Conteúdo do Curso
Introduction to TensorFlow
Introduction to TensorFlow
Applications of Tensors
Applications of Tensors
Tensors, with their multi-dimensional nature, find applications across a diverse range of data tasks. Their structure and shape are pivotal in determining how they represent and process data in various scenarios. Let's explore:
- Table Data: Often represented in 2D tensors, table data is reminiscent of matrices. Each row can represent a data entry, and each column might signify a feature or attribute of the data. For instance, a dataset with 1000 samples and 10 features would be encapsulated in a tensor of shape
(1000, 10)
;
- Text Sequences: Sequences, such as a time series or text data, are typically mapped to 2D tensors. One dimension sequences through time or length, while the other denotes features at each timestep. A
200
-word text processed with embeddings of size50
would translate to a tensor of(200, 50)
;Note
Embeddings in text processing are a way to convert words into numerical vectors, such that words with similar meanings have similar vector values. This allows computers to better understand and work with textual data by capturing semantic relationships between words. In this example, every word will be converted into a vector with a length of
50
, meaning each word will be represented by50
floating-point numbers.
- Numerical Sequences: In scenarios such as monitoring multiple system parameters over time, 2D tensors can be employed. Consider a control system where you're observing the behavior of
5
different parameters (e.g., temperature, pressure, humidity, voltage, and current) over a span of10
hours. Each parameter has40
data points recorded every hour. Over10
hours, this aggregates to a tensor shape of(400, 5)
. In this format, the first dimension sequentially tracks the timeline (with40
data points for each of the10
hours, totaling400
), while the second dimension details the data for each of the5
parameters at every data point;
- Image Processing: Images are predominantly represented as 3D tensors. The height and width of the image create the first two dimensions, while the depth (color channels like RGB) forms the third. A colored image of
256x256
pixels would have a tensor shape of(256, 256, 3)
;Note
The last dimension has a length of 3 since each pixel in the RGB color palette is represented by three distinct values, corresponding to its color channels: Red, Green, and Blue.
- Video Processing: Videos, being sequences of images, are expressed using 4D tensors. Think of each frame as an image. So, a
60
-second video, sampled at1
frame per second, with each frame being a256x256
colored image, would be represented as a tensor of(60, 256, 256, 3)
.
Note
For a video with
30
frames every second, we would have30 * number of seconds
total frames. So, for60
seconds, that's30
frames/second multiplied by60
seconds, giving us1800
frames. This would result in a tensor shape of(1800, 256, 256, 3)
.
Understanding these shapes and the logic behind them is fundamental. By ensuring the correct tensor dimensions, we align data appropriately, laying the groundwork for effective model training and inference.
Obrigado pelo seu feedback!