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| keep_p = 1- dropout_p | |
| a = np.sqrt(target_var / (keep_p *((1-keep_p) * np.power(alpha-target_mean,2) + target_var))) | |
| b = target_mear - a * (keep_p * target_mean + (1 - keep_p) * alpha) | |
| def alpha_dropout(x, alpha_p=-1.758, dropout_p=0.05): | |
| mask = np.random.rand(*x.shape) > dropout_p | |
| x[mask] = alpha_p | |
| output = a*x + b | |
| return output |
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