cs501r_f2016:lab7
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| - ''H0'': A 2d convolution on ''imgs'' with 32 filters, followed by a leaky relu | - ''H0'': A 2d convolution on ''imgs'' with 32 filters, followed by a leaky relu | ||
| - ''H1'': A 2d convolution on ''H0'' with 64 filters, followed by a leaky relu | - ''H1'': A 2d convolution on ''H0'' with 64 filters, followed by a leaky relu | ||
| - | - ''H2'': A linear layer from ''H1'' to a 1024 dimensional vector | + | - ''H2'': A linear layer from ''H1'' to a 1024 dimensional vector, followed by a leaky relu |
| - ''H3'': A linear layer mapping ''H2'' to a single scalar (per image) | - ''H3'': A linear layer mapping ''H2'' to a single scalar (per image) | ||
| - The final output should be a sigmoid of ''H3''. | - The final output should be a sigmoid of ''H3''. | ||
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| **Part 4: create your loss functions and training ops** | **Part 4: create your loss functions and training ops** | ||
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| + | {{ :cs501r_f2016:lab7_graph.png?200|}} | ||
| You should create two loss functions, one for the discriminator, and | You should create two loss functions, one for the discriminator, and | ||
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| I highly recommend using Tensorboard to visualize your final | I highly recommend using Tensorboard to visualize your final | ||
| - | computation graph to make sure you got this right. | + | computation graph to make sure you got this right. Check out my computation graph image on the right - you can see the two discriminator blocks, and you can see that the same variables are feeding into both of them. |
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cs501r_f2016/lab7.txt ยท Last modified: 2021/06/30 23:42 (external edit)
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