Training and test data, plus saved models for the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022

Each file is in the pickle format and was generated with Python 3.8.5. Each file contains a Python dictionary with the following fields: 'train_images': 640,000 float32 images used for model training. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. 'test_images': 64,000 float32 images used for model testing. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. For more details, see the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022 (to be published soon on arXiv.org). Details on saved models coming soon.所有文件均采用pickle格式（pickle）存储，且由Python 3.8.5版本生成。每个文件均包含一个Python字典，包含以下字段： `train_images`：用于模型训练的640,000张32位浮点数（float32）格式图像。其中20像素（20px）的图像包含400个像素强度值，40像素（40px）的图像每张包含1600个像素强度值。 `train_labels`：对应`train_images`中每张图像的32位浮点数（float32）格式标签。所有自然图像的标签均为0.0；纹理图像则根据其纹理类别，分别标记为0.0、1.0、2.0、3.0或4.0。 `test_images`：用于模型测试的64,000张32位浮点数（float32）格式图像。其中20像素（20px）的图像包含400个像素强度值，40像素（40px）的图像每张包含1600个像素强度值。 `test_labels`：对应`test_images`中每张图像的32位浮点数（float32）格式标签。所有自然图像的标签均为0.0；纹理图像则根据其纹理类别，分别标记为0.0、1.0、2.0、3.0或4.0。 如需了解更多细节，请参阅提交至NeurIPS 2022的论文"Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder"，该论文即将在arXiv.org发布。有关已保存模型的详细信息即将公开。