データの準備
今回はグレー画像をカラーに変換して使用する。
import cv2
import random
import mxnet as mx
from mxnet.gluon import data
train_data = data.vision.datasets.FashionMNIST(train=True)
Sneaker = []
for each_data, label in train_data:
if label==7:
img = cv2.cvtColor(each_data.asnumpy(), cv2.COLOR_GRAY2RGB)
Sneaker.append(mx.nd.array(img))
Sneaker = random.sample(Sneaker, 500)
mx.nd.save('Sneaker', Sneaker)
Boot = []
for each_data, label in train_data:
if label==9:
img = cv2.cvtColor(each_data.asnumpy(), cv2.COLOR_GRAY2RGB)
Boot.append(mx.nd.array(img))
Boot = random.sample(Boot, 10)
mx.nd.save('Boot', Boot)
test_data = data.vision.datasets.FashionMNIST(train=False)
test_Sneaker = []
for each_data, label in test_data:
if label==7:
img = cv2.cvtColor(each_data.asnumpy(), cv2.COLOR_GRAY2RGB)
test_Sneaker.append(mx.nd.array(img))
mx.nd.save('test_Sneaker', test_Sneaker)
test_Boot = []
for each_data, label in test_data:
if label==9:
img = cv2.cvtColor(each_data.asnumpy(), cv2.COLOR_GRAY2RGB)
test_Boot.append(mx.nd.array(img))
mx.nd.save('test_Boot', test_Boot)
単純なクラス分類を試してみる
import numpy as np
import mxnet as mx
from mxnet import autograd, gluon
from mxnet.gluon import nn, data
from mxnet.gluon.data.vision import transforms
from gluoncv.model_zoo import get_model
jitter_param = 0.4
lighting_param = 0.1
transform_train = transforms.Compose([
transforms.Resize(224),
transforms.RandomFlipLeftRight(),
transforms.RandomColorJitter(brightness=jitter_param, contrast=jitter_param,
saturation=jitter_param),
transforms.RandomLighting(lighting_param),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
transform_test = transforms.Compose([
transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
train_data = mx.nd.load('Sneaker') + mx.nd.load('Boot')
train_label = [0]*500 + [1]*10
train_dataset = data.dataset.ArrayDataset(train_data, train_label)
train_data = data.DataLoader(
train_dataset.transform_first(transform_train),
batch_size=32,
shuffle=True)
test_data = mx.nd.load('test_Sneaker') + mx.nd.load('test_Boot')
test_label = [0]*1000 + [1]*1000
test_dataset = data.dataset.ArrayDataset(test_data, test_label)
test_data = data.DataLoader(
test_dataset.transform_first(transform_test),
batch_size=32,
shuffle=False)
finetune_net = get_model('mobilenet1.0', pretrained=True, root='MobileNet')
with finetune_net.name_scope():
finetune_net.output = nn.Dense(2)
finetune_net.output.initialize(mx.init.Xavier())
finetune_net.hybridize()
def evaluate_accuracy(dataloader, net):
sample_n = 0
acc = 0
for batch in dataloader:
data = batch[0]
label = batch[1].asnumpy().astype('int32')
output = net(data)
predictions = np.argmax(output.asnumpy(), axis=1).astype('int32')
sample_n += data.shape[0]
acc += sum(predictions==label)
return acc / sample_n
trainer = gluon.Trainer(finetune_net.collect_params(), 'adam')
Loss = gluon.loss.SoftmaxCrossEntropyLoss()
epoch = 10
print('start training...')
for epoch in range(1, epoch + 1):
for batch in train_data:
data = batch[0]
label = batch[1]
with autograd.record():
output = finetune_net(data)
loss = Loss(output, label)
loss.backward()
trainer.step(data.shape[0])
train_acc = evaluate_accuracy(train_data, finetune_net)
test_acc = evaluate_accuracy(test_data, finetune_net)
print('{:<2} epoch train_acc = {:<10,.5f} test_acc = {:<10,.5f}'.format(epoch, train_acc, test_acc))
finetune_net.save_parameters('finetune_%d.params'%epoch)
1 epoch train_acc = 0.21765 test_acc = 0.56150
2 epoch train_acc = 0.99608 test_acc = 0.79000
3 epoch train_acc = 0.99804 test_acc = 0.88500
4 epoch train_acc = 1.00000 test_acc = 0.81750
5 epoch train_acc = 0.99608 test_acc = 0.89800
6 epoch train_acc = 1.00000 test_acc = 0.87300
7 epoch train_acc = 1.00000 test_acc = 0.87100
8 epoch train_acc = 1.00000 test_acc = 0.87100
9 epoch train_acc = 1.00000 test_acc = 0.76400
10 epoch train_acc = 1.00000 test_acc = 0.78600
import numpy as np
import mxnet as mx
from mxnet.gluon import nn, data
from mxnet.gluon.data.vision import transforms
from gluoncv.model_zoo import get_model
transform_test = transforms.Compose([
transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
train_data = mx.nd.load('Sneaker') + mx.nd.load('Boot')
train_label = [0]*500 + [1]*10
train_dataset = data.dataset.ArrayDataset(train_data, train_label)
train_data = data.DataLoader(
train_dataset.transform_first(transform_test),
batch_size=32,
shuffle=False)
test_data = mx.nd.load('test_Sneaker') + mx.nd.load('test_Boot')
test_label = [0]*1000 + [1]*1000
test_dataset = data.dataset.ArrayDataset(test_data, test_label)
test_data = data.DataLoader(
test_dataset.transform_first(transform_test),
batch_size=32,
shuffle=False)
finetune_net = get_model('mobilenet1.0', pretrained=False, root='MobileNet')
with finetune_net.name_scope():
finetune_net.output = nn.Dense(2)
finetune_net.load_parameters('finetune_5.params')
finetune_net.hybridize()
train_result = []
for batch in train_data:
output = finetune_net.features(batch[0])
train_result.append(output)
train_x = mx.nd.concat(*train_result, dim=0)
test_result = []
for batch in test_data:
output = finetune_net.features(batch[0])
test_result.append(output)
test_x = mx.nd.concat(*test_result, dim=0)
from sklearn.manifold import TSNE
train_result = TSNE(n_components=2).fit_transform(train_x.asnumpy())
test_result = TSNE(n_components=2).fit_transform(test_x.asnumpy())
import matplotlib.pyplot as plt
plt.scatter(train_result[:,0], train_result[:,1], c = train_label, cmap='bwr')
plt.show()
plt.scatter(test_result[:,0], test_result[:,1], c = test_label, cmap='bwr')
plt.show()
上が訓練データ、下がテストデータ
訓練データでは異常画像が十分分類されており、これ以上の改善は望めないことがわかる。
