- Constants
- Variables
- Placeholder
- Graph
- Session
In TensorFlow, there are several fundamental objects that are crucial for building and working with machine learning models. Some of the key TensorFlow objects include:
- Tensors: Tensors are the fundamental building blocks in TensorFlow. They represent the data flowing through the computational graph. Tensors can be scalars, vectors, matrices, or higher-dimensional arrays.
- Variables: Variables are used to hold and update parameters of the model during training. They are mutable tensors that persist across multiple calls to the computational graph. Variables are typically used to represent weights and biases in neural network models.
- Operations (Ops): Operations represent mathematical operations or transformations applied to tensors. These operations define the computations within the computational graph. Examples of operations include addition, multiplication, convolution, and activation functions like ReLU.
- Graphs: TensorFlow computations are represented as dataflow graphs, where nodes represent operations and edges represent tensors flowing between operations. The computational graph defines the relationship between different operations and tensors in the model.
- Sessions: Sessions are used to execute operations and evaluate tensors within a TensorFlow graph. When a session is created and run, TensorFlow allocates resources and performs computations according to the specified graph.
- Layers: TensorFlow provides a high-level API called TensorFlow Layers for building neural network models. Layers encapsulate groups of operations and parameters, making it easier to construct and manipulate complex models.
- Models: In TensorFlow, models are typically built using layers and may include additional components such as loss functions, optimizers, and evaluation metrics. Models are trained using data and updated based on feedback from the training process.
Understanding these fundamental TensorFlow objects is essential for effectively building and working with machine learning models using the TensorFlow framework.