Building RNN neural network for NLP

Article directory

    • Code display
    • code intent
    • Code interpretation
      • 1. Embedding Layer
      • 2. SimpleRNN Layer (simple recurrent neural network layer)
      • 3. Dense Layer (fully connected layer)
      • 4. Dense Layer (the second fully connected layer)
      • Summarize
    • Introduction to knowledge points
      • 1. Embedding
      • 2. SimpleRNN
      • 3. Dense

Code display

# Build RNN neural network
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, SimpleRNN, Embedding
import tensorflow astf

dict_size = 20000
max_comment_length = 120

rnn = Sequential()
# For rnn, first perform the word vector operation
rnn.add(Embedding(input_dim=dict_size, output_dim=60, input_length=max_comment_length))
rnn.add(SimpleRNN(units=100)) # The second layer builds 100 RNN neurons
rnn.add(Dense(units=10, activation=tf.nn.relu))
rnn.add(Dense(units=5, activation=tf.nn.softmax)) # Output the classification results
rnn.compile(loss='sparse_categorical_crossentropy', optimizer="adam", metrics=['accuracy'])
print(rnn.summary())

Code intent

The purpose of this code is touse the TensorFlow library to build a simple recurrent neural network (RNN) model for processing text data. Intended applications of this model might be text classification tasks such as sentiment analysis or text topic classification.

Process description:

  1. Import necessary libraries and modules:

    • Sequential: A model used to build linear stacks in Keras.
    • Dense: Fully connected layer.
    • SimpleRNN: Simple RNN layer.
    • Embedding: Embedding layer used to convert integer identifiers (usually words) into fixed-size vectors.

  2. Initialize model:

    • Use the Sequential() method to initialize a new model.

  3. Add embedding layer (Embedding):

    • Map integer indices of words to dense vectors. This is a common way to convert text data into a form that can be processed by a neural network.
    • The input dimension (input_dim) is the size of the vocabulary.
    • The output dimension (output_dim) is the size of the embedding vector.
    • Input length (input_length) is the maximum length of input text.

  4. Add a simple RNN layer (SimpleRNN):

    • This layer has 100 neurons.
    • RNN is a recurrent neural network that can operate on sequence data and capture patterns over time or sequence.

  5. Add two fully connected layers (Dense):

    • The first fully connected layer has 10 neurons and uses the ReLU activation function.
    • The second fully connected layer has 5 neurons and uses a softmax activation function, which may mean that this is a five-class problem.

  6. Compile model:

    • The loss function is ‘sparse_categorical_crossentropy’, which is a common loss function for multi-classification problems.
    • Use the “adam” optimizer.
    • The evaluation criterion is “accuracy”.

  7. Print Model Overview:

    • Use the rnn.summary() method to print the structure and number of parameters of the model.

In this way, a simple RNN model is constructed, and the corresponding data can be used for training and prediction operations.

Code interpretation

Interpret this code line by line and explain the usage and functionality of the functions and imported modules.

from tensorflow.keras.models import Sequential

Import the Sequential class from tensorflow.keras.models. Sequential is a container for linearly stacked layers for simple model building.

from tensorflow.keras.layers import Dense, SimpleRNN, Embedding

Import three layer classes from tensorflow.keras.layers:

  • Dense: Fully connected layer.
  • SimpleRNN: Simple recurrent neural network layer.
  • Embedding: Embedding layer, used to convert positive integers (index values) into fixed-size vectors, often used to process text data.
import tensorflow as tf

Import the TensorFlow library and give it an alias tf.

rnn = Sequential()

Create a new Sequential model object and name it rnn.

rnn.add(Embedding(input_dim=dict_size, output_dim=60, input_length=max_comment_length))

Add an Embedding layer to the model and set the following parameters:

  • input_dim=dict_size: The size of the vocabulary.
  • output_dim=60: Each input integer (i.e. each word) will be converted into a 60-dimensional vector.
  • input_length=max_comment_length: The length of the input sequence.
rnn.add(SimpleRNN(units=100))

Add a SimpleRNN layer to the model with 100 RNN neurons.

rnn.add(Dense(units=10, activation=tf.nn.relu))

Add a fully connected layer Dense to the model with 10 neurons and use the ReLU activation function.

rnn.add(Dense(units=5, activation=tf.nn.softmax))

Add a fully connected layer Dense to the model again, this time with 5 neurons, and use the softmax activation function. The purpose of this layer is usually classification, and 5 neurons means that the model outputs a probability distribution of 5 categories.

rnn.compile(loss='sparse_categorical_crossentropy', optimizer="adam", metrics=['accuracy'])

Use the compile method to configure the learning process of the model. Set the following parameters:

  • loss='sparse_categorical_crossentropy': Loss function, used for classification problems.
  • optimizer="adam": Optimizer, Adam is a commonly used optimization algorithm.
  • metrics=['accuracy']: Model evaluation criteria during training and testing.
print(rnn.summary())

Use the summary method to output summary information of the model, including the type, output shape, and number of parameters of each layer.

print(rnn.summary())
Model: "sequential"
______________________________________________________________
Layer (type) Output Shape Param #
================================================== ===============
embedding (Embedding) (None, 120, 60) 1200000
______________________________________________________________
simple_rnn (SimpleRNN) (None, 100) 16100
______________________________________________________________
dense (Dense) (None, 10) 1010
______________________________________________________________
dense_1 (Dense) (None, 5) 55
================================================== ===============
Total params: 1,217,165
Trainable params: 1,217,165
Non-trainable params: 0
______________________________________________________________
None

This code outputs a summary of a sequential model built using Keras. The model consists of four layers, each layer’s type, output shape, and number of parameters are detailed. This information is explained layer by layer below:

1. Embedding Layer

  • Type: Embedding. This is an embedding layer used to map integers (usually representing word indices) into a high-dimensional space, typically used for processing text data.
  • Output shape: (None, 120, 60). This means that each input sample is converted into a 120×60 matrix. where 120 may be the length of the input sequence (e.g. number of words) and 60 is the embedding dimension.
  • Number of parameters: 1,200,000. This is determined by vocabulary size and embedding dimensions. For example, if the vocabulary size is 20,000 words and each word maps to a 60-dimensional space, the number of parameters is 20,000 * 60 = 1,200,000.

2. SimpleRNN Layer (simple recurrent neural network layer)

  • Type: SimpleRNN. This is a simple recurrent neural network layer for processing sequence data.
  • Output shape: (None, 100). This means that the output for each sample is a 100-dimensional vector.
  • Number of parameters: 16,100. This is determined by the input dimensions, output dimensions of the layer, and the characteristics of the recurrent connections.

3. Dense Layer (fully connected layer)

  • Type: Dense. This is a fully connected layer often used for classification or regression in neural networks.
  • Output shape: (None, 10). The output of each sample is a 10-dimensional vector, potentially representing scores for 10 categories.
  • Number of parameters: 1,010. This is determined by the input dimension (100) and output dimension (10) of the layer. The calculation formula is: number of parameters = input dimension * output dimension > degree + output dimension (bias term).

4. Dense Layer (the second fully connected layer)

  • Type: Dense.
  • Output shape: (None, 5). The output of each sample is a 5-dimensional vector.
  • Number of parameters: 55. It is also determined by the input dimension and the output dimension. The input dimension here is 10 (the output of the previous layer) and the output dimension is 5.

Summary

  • Total number of parameters: 1,217,165. This is the sum of the number of parameters for all layers.
  • Trainable parameters: 1,217,165. All parameters are trainable.
  • Non-trainable parameters: 0. Indicates non-training parameters that do not use layers such as batch normalization (BatchNormalization).

The architecture of this model is a typical sequence processing model, perhaps used for text classification or similar tasks. It starts by converting words into embedding vectors, then processes the sequence through an RNN layer, and finally through two dense layers for classification or regression.

Summary: This code defines and builds a simple RNN model for processing text data. The model consists of an embedding layer, a SimpleRNN layer and two Dense layers.

Introduction to knowledge points

Dense, SimpleRNN, Embedding’s simple principles to implement logic and functions
Of course, I will give you a concise explanation of the basic principles, implementation logic and functions of these three layers.

1. Embedding

Basic Principle:

  • Embedding is a technique for processing categorical data (usually text data) that can convert large amounts of categorical data (such as words) into dense vectors. These vectors capture the semantic relationships between data.

Implementation logic:

  • Suppose we have a vocabulary of size V, the Embedding layer will assign a D-dimensional vector to each word, where D is the preset vector size.
  • When we input an integer i to the embedding layer, it looks up the corresponding D-dimensional vector and returns it.

Features:

  • Convert text or other categorical data into continuous, fixed-size vectors to provide suitable input forms for subsequent deep learning models.

2. SimpleRNN

Basic Principle:

  • RNN (Recurrent Neural Network) is a neural network structure used to process sequence data.
  • RNN has a memory function that can save the hidden state of the previous step and use it as input for the next step.

Implementation logic:

  • At each time step, the RNN receives an input and produces an output.
  • At the same time, it will also use this output as the hidden state for the next time step.
  • SimpleRNN is a simple implementation of RNN that directly uses the output as the hidden state.

Features:

  • Due to its internal memory mechanism, RNN is particularly suitable for processing sequence data such as time series, text, and speech.

3. Dense

Basic Principle:

  • Dense layer, also called fully connected layer, is the most basic layer in deep learning.
  • Every input node is connected to every output node.

Implementation logic:

  • If we have N inputs and M outputs, then this Dense layer will have N*M weights and M biases.
  • When the input data is passed to the Dense layer, it performs matrix multiplication and bias operations, and is usually followed by an activation function.

Features:

  • Perform non-linear transformations to help neural networks capture and learn more complex patterns and relationships.

In short, Embedding, SimpleRNN and Dense are all commonly used layers in deep learning models. Embedding is used to process text data, SimpleRNN processes sequence data, and the Dense layer adds non-linear capabilities and scalability to the model.