Sentiment Analysis with RNNs in Keras: How Does Neural Network Learn by Adjusting Weights and Biases?

What Is the Role of Weights and Biases in Minimizing Error During Model Training?

Understand the core of neural network training: the iterative adjustment of weights and biases to minimize a loss function. Learn how backpropagation and optimizers like Adam work in Keras to fine-tune your model for tasks like sentiment analysis, improving prediction accuracy with each epoch.

Question

LSTMs overcome vanishing gradient problems of standard RNNs.

A. The number of layers automatically
B. The dataset content
C. The length of padded sequences
D. The model’s weights and biases

Answer

D. The model’s weights and biases

Explanation

Training adjusts weights and biases to minimize error. The fundamental process of training any neural network, including LSTMs, involves iteratively updating the model’s internal parameters—its weights and biases—to minimize the difference between its predictions and the actual target values.

The training process of a neural network is an optimization problem aimed at finding the set of weights and biases that results in the lowest possible error, or “loss.” This is achieved through a process called backpropagation, combined with an optimization algorithm like gradient descent.

1. Initialization: Before training begins, the model’s weights and biases are initialized, often with small, random values.
2. Forward Propagation: A batch of data is fed into the network. At each layer, the input data is multiplied by the layer’s weights, and a bias is added. This result is then passed through an activation function, and the output becomes the input for the next layer. This continues until the final layer produces a prediction.
3. Loss Calculation: The model’s prediction is compared to the true label from the dataset using a loss function (e.g., binary cross-entropy for sentiment classification). This function quantifies how wrong the model’s prediction was.
4. Backward Propagation (Backpropagation): The algorithm calculates the gradient of the loss function with respect to every weight and bias in the network. The gradient is a vector that points in the direction of the steepest ascent of the loss function; therefore, moving in the opposite direction will decrease the loss.
5. Parameter Update: An optimizer (such as Adam or RMSprop) uses these calculated gradients to update the weights and biases. It adjusts each parameter slightly in the direction that minimizes the loss. The size of this adjustment is controlled by the learning rate.

This cycle is repeated for many epochs (passes through the entire dataset), with the model’s weights and biases continuously being fine-tuned to improve its accuracy.

A. The number of layers automatically (Incorrect): The number of layers defines the model’s architecture. It is a hyperparameter set by the developer before training starts and is not altered during the training process itself.

B. The dataset content (Incorrect): The dataset is the source of truth that the model learns from. The training process uses the data but does not change it.

C. The length of padded sequences (Incorrect): The length of sequences is determined during the data preprocessing step (e.g., using pad_sequences in Keras). It is a fixed hyperparameter chosen to ensure all inputs have a uniform shape and is not modified during training.

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