How to use ready network and generate data?

t0mo: <p>Hi, I’m new in pytorch as well as in deep learning.<br>
I have used this repository: <a href="https://github.com/LixiangHan/GANs-for-1D-Signal" class="inline-onebox" rel="noopener nofollow ugc">GitHub - LixiangHan/GANs-for-1D-Signal: implementation of several GANs with pytorch</a> (dcgan is part im interested in) I changed training set for my needs and it performed pretty well. Now I would like to use trained generator to generate data and test it. Some research I have performed didn’t help me to find out how to do that…</p>
<p>Could anyone look at this code and give me some tips? Maybe answer is simple and obvious or<br>
maybe there are some good articles/resources that could help me make progress.<br>
After training a .pkl file is created. I was wondering If this might help?</p>

ptrblck: <p>After training the models, you could create a new <code>Generator</code> and load the trained <code>state_dict</code> via:</p>
<pre><code class="lang-python">netG = Generator()
netG.load_state_dict(torch.load(path_to_generator_state_dict))
</code></pre>
<p>Afterwards, you could execute the forward pass using the same inputs as were used to train this model:</p>
<pre><code class="lang-python">with torch.no_grad():
    noise = torch.randn(b_size, nz, 1, device=device)
    fake = netG(noise)
</code></pre>

Joe_Suarez: <aside class="quote no-group quote-modified" data-username="t0mo" data-post="1" data-topic="127692">
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<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/t0mo/48/32462_2.png" class="avatar"> t0mo:</div>
<blockquote>
<p>Hi, I’m new in pytorch as well as in deep learning.<br>
I have used this repository: <a href="https://github.com/LixiangHan/GANs-for-1D-Signal" rel="noopener nofollow ugc">GitHub - LixiangHan/GANs-for-1D-Signal: implementation of several GANs with pytorch </a> (dcgan is part im interested in) I changed training set for my needs and it performed pretty well. Now I would like to use trained generator to generate data and test it. Some research I have performed didn’t help me to find out how to do that…</p>
<p>Could anyone look at this code and give me some tips? Maybe answer is simple and obvious or<br>
maybe there are some good articles/resources that could help me make progress.<br>
After training a .pkl file is created. I was wondering If this might help?</p>
<aside class="quote no-group">
<blockquote>
<p>After training the models, you could create a new Generator and load the trained state_dict via: netG = Generator() netG.load_state_dict(torch.load(path_to_generator_state_dict)) Afterwards, you could execute the forward pass using the same inputs as were used to train this model: with torch.no_g…</p>
</blockquote>
</aside>
<p>After training the models, you could create a new Generator and load the trained state_dict via: netG = Generator() netG.load_state_dict(torch.load(path_to_generator_state_dict)) Afterwards, you could execute the forward pass using the same inputs as were used to train this model: with torch.no_g…</p>
</blockquote>
</aside>
<p>Yes, you’re on the right track! After training the models, you can save the state_dict of the trained generator using <code>torch.save()</code>. The code snippet you provided demonstrates how to load the saved state_dict and create a new instance of the generator.</p>
<p>Here’s an example of how you can generate <a href="https://www.globaldatabase.com/what-is-lead-enrichment" rel="noopener nofollow ugc">lead data enrichment</a> using the trained generator:<br>
import torch<br>
from generator import Generator  # Assuming the generator code is in ‘generator.py’</p>
<h1><a name="create-an-instance-of-the-generator-1" class="anchor" href="#create-an-instance-of-the-generator-1"></a>Create an instance of the generator</h1>
<p>netG = Generator()</p>
<h1><a name="load-the-state_dict-of-the-trained-generator-2" class="anchor" href="#load-the-state_dict-of-the-trained-generator-2"></a>Load the state_dict of the trained generator</h1>
<p>path_to_generator_state_dict = ‘path/to/your/generator_state_dict.pkl’<br>
netG.load_state_dict(torch.load(path_to_generator_state_dict))</p>
<h1><a name="set-the-generator-in-evaluation-mode-3" class="anchor" href="#set-the-generator-in-evaluation-mode-3"></a>Set the generator in evaluation mode</h1>
<p>netG.eval()</p>
<h1><a name="generate-data-using-the-generator-4" class="anchor" href="#generate-data-using-the-generator-4"></a>Generate data using the generator</h1>
<p>num_samples = 10  # Specify the number of samples you want to generate<br>
latent_dim = 100  # Specify the size of the generator’s input latent vector</p>
<h1><a name="generate-random-noise-vectors-as-input-to-the-generator-5" class="anchor" href="#generate-random-noise-vectors-as-input-to-the-generator-5"></a>Generate random noise vectors as input to the generator</h1>
<p>noise = torch.randn(num_samples, latent_dim)</p>
<h1><a name="pass-the-noise-through-the-generator-to-generate-samples-6" class="anchor" href="#pass-the-noise-through-the-generator-to-generate-samples-6"></a>Pass the noise through the generator to generate samples</h1>
<p>generated_samples = netG(noise)</p>
<h1><a name="the-generated_samples-tensor-now-contains-the-generated-data-7" class="anchor" href="#the-generated_samples-tensor-now-contains-the-generated-data-7"></a>The generated_samples tensor now contains the generated data</h1>
<p>print(generated_samples)</p>
<p>Make sure you have the <code>generator.py</code> file available, or modify the import statement accordingly to match the actual file name and location.</p>
<p>The <code>generated_samples</code> tensor will contain the generated data. You can manipulate or analyze this data as per your requirements.</p>
<p>Remember to adjust the <code>num_samples</code> and <code>latent_dim</code> variables to match your specific needs. <code>num_samples</code> determines how many samples you want to generate, and <code>latent_dim</code> specifies the size of the input noise vector for the generator.</p>