on 11-Jul-2022 (Mon)

Of course, in many cases we don't have enough expert knowledge to draw the true causal DAG that represents a causal structure of treatment A, outcome Y, and potential confounder L. In those cases we may propose several possible causal DAGs without being able choose a particular one. And that's fine, because those causal DAGs that we propose allow us to identify inconsistencies between our beliefs and our actions.

During the Experience phase agents make random decisions and add new entries to the database consisting of a vector with all their sensory input, the randomly chosen action and the result, i.e. if the score increased, decreased or stayed the same due to this decision.

Expanding the framework In the previous section, we found an example in which the presented framework fails, because the states encountered during Experience phase and Application phase differ too much. We will now adapt the framework, in order to increase its scope to such systems.

# Import the sigmoid function from scipy

from scipy.special import expit as sigmoid

# Weight from the model

weight = 0.14

# Print the approximate win probability predicted close game

print(sigmoid(1 * 0.14))

# Print the approximate win probability predicted blowout game

print(sigmoid(10 * 0.14))

the 4 different types of sequence prediction problems: 1. Sequence Prediction. 2. Sequence Classification. 3. Sequence Generation. 4. Sequence-to-Sequence Prediction

Sequence prediction may also generally be referred to as sequence learning. Technically, we could refer to all of the following problems as a type of sequence prediction problem. This can make things confusing for beginner

Some examples of sequence prediction problems include: Weather Forecasting . Given a sequence of observations about the weather over time, predict the expected weather tomorrow. Stock Market Prediction . Given a sequence of movements of a security over time, predict the next movement of the security. Product Recommendation . Given a sequence of past purchases for a customer, predict the next purchase for a customer

Sequence classification involves predicting a class label for a given input sequence.

Some examples of sequence classification problems include:

DNA Sequence Classification
Given a DNA sequence of A, C, G, and T values, predict whether the sequence is for a coding or non-coding region.

Anomaly Detection
Given a sequence of observations, predict whether the sequence is anomalous or not.

Sentiment Analysis .
iven a sequence of text such as a review or a tweet, predict whether the sentiment of the text is positive or negative

Sequence generation involves generating a new output sequence that has the same general characteristics as other sequences in the corpus. For example: Input Sequence: [1, 3, 5], [7, 9, 11] Output Sequence: [3, 5 ,7]

Sequence-to-sequence prediction involves predicting an output sequence given an input sequence. For example: Input Sequence: 1, 2, 3, 4, 5 Output Sequence: 6, 7, 8, 9, 1

Limitations of using MLP for sequence predicting

This can work well on some problems, but it has 5 critical limitations.

Stateless .

MLPs learn a fixed function approximation. Any outputs that are conditional on the context of the input sequence must be generalized and frozen into the network weights.

Unaware of Temporal Structure.

Time steps are modelled as input features, meaning that the network has no explicit handling or understanding of the temporal structure or order between observations.

Messy Scaling .

For problems that require modeling multiple parallel input sequences, the number of input features increases as a factor of the size of the sliding window without any explicit separation of time steps of series.

Fixed Sized Inputs

The size of the sliding window is fixed and must be imposed on all inputs to the network.

Fixed Sized Outputs .

The size of the output is also fixed and any outputs that do not conform must be forced. MLPs do offer great capability for sequence prediction but still suffer from this key limitation of having to specify the scope of temporal dependence between observations explicitly upfront in the design of the model.

Sequences pose a challenge for [deep neural networks] because they require that the dimensionality of the inputs and outputs is known and fixed. — Sequence to Sequence Learning with Neural Networks, 2014 MLPs are a good starting point for modeling sequence prediction problems, but we now have better options.

The Long Short-Term Memory, or LSTM, network is a type of Recurrent Neural Network.

Given a standard feedforward MLP network, an RNN can be thought of as the addition of loops to the architecture. For example, in a given layer, each neuron may pass its signal laterally (sideways) in addition to forward to the next layer. The output of the network may feedback as an input to the network with the next input vector. And so on.

.. recurrent neural networks contain cycles that feed the network activations from a previous time step as inputs to the network to influence predictions at the current time step. These activations are stored in the internal states of the network which can in principle hold long-term temporal contextual information. This mechanism allows RNNs to exploit a dynamically changing contextual window over the input sequence history

— Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling, 2014

The promise of recurrent neural networks is that the temporal dependence and contextual information in the input data can be learned. A recurrent network whose inputs are not fixed but rather constitute an input sequence can be used to transform an input sequence into an output sequence while taking into account contextual information in a flexible way

The LSTM network is different to a classical MLP. Like an MLP, the network is comprised of layers of neurons. Input data is propagated through the network in order to make a prediction. Like RNNs, the LSTMs have recurrent connections so that the state from previous activations of the neuron from the previous time step is used as context for formulating an output. But unlike other RNNs, the LSTM has a unique formulation that allows it to avoid the problems that prevent the training and scaling of other RNNs. This, and the impressive results that can be achieved, are the reason for the popularity of the technique

Unfortunately, the range of contextual information that standard RNNs can access is in practice quite limited. The problem is that the influence of a given input on the hidden layer, and therefore on the network output, either decays or blows up exponentially as it cycles around the network’s recurrent connections. This shortcoming ... referred to in the literature as the vanishing gradient problem ... Long Short-Term Memory (LSTM) is an RNN architecture specifically designed to address the vanishing gradient problem. — A Novel Connectionist System for Unconstrained Handwriting Recognition, 2009

The computational unit of the LSTM network is called the memory cell, memory block, or just cell for short.

LSTM cells are comprised of weights and gates

1.4.1 LSTM Weights

A memory cell has weight parameters for the input, output, as well as an internal state that is built up through exposure to input time steps.

Input Weights. Used to weight input for the current time step.

Output Weights. Used to weight the output from the last time step.

Internal State. Internal state used in the calculation of the output for this time step

1.4.2 LSTM Gates

The key to the memory cell are the gates. These too are weighted functions that further govern the information flow in the cell.

There are three gates:
Forget Gate:
Decides what information to discard from the cell.

Input Gate:
Decides which values from the input to update the memory state.

Output Gate:
Decides what to output based on input and the memory of the cell.

The forget gate and input gate are used in the updating of the internal state. The output gate is a final limiter on what the cell actually outputs.

It is these gates and the consistent data flow called the constant error carrousel or CEC that keep each cell stable (neither exploding or vanishing). Each memory cell’s internal architecture guarantees constant error flow within its constant error carrousel CEC... This represents the basis for bridging very long time lags. Two gate units learn to open and close access to error flow within each memory cell’s CEC. The multiplicative input gate affords protection of the CEC from perturbation by irrelevant inputs. Likewise, the multiplicative output gate protects other units from perturbation by currently irrelevant memory contents

We can summarize the 3 key benefits of LSTMs as:

1. Overcomes the technical problems of training an RNN, namely vanishing and exploding gradients.

2. Possesses memory to overcome the issues of long-term temporal dependency with input sequences

3. Process input sequences and output sequences time step by time step, allowing variable length inputs and outputs

LSTMs are very impressive. The design of the network overcomes the technical challenges of RNNs to deliver on the promise of sequence prediction with neural networks. The applications of LSTMs achieve impressive results on a range of complex sequence prediction problems. But LSTMs may not be ideal for all sequence prediction problems. For example, in time series forecasting, often the information relevant for making a forecast is within a small window of past observations. Often an MLP with a window or a linear model may be a less complex and more suitable model

Time series benchmark problems found in the literature ... are often conceptually simpler than many tasks already solved by LSTM. They often do not require RNNs at all, because all relevant information about the next event is conveyed by a few recent events contained within a small time window. — Applying LSTM to Time Series Predictable through Time-Window Approaches, 2001

A time window based MLP outperformed the LSTM pure-[autoregression] approach on certain time series prediction benchmarks solvable by looking at a few recent inputs only. Thus LSTM’s special strength, namely, to learn to remember single events for very long, unknown time periods, was not necessary

The caution is that LSTMs are not a silver bullet and to carefully consider the framing of your problem. Think of the internal state of LSTMs as a handy internal variable to capture and provide context for making predictions. If your problem looks like a traditional autoregression type problem with the most relevant lag observations within a small window, then perhaps develop a baseline of performance with an MLP and sliding window before considering an LSTM.

The goal of this lesson is for you to understand the Backpropagation Through Time algorithm used to train LSTMs. After completing this lesson, you will know: What Backpropagation Through Time is and how it relates to the Backpropagation training algorithm used by Multilayer Perceptron networks. The motivations that lead to the need for Truncated Backpropagation Through Time, the most widely used variant in deep learning for training LSTMs. A notation for thinking about how to configure Truncated Backpropagation Through Time and the canonical configurations used in research and by deep learning libraries.

The goal of the backpropagation training algorithm is to modify the weights of a neural network in order to minimize the error of the network outputs compared to some expected output in response to corresponding inputs. It is a supervised learning algorithm that allows the network to be corrected with regard to the specific errors made. The general algorithm is as follows: 1. Present a training input pattern and propagate it through the network to get an output. 2. Compare the predicted outputs to the expected outputs and calculate the error. 3. Calculate the derivatives of the error with respect to the network weights. 4. Adjust the weights to minimize the error. 5. Repeat

researchers have found the embedding can be also used in other domains like search and recommendations, where we can put latent meanings into the products to train the machine learning tasks through the use of neural networks

With the similar idea of how we get word embeddings, we can make an analogy like this: a word is like a product; a sentence is like a sequence of ONE customer’s shopping sequence; an article is like a sequence of ALL customers’ shopping sequence. This embedding technique allows us to represent product or user as low dimensional continuous vectors, while the one-hot encoding method will lead to the curse of dimensionality for the machine learning models