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feat: use one-sided smoothing (eg: positive/real labels 1.0 -> 0.9), and only apply to discriminator

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jh 2021-03-22 15:22:28 -07:00
commit c36eca3610
1 changed files with 13 additions and 14 deletions
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25
models/Model_SAEHD/Model.py
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@ -551,29 +551,28 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
x = tf.random.categorical(probs, num_labels) x = tf.random.categorical(probs, num_labels)
x = tf.cast(x, tf.float32) x = tf.cast(x, tf.float32)
x = tf.math.scalar_mul(1-smoothing, x) x = tf.math.scalar_mul(1-smoothing, x)
x = x + (smoothing/num_labels) # x = x + (smoothing/num_labels)
x = tf.reshape(x, (self.batch_size,) + tensor.shape[1:]) x = tf.reshape(x, (self.batch_size,) + tensor.shape[1:])
return x return x
smoothing = self.options['gan_smoothing'] smoothing = self.options['gan_smoothing']
noise = self.options['gan_noise'] noise = self.options['gan_noise']
gpu_pred_src_src_d_ones = get_smooth_noisy_labels(1, gpu_pred_src_src_d, smoothing=smoothing, noise=noise) gpu_pred_src_src_d_ones = tf.ones_like(gpu_pred_src_src_d)
gpu_pred_src_src_d_zeros = get_smooth_noisy_labels(0, gpu_pred_src_src_d, smoothing=smoothing, noise=noise) gpu_pred_src_src_d2_ones = tf.ones_like(gpu_pred_src_src_d2)
gpu_pred_src_src_d2_ones = get_smooth_noisy_labels(1, gpu_pred_src_src_d2, smoothing=smoothing, noise=noise) gpu_pred_src_src_d_smooth_zeros = get_smooth_noisy_labels(0, gpu_pred_src_src_d, smoothing=smoothing, noise=noise)
gpu_pred_src_src_d2_zeros = get_smooth_noisy_labels(0, gpu_pred_src_src_d2, smoothing=smoothing, noise=noise) gpu_pred_src_src_d2_smooth_zeros = get_smooth_noisy_labels(0, gpu_pred_src_src_d2, smoothing=smoothing, noise=noise)
gpu_target_src_d, \ gpu_target_src_d, gpu_target_src_d2 = self.D_src(gpu_target_src_masked_opt)
gpu_target_src_d2 = self.D_src(gpu_target_src_masked_opt)
gpu_target_src_d_ones = get_smooth_noisy_labels(1, gpu_target_src_d, smoothing=smoothing, noise=noise) gpu_target_src_d_smooth_ones = get_smooth_noisy_labels(1, gpu_target_src_d, smoothing=smoothing, noise=noise)
gpu_target_src_d2_ones = get_smooth_noisy_labels(1, gpu_target_src_d2, smoothing=smoothing, noise=noise) gpu_target_src_d2_smooth_ones = get_smooth_noisy_labels(1, gpu_target_src_d2, smoothing=smoothing, noise=noise)
gpu_D_src_dst_loss = (DLoss(gpu_target_src_d_ones , gpu_target_src_d) + \ gpu_D_src_dst_loss = DLoss(gpu_target_src_d_smooth_ones, gpu_target_src_d) \
DLoss(gpu_pred_src_src_d_zeros , gpu_pred_src_src_d) ) * 0.5 + \ + DLoss(gpu_pred_src_src_d_smooth_zeros, gpu_pred_src_src_d) \
(DLoss(gpu_target_src_d2_ones , gpu_target_src_d2) + \ + DLoss(gpu_target_src_d2_smooth_ones, gpu_target_src_d2) \
DLoss(gpu_pred_src_src_d2_zeros , gpu_pred_src_src_d2) ) * 0.5 + DLoss(gpu_pred_src_src_d2_smooth_zeros, gpu_pred_src_src_d2)
gpu_D_src_dst_loss_gvs += [ nn.gradients (gpu_D_src_dst_loss, self.D_src.get_weights() ) ]#+self.D_src_x2.get_weights() gpu_D_src_dst_loss_gvs += [ nn.gradients (gpu_D_src_dst_loss, self.D_src.get_weights() ) ]#+self.D_src_x2.get_weights()