Recurrent neural networks can be described as extremely robust and powerful kind of neural network which is among the most exciting algorithms currently that are currently in use since theyre the only kind of neural network equipped with internal memory. RNNs are able to remember crucial details about input they have received and can predict with precision whats to come next.
Theyre also the best algorithm to process sequences of data such as time sequences speech text and financial information as well as video audio weather and lot more. Recurrent neural networks may provide an incredibly deep understanding of the sequence and its context compared to other algorithms. As RNNs are utilized for the programs that powers Siri as well as Google Translate recurrent neural networks appear frequently all the time in daily life.
Types Of Recurrent Neural Networks
Theres four kinds of RNNs that are based on the quantity of inputs as well as outputs that are part of the network:
1. One to One RNN
One to One RNNbehaves like it is the vanilla Neural Network is the most basic type of neural network structure. With this configuration there only one input as well as single output. It is typically used to classify simple task where input is not dependent upon previous components.
2. One to Many RNN
In the case of One to Many RNN the network process one input element to generate various outputs throughout the course of. This configuration is useful in situations where single input is expected to produce set of forecasts.
In the case of an image captioning tasks using just one picture is the source and the model anticipates string of captions.
3. Many to One RNN
It is Many to 1 RNN takes series of inputs and produces one output. This kind of RNN is helpful for situations when the entire background of inputs is required to create single predicate.
In the process of sentiment analysis model is able to read string of words (like an entire sentence) and creates one output that represents the mood that the sentences convey (positive positive negative or neutral).
4. Many to Many RNN
This Many to Many RNN type takes an input sequence and produces series of outputs. This is great configuration when sequences of input and output have to coincide with time usually as one to 1 or many to many mapping.
When performing task of translation an alphabet of words from one language are given as input. Then equivalent sequence from another one is created in output.
Variants of Recurrent Neural Networks (RNNs)
There are variety of RNNs that are designed to tackle specific problems or to optimize specific jobs:
1. Vanilla RNN
The simplest version of RNN comprises single invisible layer in which weights are distributed over time intervals. Vanilla RNNs work well for short term dependency learning however they have limitations due to the vanishing gradient issue which hinders long sequence learning.
2. Bidirectional RNNs
Bidirectional RNNs handle inputs both in direction of forward and reverse by capturing past as well as future information for each step. This is great solution for applications where the entire sequence of events is accessible for use like name entity recognition or question answer.
3. Long Short Term Memory Networks (LSTMs)
Long Short Term Memory Networks (LSTMs) offer memory system to solve the vanishing gradient issue. Every LSTM cell is equipped with three gates.
- Input Gate controls how much data should be included in the cells status.
- Forget Gate: Chooses which past data is to be deleted.
- Output Gate regulates the information that will be displayed at the moment of. This type of memory allows LSTMs to deal with dependencies that last for long time which makes them perfect to be used in cases where prior information is crucial.
4. Gated Recurrent Units (GRUs)
Gated Recurrent Units (GRUs)simplify LSTMs by merging the gates for input and forget into one update gate and streamlining the output system. It is computationally efficient design which is often comparable as LSTMs as well as being useful for tasks where speed as well as faster learning are advantageous.
Recurrent Neural Network Architecture
RNNs have lot in common with structure of output and input with other models of deep learning however they differ in the way information is transferred from input to output. In contrast to traditional deep neural networks in which each layer is distinct weight matrices RNNs utilize the same weights for all time steps which allows them to retain the information across sequences.
Within an RNN every time step is made up of units which have specific activation mechanism. Every unit has an internal hidden state which functions as memory by keeping information from prior time steps. This allows the network to save the past information. The hidden state is constantly updated to take into account new inputs which allows the network to adapt its understanding of inputs from the past.
Updating the Hidden State in RNNs
The present state ht varies based on earlier state ht 1 as well as the input type xt at present and is determined by using the following relationships:
1. State Update:
ht=f(ht 1xt)
where:
- It is currently in the status
- The previous state is ht 1.
- This is the input for the time of the present step
2. Activation Function Application:
ht=tanh(Whhht 1+Wxhxt)
In this case Whh is the weight matrix used by the neuron that is recurrent while Wxh is the matrix of weight that is used for the input neuron.
3. Output Calculation:
yt=Whyht
in which yt represents the output and what is the weight in the layer of output.
Training Process in Recurrent Neural Networks
In order to train RNNs you must feed the input data through various time stages recording dependencies between these stages and then re inforcing the model by backpropagation.
The stages of RNN course consist of:
- Input At Each Time Step Each time step in this input sequence transmitted for the entire network.
- Calculate hidden state Utilizing the present input as well as the previously secret state the network determines the hidden state of the present in ht.
- State Transformation state ht: The present hidden state ht will then change to Ht 1 in the following time step.
- Sequential Processing The process is repeated through all steps of time in order to collect information from prior states.
- Output Generating and Error Calculation: The hidden state used is used to calculate the output of the network which can then be checked against the output of the target in order to produce an error.
- Backpropagation through Time (BPTT): This error is propagated to each time step to modify weights and to train the RNN.
Implementing Text Generator Using Recurrent Neural Networks (RNNs)
In this part we will create the character based generator for text made using Recurrent Neural Network (RNN) using TensorFlow as well as Keras. Well implement an RNN that learns patterns from text sequence to generate new text character by character.
Step 1: Import Necessary Libraries
The first step is to import the essential libraries that handle data and then developing our neural network.
Step 2: Define the Input Text and Prepare Character Set
We will define the input text and then identify the unique characters within the text. Well encode to our model.
Step 3: Create Sequences and Labels
For training the RNN to train the RNN we require sequences with duration fixed (seq_length) and character in the sequence that is followed by the label.
Step 4: Convert Sequences and Labels to One Hot Encoding
In order to train for training we convert X and the y to one hot encoded Tensors.
Step 5: Build the RNN Model
The model is very basic RNN model using hidden layer consisting of 50 units as well as dense output layer which has Softmax activation.
Step 6: Compile and Train the Model
We compile the model using the categorical_crossentropy loss and train it for 100 epochs.
Step 7: Generate New Text Using the Trained Model
After training we use starting sequence to generate new text character by character.
Advantages of Recurrent Neural Networks
- Sequential Memory RNNs store information that was previously input which makes the perfect time series prediction in which past information is essential. The term used to describe this capability is “Long Short Term Memory” (LSTM).
- Enhanced Pixel Neighborhoods RNNs may be used in conjunction with convolutional layers to create extended pixel neighborhood which can improve the performance of the processing of video and image data.
Limitations of Recurrent Neural Networks (RNNs)
Although RNNs excel at analyzing sequenced data they have to face two major training issues i.e. the vanishing gradient as well as the exploding gradient:
- The Gradient is Vanishing Backpropagation: Gradients decrease as they go through each step of the process and result in lack of weight adjustments. This hinders the ability of RNNs to understand long term dependency important in tasks such as translation of languages.
- Extending Gradient The gradients may expand uncontrollably and cause huge weight adjustments that cause instability in learning. Gradient clipping is an effective method to deal with this problem.
These issues can hamper the effectiveness of standard RNNs for complex lengthy sequence task.
Applications of Recurrent Neural Networks
RNNs are employed in many applications that require data to be sequenced or time based
- Timing Series prediction RNNs do well in forecasting jobs like prediction of stock prices as well as weather forecasting.
- Natural Language Processing (NLP) The role of RNNs is fundamental for NLP tasks such as speech modeling the analysis of sentiment machine translation.
- Speech Recognition RNNs can capture spatial patterns in speech data which aids in speech to text and other applications involving audio.
- Video and Image Processing when combined with convolutional layers RNNs assist in analyzing the video facial expressions facial expressions and gesture recognition.
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