Datasets:
HenryAI
/
KerasCodeExamples.txt

Dataset card Data Studio Files
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Epoch 77/100
Mean PSNR for epoch: 26.50
50/50 - 13s - loss: 0.0025 - val_loss: 0.0022
Epoch 78/100
Mean PSNR for epoch: 26.90
50/50 - 13s - loss: 0.0025 - val_loss: 0.0022
Epoch 79/100
Mean PSNR for epoch: 26.92
50/50 - 15s - loss: 0.0025 - val_loss: 0.0022
Epoch 80/100
Mean PSNR for epoch: 27.00
50/50 - 14s - loss: 0.0025 - val_loss: 0.0022
Epoch 81/100
Mean PSNR for epoch: 26.89
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50/50 - 14s - loss: 0.0025 - val_loss: 0.0022
Epoch 82/100
Mean PSNR for epoch: 26.62
50/50 - 14s - loss: 0.0025 - val_loss: 0.0022
Epoch 83/100
Mean PSNR for epoch: 26.85
50/50 - 14s - loss: 0.0025 - val_loss: 0.0022
Epoch 84/100
Mean PSNR for epoch: 26.69
50/50 - 14s - loss: 0.0025 - val_loss: 0.0022
Epoch 85/100
Mean PSNR for epoch: 26.81
50/50 - 14s - loss: 0.0025 - val_loss: 0.0022
Epoch 86/100
Mean PSNR for epoch: 26.16
50/50 - 14s - loss: 0.0025 - val_loss: 0.0022
Epoch 87/100
Mean PSNR for epoch: 26.48
50/50 - 14s - loss: 0.0025 - val_loss: 0.0022
Epoch 88/100
Mean PSNR for epoch: 25.62
50/50 - 14s - loss: 0.0026 - val_loss: 0.0027
Epoch 89/100
Mean PSNR for epoch: 26.55
50/50 - 14s - loss: 0.0025 - val_loss: 0.0022
Epoch 90/100
Mean PSNR for epoch: 26.20
50/50 - 14s - loss: 0.0025 - val_loss: 0.0023
Epoch 91/100
Mean PSNR for epoch: 26.35
50/50 - 14s - loss: 0.0025 - val_loss: 0.0022
Epoch 92/100
Mean PSNR for epoch: 26.85
50/50 - 13s - loss: 0.0025 - val_loss: 0.0022
Epoch 93/100
Mean PSNR for epoch: 26.83
50/50 - 13s - loss: 0.0025 - val_loss: 0.0022
Epoch 94/100
Mean PSNR for epoch: 26.63
50/50 - 14s - loss: 0.0025 - val_loss: 0.0022
Epoch 95/100
Mean PSNR for epoch: 25.94
50/50 - 13s - loss: 0.0025 - val_loss: 0.0024
Epoch 96/100
Mean PSNR for epoch: 26.47
50/50 - 14s - loss: 0.0025 - val_loss: 0.0022
Epoch 97/100
Mean PSNR for epoch: 26.42
50/50 - 14s - loss: 0.0025 - val_loss: 0.0022
Epoch 98/100
Mean PSNR for epoch: 26.33
50/50 - 13s - loss: 0.0025 - val_loss: 0.0022
Epoch 99/100
Mean PSNR for epoch: 26.55
50/50 - 13s - loss: 0.0025 - val_loss: 0.0022
Epoch 100/100
Mean PSNR for epoch: 27.08
50/50 - 13s - loss: 0.0025 - val_loss: 0.0022
<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x14b62cc50>
Run model prediction and plot the results
Let's compute the reconstructed version of a few images and save the results.
total_bicubic_psnr = 0.0
total_test_psnr = 0.0
for index, test_img_path in enumerate(test_img_paths[50:60]):
img = load_img(test_img_path)
lowres_input = get_lowres_image(img, upscale_factor)
w = lowres_input.size[0] * upscale_factor
h = lowres_input.size[1] * upscale_factor
highres_img = img.resize((w, h))
prediction = upscale_image(model, lowres_input)
lowres_img = lowres_input.resize((w, h))
lowres_img_arr = img_to_array(lowres_img)
highres_img_arr = img_to_array(highres_img)
predict_img_arr = img_to_array(prediction)
bicubic_psnr = tf.image.psnr(lowres_img_arr, highres_img_arr, max_val=255)
test_psnr = tf.image.psnr(predict_img_arr, highres_img_arr, max_val=255)
total_bicubic_psnr += bicubic_psnr
total_test_psnr += test_psnr
print(