Kerasを用いてbert as serviceから得られたベクトル表現に対して分類をします。
以下でネットワークを構築します。
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.utils import np_utils
from sklearn import preprocessing
# BERTから得るベクトル表現をこのモデルに流し込むdefbuild_multilayer_perceptron():
model = Sequential()
# 隠れ層512は好きに変えて良い
model.add(Dense(512, input_shape=(768,)))
model.add(Activation('relu'))
# liveコーパスは9クラス分類なので9
model.add(Dense(9))
model.add(Activation('softmax'))
return model
x = train_df["Text"].values.tolist()
x = list(map(parse,x))
X = bc.encode(x,is_tokenized=True)
Y = train_df["Label"].values
train_X, test_X, train_Y, test_Y = train_test_split(X, Y, train_size=0.8)
print(train_X.shape, test_X.shape, train_Y.shape, test_Y.shape)
model = build_multilayer_perceptron()
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
# Each architecture is provided with several class for fine-tuning on down-stream tasks, e.g.
BERT_MODEL_CLASSES = [BertModel, BertForPreTraining, BertForMaskedLM, BertForNextSentencePrediction,
BertForSequenceClassification, BertForTokenClassification, BertForQuestionAnswering]
bert as serviceは一応CPUでも動くようなのでGPUないけど回したいみたいな人にはいいのかなと思いました。 NLP界隈の人間ではないので詳しいことまでは分かりませんが。
Figure 1: Image generation learned from a single training image. We propose SinGAN–a new unconditional generative model trained on a single natural image. Our model learns the image’s patch statistics across multiple scales, using a dedicated multi-scale adversarial training scheme; it can then be used to generate new realistic image samples that preserve the original patch distribution while creating new object configurations and structures.
先行研究と比べてどこがすごい？
Figure 2: Image manipulation. SinGAN can be used in various image manipulation tasks, including: transforming a paint (clipart) into a realistic photo, rearranging and editing objects in the image, harmonizing a new object into an image, image super-resolution and creating an animation from a single input. In all these cases, our model observes only the training image (first row) and is trained in the same manner for all applications, with no architectural changes or further tuning (see Sec. 4).
Figure 3: SinGAN vs. Single Image Texture Generation. Single image models for texture generation [3, 16] are not designed to deal with natural images. Our model can produce realistic image samples that consist of complex textures and non-reptititve global structures.
Figure 4: SinGAN’s multi-scale pipeline. Our model consists of a pyramid of GANs, where both training and inference are done in a coarse-to-fine fashion. At each scale, Gn learns to generate image samples in which all the overlapping patches cannot be distinguished from the patches in the down-sampled training image, xn, by the discriminator Dn; the effective patch size decreases as we go up the pyramid (marked in yellow on the original image for illustration). The input to Gn is a random noise image zn, and the generated image from the previous scale x˜n, upsampled to the current resolution (except for the coarsest level which is purely generative). The generation process at level n involves all generators {GN . . . Gn} and all noise maps {zN , . . . , zn} up to this level. See more details at Sec. 2.
Figure 6: Random image samples. After training SinGAN on a single image, our model can generate realistic random image samples that depict new structures and object configurations, yet preserve the patch distribution of the training image. Because our model is fully convolutional, the generated images may have arbitrary sizes and aspect ratios. Note that our goal is not image retargeting – our image samples are random and optimized to maintain the patch statistics, rather than preserving salient objects. See SM for more results and qualitative comparison to image retargeting methods.
以下は高解像度の生成結果。
Figure 7: High resolution image generation. A random sample produced by our model, trained on the 243 × 1024 image (upper right corner); new global structures as well as fine details are realistically generated. See 4Mpix examples in SM.
Figure 8: Generation from different scales (at inference). We show the effect of starting our hierarchical generation from a given level n. For our full generation scheme (n = N), the input at the coarsest level is random noise. For generation from a finer scale n, we plug in the downsampled original image, xn, as input to that scale. This allows us to control the scale of the generated structures, e.g., we can preserve the shape and pose of the Zebra and only change its stripe texture by starting the generation from n = N −1.
Figure 9: The effect of training with a different number of scales. The number of scales in SinGAN’s architecture strongly influences the results. A model with a small number of scales only captures textures. As the number of scales increases, SinGAN manages to capture larger structures as well as the global arrangement of objects in the scene.
Figure 10: Super-Resolution. When SinGAN is trained on a low resolution image, we are able to super resolve. This is done by iteratively upsampling the image and feeding it to SinGAN’s finest scale generator. As can be seen, SinGAN’s visual quality is better than the SOTA internal methods ZSSR [46] and DIP [51]. It is also better than EDSR [32] and comparable to SRGAN [30], external methods trained on large collections. Corresponding PSNR and NIQE [40] are shown in parentheses.
以下はペイント→写真変換の結果。
Figure 11: Paint-to-Image. We train SinGAN on a target image and inject a downsampled version of the paint into one of the coarse levels at test time. Our generated images preserve the layout and general structure of the clipart while generating realistic texture and fine details that match the training image. Well-known style transfer methods [17, 38] fail in this task.
以下は画像編集の結果。
Figure 12: Editing. We copy and paste a few patches from the original image (a), and input a downsampled version of the edited image (b) to an intermediate level of our model (pretrained on (a)). In the generated image (d), these local edits are translated into coherent and photo-realistic structures. (c) comparison to Photoshop content aware move.
以下は画像調和の結果。
Figure 13: Harmonization. Our model is able to preserve the structure of the pasted object, while adjusting its appearance and texture. The dedicated harmonization
https://www.slideshare.net/tdualdir/devsumi-107931922 より
https://hotcocoastudy.hatenablog.jp/entry/2019/02/20/013653
https://hotcocoastudy.hatenablog.jp/entry/2019/03/23/115503
（https://twitter.com/balajiln/status/1202764043733229568 より）
でもこの違いは、ステップ数を調整することでバランスを取れる。
公式実装だと以下のようにしてる。
（https://twitter.com/balajiln/status/1202765799636627457 より）
凄い、分かりやすいですね^^
