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2 changes: 1 addition & 1 deletion hyperparams.py
Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@ class Hyperparams:
num_epochs = 20
num_heads = 8
dropout_rate = 0.1
sinusoid = False # If True, use sinusoid. If false, positional embedding.
sinusoid = True # If True, use sinusoid. If false, positional embedding.



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24 changes: 17 additions & 7 deletions modules.py
Original file line number Diff line number Diff line change
Expand Up @@ -4,16 +4,19 @@
June 2017 by kyubyong park.
[email protected].
https://www.github.com/kyubyong/transformer

'''

from __future__ import print_function
import tensorflow as tf

import numpy as np
def normalize(inputs,
epsilon = 1e-8,
scope="ln",
reuse=None):
'''Applies layer normalization.
'''

Applies layer normalization.

Args:
inputs: A tensor with 2 or more dimensions, where the first dimension has
Expand Down Expand Up @@ -45,7 +48,8 @@ def embedding(inputs,
scale=True,
scope="embedding",
reuse=None):
'''Embeds a given tensor.
'''
Embeds a given tensor.

Args:
inputs: A `Tensor` with type `int32` or `int64` containing the ids
Expand Down Expand Up @@ -153,15 +157,20 @@ def positional_encoding(inputs,

# Convert to a tensor
lookup_table = tf.convert_to_tensor(position_enc)

lookup_table= tf.cast(lookup_table, tf.float32)

if zero_pad:
lookup_table = tf.concat((tf.zeros(shape=[1, num_units]),
lookup_table[1:, :]), 0)


outputs = tf.nn.embedding_lookup(lookup_table, position_ind)



if scale:
outputs = outputs * num_units**0.5



return outputs


Expand Down Expand Up @@ -260,7 +269,8 @@ def feedforward(inputs,
num_units=[2048, 512],
scope="multihead_attention",
reuse=None):
'''Point-wise feed forward net.
'''
Point-wise feed forward net.

Args:
inputs: A 3d tensor with shape of [N, T, C].
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1 change: 0 additions & 1 deletion train.py
Original file line number Diff line number Diff line change
Expand Up @@ -88,7 +88,6 @@ def __init__(self, is_training=True):
## Positional Encoding
if hp.sinusoid:
self.dec += positional_encoding(self.decoder_inputs,
vocab_size=hp.maxlen,
num_units=hp.hidden_units,
zero_pad=False,
scale=False,
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