Month: December 2021

I’m wondering how the pre-trained models actually handle the variety of inputs? Do the original layer weights stay exactly the same?

Many thanks in advance for your care & time.

I have been looking around and haven’t been able to find the answer to this one. I am having trouble trying to resume training on a CGAN that I am working with after loading the h5 file. When I try to start training the model again after loading the files, the generator loss will begin to move towards zero very quickly, within 3-4 epochs.

Below is some of the code for loading the models and resuming training. Any help or suggestions would be greatly appreciated!

Loading Models:

d_model = load_model('Aeon5/cgan_model/discriminator_0_to_83.h5') g_model = load_model('Aeon5/cgan_model/generator_0_to_83.h5') gen_noise, gen_label = g_model.input gen_output = g_model.output gan_output = d_model([gen_output, gen_label]) combined = Model([gen_noise, gen_label], gan_output) opt = Adam(lr=0.0002, beta_1=0.5) combined.compile(loss=['binary_crossentropy'], optimizer=opt) 

Resuming Training:

def resume_train(epochs, start, generator, discriminator, combined_model, latent_dim, data_loader, name_append, batch_size=50): for epoch in range(start, epochs): random = np.random.randint(0, 11) for index in range(int(50000/batch_size)): valid = np.ones((batch_size, 1)) fake = np.zeros((batch_size, 1)) idx = np.random.randint(0, 50000, batch_size) x_train = data_loader.get_img_batch(idx) y_train = data_loader.get_label_batch(idx) x_train = (x_train.astype(np.float32) - 127.5)/127.5 if index % 100 == random: valid = np.zeros((batch_size, 1)) + (np.random.random()*0.1) fake = np.ones((batch_size, 1)) - (np.random.random()*0.1) noise = np.random.randn(batch_size, latent_dim) gen_img = generator.predict([noise, y_train]) d_loss_real, _ = discriminator.train_on_batch([x_train, y_train], valid) d_loss_fake, _ = discriminator.train_on_batch([gen_img, y_train], fake) d_loss = 0.5*(np.add(d_loss_real, d_loss_fake)) sample_label = np.random.randint(0, 10, batch_size).reshape(-1, 1) valid = np.ones((batch_size, 1)) g_loss = combined_model.train_on_batch([noise, sample_label], valid) if index % (batch_size) == 0: sample_images(epoch, latent_dim, generator, data_loader) print("%d [D loss: %f] [G loss: %f]" % (epoch, d_loss, g_loss)) #Save the combined model and the generator name = './cgan_model/combined_' + name_append + '.h5' combined_model.save(name) name = './cgan_model/generator_' + name_append + '.h5' generator.save(name) name = './cgan_model/discriminator_' + name_append + '.h5' discriminator.save(name) 

Where I work, we need to quantize our models to run them quick enough, and we found that Quantization Aware Training is the only one that has a chance of retaining the desired accuracy. Using Post-training Quantization incurs too many losses.

However, QAT is incredibly difficult and cumbersome in TF 2 because it only applies to models defined through the functional API, whereas many interesting models use for example the object-oriented approach of defining a model.

Does anyone know if there are plans to make QAT easier to use in the future?

I have been searching for some quality tutorials on tensorflow for quite a while and I can’t find some good ones.

Can anyone please suggest me any tutorial (I would prefer video) to learn tensorflow with hands on (I mean using it practically too not just go through docs only)?

First of all, nice to meet you, I’m new. Well, I already read him a lot about the theory and he had even bought courses, but I feel that the examples were already old or not useful. I have also been reading some posts in this sub and I have noticed that tensorflow and keras have their inefficiencies, so I don’t know what tools and resources to use to start with.

Beforehand thank you very much.