Datasets:
HenryAI
/
KerasCodeExamples.txt

Dataset card Data Studio Files
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4.99k
16/16 [==============================] - 13s 746ms/step - loss: 0.0607 - psnr: 12.5281 - val_loss: 0.0673 - val_psnr: 12.0121
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Epoch 4/20
16/16 [==============================] - 13s 758ms/step - loss: 0.0595 - psnr: 12.7050 - val_loss: 0.0646 - val_psnr: 12.2768
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Epoch 5/20
16/16 [==============================] - 13s 755ms/step - loss: 0.0583 - psnr: 12.7522 - val_loss: 0.0613 - val_psnr: 12.5351
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Epoch 6/20
16/16 [==============================] - 13s 749ms/step - loss: 0.0545 - psnr: 13.0654 - val_loss: 0.0553 - val_psnr: 12.9512
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Epoch 7/20
16/16 [==============================] - 13s 744ms/step - loss: 0.0480 - psnr: 13.6313 - val_loss: 0.0444 - val_psnr: 13.7838
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Epoch 8/20
16/16 [==============================] - 13s 763ms/step - loss: 0.0359 - psnr: 14.8570 - val_loss: 0.0342 - val_psnr: 14.8823
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Epoch 9/20
16/16 [==============================] - 13s 758ms/step - loss: 0.0299 - psnr: 15.5374 - val_loss: 0.0287 - val_psnr: 15.6171
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Epoch 10/20
16/16 [==============================] - 13s 779ms/step - loss: 0.0273 - psnr: 15.9051 - val_loss: 0.0266 - val_psnr: 15.9319
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Epoch 11/20
16/16 [==============================] - 13s 736ms/step - loss: 0.0255 - psnr: 16.1422 - val_loss: 0.0250 - val_psnr: 16.1568
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Epoch 12/20
16/16 [==============================] - 13s 746ms/step - loss: 0.0236 - psnr: 16.5074 - val_loss: 0.0233 - val_psnr: 16.4793
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Epoch 13/20
16/16 [==============================] - 13s 755ms/step - loss: 0.0217 - psnr: 16.8391 - val_loss: 0.0210 - val_psnr: 16.8950
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Epoch 14/20
16/16 [==============================] - 13s 741ms/step - loss: 0.0197 - psnr: 17.2245 - val_loss: 0.0187 - val_psnr: 17.3766
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Epoch 15/20
16/16 [==============================] - 13s 739ms/step - loss: 0.0179 - psnr: 17.6246 - val_loss: 0.0179 - val_psnr: 17.5445
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Epoch 16/20
16/16 [==============================] - 13s 735ms/step - loss: 0.0175 - psnr: 17.6998 - val_loss: 0.0180 - val_psnr: 17.5154
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Epoch 17/20
16/16 [==============================] - 13s 741ms/step - loss: 0.0167 - psnr: 17.9393 - val_loss: 0.0156 - val_psnr: 18.1784
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Epoch 18/20
16/16 [==============================] - 13s 750ms/step - loss: 0.0150 - psnr: 18.3875 - val_loss: 0.0151 - val_psnr: 18.2811
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Epoch 19/20
16/16 [==============================] - 13s 755ms/step - loss: 0.0141 - psnr: 18.6476 - val_loss: 0.0139 - val_psnr: 18.6216
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Epoch 20/20
16/16 [==============================] - 14s 777ms/step - loss: 0.0139 - psnr: 18.7131 - val_loss: 0.0137 - val_psnr: 18.7259
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100%|██████████| 20/20 [00:00<00:00, 57.59it/s]
Visualize the training step
Here we see the training step. With the decreasing loss, the rendered image and the depth maps are getting better. In your local system, you will see the training.gif file generated.
training-20
Inference
In this section, we ask the model to build novel views of the scene. The model was given 106 views of the scene in the training step. The collections of training images cannot contain each and every angle of the scene. A trained model can represent the entire 3-D scene with a sparse set of training images.
Here we provide different poses to the model and ask for it to give us the 2-D image corresponding to that camera view. If we infer the model for all the 360-degree views, it should provide an overview of the entire scenery from all around.
# Get the trained NeRF model and infer.
nerf_model = model.nerf_model
test_recons_images, depth_maps = render_rgb_depth(
model=nerf_model,
rays_flat=test_rays_flat,
t_vals=test_t_vals,
rand=True,
train=False,
)
# Create subplots.
fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(10, 20))
for ax, ori_img, recons_img, depth_map in zip(
axes, test_imgs, test_recons_images, depth_maps
):
ax[0].imshow(keras.preprocessing.image.array_to_img(ori_img))
ax[0].set_title(\"Original\")