learning/annotated_deep_learning_paper_implementations
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no loss smoothing

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This commit is contained in:
Varuna Jayasiri
2021-01-07 12:02:19 +05:30
parent b9da12ee3f
commit dfefde657b
2 changed files with 3 additions and 2 deletions
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2
labml_nn/experiments/nlp_autoregression.py
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@ -49,8 +49,8 @@ class NLPAutoRegressionConfigs(TrainValidConfigs):
d_model: int = 512 d_model: int = 512
def init(self): def init(self):
tracker.set_queue("loss.*", 20, True)
tracker.set_scalar("accuracy.*", True) tracker.set_scalar("accuracy.*", True)
tracker.set_scalar("loss.*", True)
hook_model_outputs(self.mode, self.model, 'model') hook_model_outputs(self.mode, self.model, 'model')
self.state_modules = [self.accuracy] self.state_modules = [self.accuracy]

3
labml_nn/transformers/positional_encoding.py
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@ -22,7 +22,6 @@ Where $1 \leq 2i, 2i + 1 \leq d_{model}$
import math import math
import matplotlib.pyplot as plt
import numpy as np import numpy as np
import torch import torch
import torch.nn as nn import torch.nn as nn
@ -65,6 +64,8 @@ def get_positional_encoding(d_model: int, max_len: int = 5000):
def _test_positional_encoding(): def _test_positional_encoding():
import matplotlib.pyplot as plt
plt.figure(figsize=(15, 5)) plt.figure(figsize=(15, 5))
pe = get_positional_encoding(20, 100) pe = get_positional_encoding(20, 100)
plt.plot(np.arange(100), pe[:, 0, 4:8].numpy()) plt.plot(np.arange(100), pe[:, 0, 4:8].numpy())