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Vectorize

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jh 2021-03-17 08:26:00 -07:00
commit 3e7fb39cad
1 changed files with 6 additions and 10 deletions
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16
models/Model_SAEHD/Model.py
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@ -502,19 +502,15 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
def get_smooth_noisy_labels(label, tensor, smoothing=0.2, noise=0.05): def get_smooth_noisy_labels(label, tensor, smoothing=0.2, noise=0.05):
labels = []
num_labels = self.batch_size num_labels = self.batch_size
for d in tensor.shape[1:]: for d in tensor.shape[1:]:
num_labels *= d num_labels *= d
for _ in range(num_labels): probs = tf.math.log([[noise, 1-noise]]) if label == 1 else tf.math.log([[1-noise, noise]])
if np.random.random() < noise: x = tf.random.categorical(probs, num_labels)
label = 0 if label == 1 else 1 x = tf.cast(x, tf.float32)
if label == 0: x = x * (1-smoothing) + (smoothing/x.shape[1])
label = np.random.uniform(0, 0+smoothing/2) x = tf.reshape(x, (self.batch_size,) + tensor.shape[1:])
else: return x
label = np.random.uniform(1-smoothing/2, 1.0)
labels.append(label)
return tf.reshape(labels, (self.batch_size,) + tensor.shape[1:])
gpu_pred_src_src_d_ones = get_smooth_noisy_labels(1, gpu_pred_src_src_d, smoothing=0.2, noise=0.05) gpu_pred_src_src_d_ones = get_smooth_noisy_labels(1, gpu_pred_src_src_d, smoothing=0.2, noise=0.05)
gpu_pred_src_src_d_zeros = get_smooth_noisy_labels(0, gpu_pred_src_src_d, smoothing=0.2, noise=0.05) gpu_pred_src_src_d_zeros = get_smooth_noisy_labels(0, gpu_pred_src_src_d, smoothing=0.2, noise=0.05)