HenryAI/KerasBERTv1-Data · Datasets at Hugging Face

text stringlengths 0 4.99k
<tensorflow.python.keras.callbacks.History at 0x15f3240d0>
For more information, make sure to read the Functional API guide.Working with RNNs
Authors: Scott Zhu, Francois Chollet
Date created: 2019/07/08
Last modified: 2020/04/14
Description: Complete guide to using & customizing RNN layers.

View in Colab • GitHub source

Introduction
Recurrent neural networks (RNN) are a class of neural networks that is powerful for modeling sequence data such as time series or natural language.

Schematically, a RNN layer uses a for loop to iterate over the timesteps of a sequence, while maintaining an internal state that encodes information about the timesteps it has seen so far.

The Keras RNN API is designed with a focus on:

Ease of use: the built-in keras.layers.RNN, keras.layers.LSTM, keras.layers.GRU layers enable you to quickly build recurrent models without having to make difficult configuration choices.
Ease of customization: You can also define your own RNN cell layer (the inner part of the for loop) with custom behavior, and use it with the generic keras.layers.RNN layer (the for loop itself). This allows you to quickly prototype different research ideas in a flexible way with minimal code.
Setup
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
Built-in RNN layers: a simple example
There are three built-in RNN layers in Keras:

keras.layers.SimpleRNN, a fully-connected RNN where the output from previous timestep is to be fed to next timestep.

keras.layers.GRU, first proposed in Cho et al., 2014.

keras.layers.LSTM, first proposed in Hochreiter & Schmidhuber, 1997.

In early 2015, Keras had the first reusable open-source Python implementations of LSTM and GRU.

Here is a simple example of a Sequential model that processes sequences of integers, embeds each integer into a 64-dimensional vector, then processes the sequence of vectors using a LSTM layer.

model = keras.Sequential()
# Add an Embedding layer expecting input vocab of size 1000, and
# output embedding dimension of size 64.
model.add(layers.Embedding(input_dim=1000, output_dim=64))

# Add a LSTM layer with 128 internal units.
model.add(layers.LSTM(128))

# Add a Dense layer with 10 units.
model.add(layers.Dense(10))

model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, None, 64) 64000
_________________________________________________________________
lstm (LSTM) (None, 128) 98816
_________________________________________________________________
dense (Dense) (None, 10) 1290
=================================================================
Total params: 164,106
Trainable params: 164,106
Non-trainable params: 0
_________________________________________________________________
Built-in RNNs support a number of useful features:

Recurrent dropout, via the dropout and recurrent_dropout arguments
Ability to process an input sequence in reverse, via the go_backwards argument
Loop unrolling (which can lead to a large speedup when processing short sequences on CPU), via the unroll argument
...and more.
For more information, see the RNN API documentation.

Outputs and states
By default, the output of a RNN layer contains a single vector per sample. This vector is the RNN cell output corresponding to the last timestep, containing information about the entire input sequence. The shape of this output is (batch_size, units) where units corresponds to the units argument passed to the layer's constructor.

A RNN layer can also return the entire sequence of outputs for each sample (one vector per timestep per sample), if you set return_sequences=True. The shape of this output is (batch_size, timesteps, units).

model = keras.Sequential()
model.add(layers.Embedding(input_dim=1000, output_dim=64))

# The output of GRU will be a 3D tensor of shape (batch_size, timesteps, 256)
model.add(layers.GRU(256, return_sequences=True))

# The output of SimpleRNN will be a 2D tensor of shape (batch_size, 128)
model.add(layers.SimpleRNN(128))

model.add(layers.Dense(10))

model.summary()
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_1 (Embedding) (None, None, 64) 64000
_________________________________________________________________
gru (GRU) (None, None, 256) 247296
_________________________________________________________________
simple_rnn (SimpleRNN) (None, 128) 49280
_________________________________________________________________
dense_1 (Dense) (None, 10) 1290
=================================================================
Total params: 361,866

<tensorflow.python.keras.callbacks.History at 0x15f3240d0>

For more information, make sure to read the Functional API guide.Working with RNNs

Authors: Scott Zhu, Francois Chollet

Date created: 2019/07/08

Last modified: 2020/04/14

Description: Complete guide to using & customizing RNN layers.

View in Colab • GitHub source

Introduction

Recurrent neural networks (RNN) are a class of neural networks that is powerful for modeling sequence data such as time series or natural language.

Schematically, a RNN layer uses a for loop to iterate over the timesteps of a sequence, while maintaining an internal state that encodes information about the timesteps it has seen so far.

The Keras RNN API is designed with a focus on:

Ease of use: the built-in keras.layers.RNN, keras.layers.LSTM, keras.layers.GRU layers enable you to quickly build recurrent models without having to make difficult configuration choices.

Ease of customization: You can also define your own RNN cell layer (the inner part of the for loop) with custom behavior, and use it with the generic keras.layers.RNN layer (the for loop itself). This allows you to quickly prototype different research ideas in a flexible way with minimal code.

Setup

import numpy as np

import tensorflow as tf

from tensorflow import keras

from tensorflow.keras import layers

Built-in RNN layers: a simple example

There are three built-in RNN layers in Keras:

keras.layers.SimpleRNN, a fully-connected RNN where the output from previous timestep is to be fed to next timestep.

keras.layers.GRU, first proposed in Cho et al., 2014.

keras.layers.LSTM, first proposed in Hochreiter & Schmidhuber, 1997.

In early 2015, Keras had the first reusable open-source Python implementations of LSTM and GRU.

Here is a simple example of a Sequential model that processes sequences of integers, embeds each integer into a 64-dimensional vector, then processes the sequence of vectors using a LSTM layer.

model = keras.Sequential()

# Add an Embedding layer expecting input vocab of size 1000, and

# output embedding dimension of size 64.

model.add(layers.Embedding(input_dim=1000, output_dim=64))

# Add a LSTM layer with 128 internal units.

model.add(layers.LSTM(128))

# Add a Dense layer with 10 units.

model.add(layers.Dense(10))

model.summary()

Model: "sequential"

_________________________________________________________________

Layer (type) Output Shape Param #

=================================================================

embedding (Embedding) (None, None, 64) 64000

_________________________________________________________________

lstm (LSTM) (None, 128) 98816

_________________________________________________________________

dense (Dense) (None, 10) 1290

=================================================================

Total params: 164,106

Trainable params: 164,106

Non-trainable params: 0

_________________________________________________________________

Built-in RNNs support a number of useful features:

Recurrent dropout, via the dropout and recurrent_dropout arguments

Ability to process an input sequence in reverse, via the go_backwards argument

Loop unrolling (which can lead to a large speedup when processing short sequences on CPU), via the unroll argument

...and more.

For more information, see the RNN API documentation.

Outputs and states

By default, the output of a RNN layer contains a single vector per sample. This vector is the RNN cell output corresponding to the last timestep, containing information about the entire input sequence. The shape of this output is (batch_size, units) where units corresponds to the units argument passed to the layer's constructor.

A RNN layer can also return the entire sequence of outputs for each sample (one vector per timestep per sample), if you set return_sequences=True. The shape of this output is (batch_size, timesteps, units).

model = keras.Sequential()

model.add(layers.Embedding(input_dim=1000, output_dim=64))

# The output of GRU will be a 3D tensor of shape (batch_size, timesteps, 256)

model.add(layers.GRU(256, return_sequences=True))

# The output of SimpleRNN will be a 2D tensor of shape (batch_size, 128)

model.add(layers.SimpleRNN(128))

model.add(layers.Dense(10))

model.summary()

Model: "sequential_1"

_________________________________________________________________

Layer (type) Output Shape Param #

=================================================================

embedding_1 (Embedding) (None, None, 64) 64000

_________________________________________________________________

gru (GRU) (None, None, 256) 247296

_________________________________________________________________

simple_rnn (SimpleRNN) (None, 128) 49280

_________________________________________________________________

dense_1 (Dense) (None, 10) 1290

=================================================================

Total params: 361,866