zomux /
deepy
| Conditions | 25 |
| Total Lines | 92 |
| Lines | 0 |
| Ratio | 0 % |
| Changes | 1 | ||
| Bugs | 0 | Features | 0 |
Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.
For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.
Commonly applied refactorings include:
If many parameters/temporary variables are present:
Complex classes like AttentionTrainer.train_func() often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
| 1 | #!/usr/bin/env python |
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| 63 | def train_func(self, train_set): |
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| 64 | cost_sum = 0.0 |
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| 65 | batch_cost = 0.0 |
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| 66 | counter = 0 |
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| 67 | total = 0 |
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| 68 | total_reward = 0 |
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| 69 | batch_reward = 0 |
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| 70 | total_position_value = 0 |
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| 71 | pena_count = 0 |
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| 72 | for d in train_set: |
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| 73 | pairs = self.grad_func(*d) |
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| 74 | cost = pairs[0] |
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| 75 | if cost > 10 or np.isnan(cost): |
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| 76 | sys.stdout.write("X") |
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| 77 | sys.stdout.flush() |
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| 78 | continue |
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| 79 | batch_cost += cost |
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| 80 | |||
| 81 | wl_grad = pairs[1] |
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| 82 | max_position_value = np.max(np.absolute(pairs[2])) |
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| 83 | total_position_value += max_position_value |
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| 84 | last_decision = pairs[3] |
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| 85 | target_decision = d[1][0] |
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| 86 | reward = 0.005 if last_decision == target_decision else 0 |
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| 87 | if max_position_value > 0.8: |
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| 88 | reward = 0 |
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| 89 | total_reward += reward |
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| 90 | batch_reward += reward |
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| 91 | if self.last_average_reward == 999 and total > 2000: |
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| 92 | self.last_average_reward = total_reward / total |
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| 93 | if not self.disable_reinforce: |
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| 94 | self.batch_wl_grad += wl_grad * - (reward - self.last_average_reward) |
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| 95 | if not self.disable_backprop: |
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| 96 | for grad_cache, grad in zip(self.batch_grad, pairs[4:]): |
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| 97 | grad_cache += grad |
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| 98 | counter += 1 |
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| 99 | total += 1 |
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| 100 | if counter >= self.batch_size: |
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| 101 | if total == counter: counter -= 1 |
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| 102 | self.update_parameters(self.last_average_reward < 999) |
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| 103 | |||
| 104 | # Clean batch gradients |
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| 105 | if not self.disable_reinforce: |
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| 106 | self.batch_wl_grad *= 0 |
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| 107 | if not self.disable_backprop: |
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| 108 | for grad_cache in self.batch_grad: |
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| 109 | grad_cache *= 0 |
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| 110 | |||
| 111 | if total % 1000 == 0: |
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| 112 | sys.stdout.write(".") |
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| 113 | sys.stdout.flush() |
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| 114 | |||
| 115 | # Cov |
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| 116 | if not self.disable_reinforce: |
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| 117 | cov_changed = False |
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| 118 | if batch_reward / self.batch_size < 0.001: |
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| 119 | if not self.large_cov_mode: |
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| 120 | if pena_count > 20: |
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| 121 | self.layer.cov.set_value(self.layer.large_cov) |
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| 122 | print "[LCOV]", |
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| 123 | cov_changed = True |
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| 124 | else: |
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| 125 | pena_count += 1 |
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| 126 | else: |
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| 127 | pena_count = 0 |
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| 128 | else: |
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| 129 | if self.large_cov_mode: |
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| 130 | if pena_count > 20: |
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| 131 | self.layer.cov.set_value(self.layer.small_cov) |
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| 132 | print "[SCOV]", |
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| 133 | cov_changed = True |
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| 134 | else: |
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| 135 | pena_count += 1 |
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| 136 | else: |
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| 137 | pena_count = 0 |
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| 138 | if cov_changed: |
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| 139 | self.large_cov_mode = not self.large_cov_mode |
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| 140 | self.layer.cov_inv_var.set_value(np.array(LA.inv(self.layer.cov.get_value()), dtype=FLOATX)) |
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| 141 | self.layer.cov_det_var.set_value(LA.det(self.layer.cov.get_value())) |
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| 142 | |||
| 143 | # Clean batch cost |
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| 144 | counter = 0 |
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| 145 | cost_sum += batch_cost |
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| 146 | batch_cost = 0.0 |
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| 147 | batch_reward = 0 |
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| 148 | if total == 0: |
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| 149 | return "COST OVERFLOW" |
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| 150 | |||
| 151 | sys.stdout.write("\n") |
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| 152 | self.last_average_reward = (total_reward / total) |
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| 153 | self.turn += 1 |
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| 154 | return "J: %.2f, Avg R: %.4f, Avg P: %.2f" % ((cost_sum / total), self.last_average_reward, (total_position_value / total)) |
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| 155 | |||
| 156 |
