moved images to github
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@ -393,7 +393,7 @@
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"\n",
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"\n",
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"Don't worry if this is confusing! The algorithm is conveniently implemented as `torch.nn.PixelShuffle` in PyTorch, so as long as you have a general idea of how this works, you're set.\n",
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"Don't worry if this is confusing! The algorithm is conveniently implemented as `torch.nn.PixelShuffle` in PyTorch, so as long as you have a general idea of how this works, you're set.\n",
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"\n",
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"\n",
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"> ![Efficient Sub-pixel CNN](https://drive.google.com/uc?export=view&id=136LMcywv1r5W1f9-L-55bJ4AKAHlH8Zr)\n",
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"> ![Efficient Sub-pixel CNN](https://github.com/https-deeplearning-ai/GANs-Public/blob/master/SRGAN-PixelShuffle.png?raw=true)\n",
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"*Efficient sub-pixel CNN, taken from Figure 1 of [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://arxiv.org/abs/1609.05158) (Shi et al. 2016). The PixelShuffle operation (also known as sub-pixel convolution) is shown as the last step on the right.*"
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"*Efficient sub-pixel CNN, taken from Figure 1 of [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://arxiv.org/abs/1609.05158) (Shi et al. 2016). The PixelShuffle operation (also known as sub-pixel convolution) is shown as the last step on the right.*"
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]
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]
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},
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},
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@ -418,7 +418,7 @@
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"\n",
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"\n",
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"The super-resolution residual network (SRResNet) and the generator are the same thing. The generator network architecture is actually quite simple - just a bunch of convolutional layers, residual blocks, and pixel shuffling layers!\n",
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"The super-resolution residual network (SRResNet) and the generator are the same thing. The generator network architecture is actually quite simple - just a bunch of convolutional layers, residual blocks, and pixel shuffling layers!\n",
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"\n",
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"\n",
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"> ![SRGAN Generator](https://drive.google.com/uc?export=view&id=1wY5YmoYTBzuhWLlTAYCI92xWgkf_uINE)\n",
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"> ![SRGAN Generator](https://github.com/https-deeplearning-ai/GANs-Public/blob/master/SRGAN-Generator.png?raw=true)\n",
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"*SRGAN Generator, taken from Figure 4 of [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802) (Ledig et al. 2017).*"
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"*SRGAN Generator, taken from Figure 4 of [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802) (Ledig et al. 2017).*"
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]
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]
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},
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},
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@ -492,7 +492,7 @@
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"\n",
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"\n",
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"The discriminator architecture is also relatively straightforward, just one big sequential model - see the diagram below for reference!\n",
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"The discriminator architecture is also relatively straightforward, just one big sequential model - see the diagram below for reference!\n",
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"\n",
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"\n",
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"![SRGAN Generator](https://drive.google.com/uc?export=view&id=1fcfTrXBcODoZa2JSEO8OiMUEo5WHdkef)\n",
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"![SRGAN Generator](https://github.com/https-deeplearning-ai/GANs-Public/blob/master/SRGAN-Discriminator.png?raw=true)\n",
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"*SRGAN Discriminator, taken from Figure 4 of [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802) (Ledig et al. 2017).*"
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"*SRGAN Discriminator, taken from Figure 4 of [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802) (Ledig et al. 2017).*"
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]
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]
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},
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},
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@ -894,7 +894,6 @@
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"cell_type": "code",
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"cell_type": "code",
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"metadata": {
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"metadata": {
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"id": "ScH0Iok8fAMS",
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"id": "ScH0Iok8fAMS",
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"outputId": "388ba277-76ce-427b-b7b1-57ab74e9b1dd",
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"colab": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"base_uri": "https://localhost:8080/",
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"height": 84,
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"height": 84,
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"0c356917abbf4afb8ef89e5e48930635",
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"0c356917abbf4afb8ef89e5e48930635",
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"c0209daeab5f4a09b01a23370e09d352"
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"c0209daeab5f4a09b01a23370e09d352"
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]
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]
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}
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},
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"outputId": "388ba277-76ce-427b-b7b1-57ab74e9b1dd"
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},
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},
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"source": [
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"source": [
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"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
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"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
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"cell_type": "code",
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"cell_type": "code",
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"metadata": {
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"metadata": {
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"id": "AvqBuRwjZrBq",
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"id": "AvqBuRwjZrBq",
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"outputId": "96ff8cef-0371-4bb3-d4ff-b1c1aee180b6",
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"colab": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"base_uri": "https://localhost:8080/",
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"height": 1000
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"height": 1000
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}
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},
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"outputId": "96ff8cef-0371-4bb3-d4ff-b1c1aee180b6"
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},
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},
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"source": [
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"source": [
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"generator = torch.load('srresnet.pt')\n",
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"generator = torch.load('srresnet.pt')\n",
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