import tensorflow as tf from keras import backend as K from keras import regularizers, constraints, initializers, activations from keras.layers.recurrent import Recurrent, _time_distributed_dense from keras.engine import InputSpec   tfPrint = lambda d, T: tf.Print(input_=T, data=[T, tf.shape(T)], message=d)   class AttentionDecoder(Recurrent):       def __init__(self, units, output_dim,                  activation=‘tanh’,                  return_probabilities=False,                  name=‘AttentionDecoder’,                  kernel_initializer=‘glorot_uniform’,                  recurrent_initializer=‘orthogonal’,                  bias_initializer=‘zeros’,                  kernel_regularizer=None,                  bias_regularizer=None,                  activity_regularizer=None,                  kernel_constraint=None, To finish reading, please visit source site

Last Updated on August 14, 2019 The encoder-decoder architecture for recurrent neural networks is proving to be powerful on a host of sequence-to-sequence prediction problems in the field of natural language processing. Attention is a mechanism that addresses a limitation of the encoder-decoder architecture on long sequences, and that in general speeds up the learning and lifts the skill of the model on sequence-to-sequence prediction problems. In this post, you will discover patterns for implementing the encoder-decoder model with and […]

Last Updated on August 14, 2019 The Keras deep learning library provides an implementation of the Long Short-Term Memory, or LSTM, recurrent neural network. As part of this implementation, the Keras API provides access to both return sequences and return state. The use and difference between these data can be confusing when designing sophisticated recurrent neural network models, such as the encoder-decoder model. In this tutorial, you will discover the difference and result of return sequences and return states for […]

Last Updated on August 14, 2019 The encoder-decoder model provides a pattern for using recurrent neural networks to address challenging sequence-to-sequence prediction problems such as machine translation. Attention is an extension to the encoder-decoder model that improves the performance of the approach on longer sequences. Global attention is a simplification of attention that may be easier to implement in declarative deep learning libraries like Keras and may achieve better results than the classic attention mechanism. In this post, you will […]

Last Updated on August 27, 2020 The encoder-decoder model provides a pattern for using recurrent neural networks to address challenging sequence-to-sequence prediction problems such as machine translation. Encoder-decoder models can be developed in the Keras Python deep learning library and an example of a neural machine translation system developed with this model has been described on the Keras blog, with sample code distributed with the Keras project. This example can provide the basis for developing encoder-decoder LSTM models for your […]

Last Updated on August 14, 2019 Teacher forcing is a method for quickly and efficiently training recurrent neural network models that use the ground truth from a prior time step as input. It is a network training method critical to the development of deep learning language models used in machine translation, text summarization, and image captioning, among many other applications. In this post, you will discover the teacher forcing as a method for training recurrent neural networks. After reading this […]

Last Updated on August 14, 2019 Exploding gradients are a problem where large error gradients accumulate and result in very large updates to neural network model weights during training. This has the effect of your model being unstable and unable to learn from your training data. In this post, you will discover the problem of exploding gradients with deep artificial neural networks. After completing this post, you will know: What exploding gradients are and the problems they cause during training. […]

Last Updated on August 27, 2020 An LSTM Autoencoder is an implementation of an autoencoder for sequence data using an Encoder-Decoder LSTM architecture. Once fit, the encoder part of the model can be used to encode or compress sequence data that in turn may be used in data visualizations or as a feature vector input to a supervised learning model. In this post, you will discover the LSTM Autoencoder model and how to implement it in Python using Keras. After […]