2019年4月現在の最新状況を踏まえた資料更新です。
確率的LUTモデルの追加とBinaryBrain version3 の公開が主な内容です。
English Version
https://www.slideshare.net/ryuz88/lutnetwork-revision2-english-version
BinaryBrain
https://github.com/ryuz/BinaryBrain
El documento presenta un resumen de tres movimientos artísticos importantes: el expresionismo, el cubismo y el arte moderno. Menciona obras emblemáticas como "El grito" de Edvard Munch perteneciente al expresionismo y "La mujer" de Pablo Picasso asociada al arte moderno. Además, brinda una definición general de arte señalando que surgió para cumplir funciones rituales, mágicas o religiosas en el paleolítico y que luego adquirió componentes estéticos y funciones sociales y pedagógic
2019年4月現在の最新状況を踏まえた資料更新です。
確率的LUTモデルの追加とBinaryBrain version3 の公開が主な内容です。
English Version
https://www.slideshare.net/ryuz88/lutnetwork-revision2-english-version
BinaryBrain
https://github.com/ryuz/BinaryBrain
El documento presenta un resumen de tres movimientos artísticos importantes: el expresionismo, el cubismo y el arte moderno. Menciona obras emblemáticas como "El grito" de Edvard Munch perteneciente al expresionismo y "La mujer" de Pablo Picasso asociada al arte moderno. Además, brinda una definición general de arte señalando que surgió para cumplir funciones rituales, mágicas o religiosas en el paleolítico y que luego adquirió componentes estéticos y funciones sociales y pedagógic
This document discusses using a recurrent neural network (RNN) to manage an environment and take actions. The RNN learns to maximize reward by updating its value function based on the reward received and the difference in value estimates for the current and next states. The RNN predicts the probability of actions at each time step based on the previous states and actions.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
- The document is a summary of a meeting about deep learning using Python. It includes an agenda of topics to be covered such as basic neural network concepts, cross entropy, convolutional neural networks, and trying Keras.
- It also provides information about a Slack team created for further discussion and questions, and encourages participation. Prerequisites and explanations of key concepts like neurons and convolution are also summarized.
4. ご注意
● TensorFlow 0.10 から RNN パッケージは
tf.models.rnn から tf.nn に移動しまし
た。tf.nn.rnn_cell 以下にあるファイルを
使ってください。
● TensorFlow 0.10 moved the recurrent network operations from
tf.models.rnn into the tf.nn package where they live along
the other neural network operations now. Cells can now be found in
tf.nn.rnn_cell.
48. How does LSTM work?
1. LSTM replaces logistic or tanh hidden units with “memory cells” that
can store an analog value.
2. Each memory cell has its own input and output gates that control.
3. There is a forget gate which the analog value stored in the memory cell
decays.
4. For periods when the input and output gates are off and the forget gate is
not causing decay, a memory cell simply holds its value over time.
Le, Jaitly, & Hinton (2015)
49. 別モデル GRU(An alternative of the LSTM)
h~h
x
y
r: reset
gate
input
output
uupdate
gate
ut
= s (Wu
+ uu
)
ht
= f (Wh
+ uh
(ut
@ )
rt
= s (Wr
+ ur
ht-1
)
tilde(h) = (1- rt
) ht
+ rt
tilde(ht-1
)
yt
= Wy
tilde(ht
)
50. 別モデル GRU(An alternative of the LSTM)
h~h
x
y
r: reset
gate
input
output
uupdate
gate
ut = σ (Wuxt + Uuht−1) .
ht = ϕ(Wxt + Uh (ut ⊙ht−1)) ,
rt = σ (Wrxt + Urht−1) ,
˜ht = (1 − rt) ht + rt
˜ht−1,
yt = Wy
˜ht
54. Pascanu (2014) より
y( t )
h( t )h( t − 1)
x ( t )
y( t )
h( t )h( t − 1)
x ( t )
y( t )
h( t )h( t − 1)
x ( t )
( a ) ( b ) ( c )
y( t )
h( t )h( t − 1)
x ( t )
y( t )
h( t − 1)
x ( t ) z( t )
z( t )h( t )
( d ) ( e )
図 4.27 パスカヌらの文献 108) の図 2 を改変
55. Pascanu (2014) より
するセルどうしをつないで 2 次元格子状,3 次元格子状に配列することを提案
している。
図 4.33 はコウトニック(J.Koutonik)の時計状 LSTM である116)
。
I * xi
m
h
m ′1h′1
m ′2
h′2
m 1h1
m 2
h2
m ′
h′
h′
2 次元格子状 LSTM
ブロック
標準の LSTM
ブロック
1 次元格子状 LSTM
ブロック
3 次元格子状 LSTM
ブロック
図 4.32 格 子 状 LSTM
出力層