data -= hp.eta * (self.lr * m / (numpy.sqrt(vhat) + hp.eps) +
1 0.189386 0.088241 0.943733 0.9717 188.366
2 0.0725646 0.0875341 0.977683 0.9714 378.107
3 0.049227 0.0849267 0.984167 0.9735 564.709
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