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- 1. DLT UNIT-3 1 (a) What is the Anatomy of a neural network? Explain building blocks of deep learning? Training a neural network revolves around the following objects: Layers, which are combined into a network (or model) The input data and corresponding targets The loss function, which defines the feedback signal used for learning The optimizer, which determines how learning proceeds Layers: the building blocks of DL A layer is a data-processing module that takes as input tensors and that outputs tensors. Different layers are appropriate for different types of data processing. Dense layers for 2D tensors (samples, features) - simple vector data RNNs (or LSTMs) for 3D tensors (samples, time-steps, features) - sequence data
- 2. CNNs for 4D tensors (samples, height, width, colour_depth) - image data We can think of layers as the LEGO bricks of deep learning. Building deep-learning models in Keras is done by clipping together compatible layers to form useful data-transformation pipelines. In Keras, the layers we add to our models are dynamically built to match the shape of the incoming layer. from keras import models from keras import layers model = models.Sequential() model.add(layers.Dense(32, input_shape=(784,))) model.add(layers.Dense(32)) The second layer didn’t receive an input shape argument - instead, it automatically inferred its input shape as being the output shape of the layer that came before 1(b) List the key features of Keras? Write two options for running Keras. Keras is an open-source deep learning framework that is known for its user-friendliness and versatility. It's built on top of other deep learning libraries like TensorFlow and Theano, which allows users to easily create and train neural networks. Here are some key features of Keras: 1. User-Friendly: Keras is designed to be user-friendly and easy to use. Its high-level API makes it accessible to both beginners and experienced machine learning practitioners. 2. Modularity: Keras is built with a modular architecture. It allows users to construct neural networks by stacking layers, making it easy to design complex network architectures. 3. Support for Multiple Backends: Keras originally supported multiple backends like TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK). However, since TensorFlow 2.0, Keras has been integrated as the official high-level API of TensorFlow, making TensorFlow the default backend.
- 3. 4. Extensibility: Keras is highly extensible, allowing users to define custom layers, loss functions, and metrics. This makes it suitable for research and experimentation. 5. Pre-trained Models: Keras provides access to popular pre- trained models for tasks like image classification, object detection, and natural language processing through its applications module. These pre-trained models can be fine- tuned for specific tasks. 6. GPU Support: Keras leverages the computational power of GPUs, which significantly accelerates the training of deep neural networks. 7. Visualization Tools: Keras includes tools for visualizing model architectures, training history, and more, making it easier to understand and debug neural networks. 8. Callback System: Keras offers a callback system that allows users to specify functions to be executed at various stages during training. This can be used for tasks like model checkpointing, early stopping, and custom logging. 9. Integration with Data Libraries: Keras seamlessly integrates with popular data manipulation libraries like NumPy and data preprocessing libraries like TensorFlow Data Validation (TFDV) and TensorFlow Data Validation (TFDV). Two options for running Keras are: 1. TensorFlow with Keras: As of TensorFlow 2.0 and later, Keras is included as the official high-level API of TensorFlow. You can use Keras by simply importing it from TensorFlow and building your models using the Keras API. For example:
- 4. 2. Stand-alone Keras with TensorFlow Backend: Before TensorFlow 2.0, Keras was often used as a standalone library with TensorFlow as a backend. You can install and use standalone Keras by installing the Keras package and configuring it to use TensorFlow as the backend. Here's how you can do it: Install Keras: pip install keras Configure Keras to use TensorFlow backend: 3. You can then build and train your models using the standalone Keras API as you would with TensorFlow. Note that, as of TensorFlow 2.0, it's recommended to use Keras through TensorFlow due to its seamless integration and the fact that Keras is now the official high-level API of TensorFlow.
- 5. 2 (a) How to set up the deep learning workstations? Explain with example.
- 6. 2 (b) What is hypothesis space and explain the functionalities of Loss functions and Optimizers? Hypothesis Space: The hypothesis space, often referred to as the hypothesis class or model space, is a fundamental concept in machine learning and statistical modeling. It represents the set of all possible
- 7. models or functions that a machine learning algorithm can use to make predictions or approximate a target variable. In simpler terms, it's the space of all possible solutions that the algorithm considers when trying to learn from data. The hypothesis space depends on the choice of machine learning algorithm and the model architecture. For example: In linear regression, the hypothesis space includes all possible linear functions of the input features. In decision tree algorithms, the hypothesis space includes all possible binary decision trees that can be constructed from the features. In neural networks, the hypothesis space consists of all possible network architectures with varying numbers of layers and neurons in each layer. The goal of training a machine learning model is to search within this hypothesis space to find the best model that fits the given data and generalizes well to unseen data. This search is guided by a combination of loss functions and optimizers. Loss Functions: A loss function, also known as a cost function or objective function, quantifies how well a machine learning model's predictions match the actual target values in the training data. It essentially measures the "loss" or error between the predicted values and the true values. The choice of a loss function depends on the type of machine learning task you're working on: 1. Regression Tasks: In regression problems where the goal is to predict a continuous value (e.g., predicting house prices), common loss functions include mean squared error (MSE) and mean absolute error (MAE). MSE penalizes larger errors more heavily, while MAE treats all errors equally. 2. Classification Tasks: In classification problems where the goal is to assign data points to discrete classes or categories (e.g., image classification), common loss functions include cross- entropy loss (log loss) for binary or multi-class classification.
- 8. 3. Custom Loss Functions: In some cases, you might need to design custom loss functions to address specific requirements or challenges in your problem domain. The optimizer's role is to minimize the loss function by adjusting the model's parameters during the training process. Optimizers: Optimizers are algorithms or methods used to update the model's parameters (e.g., weights and biases in a neural network) in order to minimize the loss function. They determine how the model should adjust its parameters to make its predictions more accurate. Common optimizers include: 1. Gradient Descent: Gradient descent is a fundamental optimization algorithm that iteratively updates model parameters in the direction of the steepest decrease in the loss function. Variants of gradient descent include stochastic gradient descent (SGD), mini-batch gradient descent, and more advanced algorithms like Adam and RMSprop. 2. Adaptive Learning Rate Methods: These optimizers automatically adjust the learning rate during training to speed up convergence. Examples include Adam, RMSprop, and Adagrad. 3. Constrained Optimization Methods: In some cases, optimization may need to adhere to certain constraints, such as L1 or L2 regularization. Algorithms like L-BFGS and Conjugate Gradient can be used for constrained optimization. 4. Evolutionary Algorithms: In some cases, optimization problems are solved using evolutionary algorithms like genetic algorithms and particle swarm optimization. The choice of optimizer can significantly impact the training speed and final performance of a machine learning model. It's often necessary to experiment with different optimizers and hyperparameters to find the best combination for a specific problem.