![how to use keras data augmentation how to use keras data augmentation](https://stepup.ai/content/images/size/w300/2020/07/grid6x5.jpeg)
- HOW TO USE KERAS DATA AUGMENTATION GENERATOR
- HOW TO USE KERAS DATA AUGMENTATION SOFTWARE
- HOW TO USE KERAS DATA AUGMENTATION CODE
Following the optimization stability considerations (get rid of vanishing or saturating values problems) we might want to normalize the dataset such that the mean value of each data sample would be equal to 0.įor that purpose, we need to calculate the mean value across one whole sample and subtract it from each number in it. So, one sample is a 3D array of numbers from 0 to 255. We’ve already seen that raw data values are from 0 to 255. The second one sets the color_mode option to “ grayscale” (while the default value is “ RGB”). Here, we create only one instance of the class but two different generators are taken from it.
HOW TO USE KERAS DATA AUGMENTATION GENERATOR
Let’s run that step in Keras with the same generator class. Nowadays, all these operations are learned through convolutional neural networks (CNN), but grayscaling as a preprocessing step might still be useful. This pair of transformations can throw away noisy pixels and detect shapes in the picture. Conventional image processing might have used grayscaling in combination with consequent thresholding. GrayscalingĪnother type of transformation that might be useful is grayscaling, which turns a color RGB image into images with only shades of gray representing colors. As one can see, raw arrays differ exactly by a factor of 255. Let’s take a look at the raw data, which are arrays. Visually both images are identical, but that’s just because Python* tools rescale images automatically to the default range to be able to display them.
![how to use keras data augmentation how to use keras data augmentation](https://i.ytimg.com/vi/k4TqxHteJ7s/maxresdefault.jpg)
Finally, let’s get a sample from the generator and see the results. Moreover, the ImageDataGenerator class allows us to stream the data directly from the hard drive directory using the flow_from_directory method.Īll the parameters can be found in the documentation, but the main parameters are the path to stream from and the target size of the image (the generator would just crop or pad the image if it doesn’t fit the target size). We just need to specify the scaling constant. Let’s create two instances of this class: one for transformed data and one for the initial (or default). Here and below we will use the Keras ImageDataGenerator class, which allows us to do all transformations on the fly. In deep neural networks you might want to restrict your input to the range from 0 to 1, due to possible overflow, optimization, stability issues, and so on.įor example, let’s cast our data from range to range. Rescaling is an operation that moves your data from one numerical range to another by simple division using a predefined constant. Each number is usually stored as an 8-bit unsigned integer type (0 to 255). Thus, every pixel is encoded through three numbers. One dimension is for channels (red, green, and blue colors) and two other dimensions are spatial dimension. In this format the image is represented as a three-dimensional (or three-channel) array.įigure 3: RGB decomposition of the image. The images are usually stored in an RGB (Red Green Blue) format.
HOW TO USE KERAS DATA AUGMENTATION CODE
Now, let’s take a look at the set of possible transformations that are usually applied for cleaning up the data, their implementation, and influence on images.Īll the code snippets can be found in the Preprocessing.ipynb notebook.
![how to use keras data augmentation how to use keras data augmentation](https://www.datasciencelearner.com/wp-content/uploads/2020/09/Brightness_range-Keras-featured-image.png)
Let’s take a look at some examples.įigure 2. It contains about 1,500 examples of images divided into two classes-positive and negative.
![how to use keras data augmentation how to use keras data augmentation](https://i.ytimg.com/vi/IrWse922-QI/maxresdefault.jpg)
In this and following articles we will use the image sentiment analysis dataset. All the materials including corresponding code, notebook, and Dockerfile* are located on Google Drive*. Let’s take a look at some of the possible preprocessing transformations and see how they can be implemented via Keras*. Transformations to apply are usually chosen randomly from the predefined set. By doing this one can increase the effective size of the dataset. It is the process of transforming each data sample in numerous possible ways and adding all of the augmented samples to the dataset. The data augmentation approach is useful in solving this problem. Sometimes small datasets are not enough for the deep model to learn sufficiently well. To make the learning process easier for the model, we can remove the artifacts using preprocessing. Let’s assume that you have some artifacts in the images. Generally, there are two occasions when one might want to do preprocessing: Preprocessing is the general term for all the transformation done to the data before feeding them into the model, including centering, normalization, shift, rotation, shear, and so on.
HOW TO USE KERAS DATA AUGMENTATION SOFTWARE
A Tutorial Series for Software Developers, Data Scientists, and Data Center Managers