Contents
How to build and train a convolutional neural network?
As you might image, we need to specify the characteristics of our convolutional layer for the first layer of this neural network. TensorFlow contains a built-in object designed specifically for building convolutional layer. This object lives within the layers module of keras.
How is image augmentation used in convolutional neural network?
The broader process of modifying the original data set to avoid overfitting is called image augmentation. The tool that we will use for image preprocessing is called the ImageDataGenerator class, which we imported earlier in this tutorial. This function is capable of applying a number of different transformations.
What kind of libraries are used for convolutional neural networks?
Once your Notebook is open, you can move on to importing the various libraries we’ll use in this tutorial. This convolutional neural network tutorial will make use of a number of open-source Python libraries, including NumPy and (most importantly) TensorFlow.
How to create a convolutional neural network in keras?
The transformations and their definitions (from the keras documentation) are shown below: zoom_range: Float or [lower, upper]. Range for random zoom. If a float, [lower, upper] = [1-zoom range, 1+zoom range]. horizontal_flip: Boolean. Randomly flip inputs horizontally.
How is flattening used in convolutional neural networks?
Flattening is turning our multi-dimensional input tensor input a 1-D input tensor. For example, say the final convolutional layer outputs a (28, 28, 3) tensor. Flattening this output would give the next layer an input of (2352, 1). 2352 comes from multiplying 28 X 28 X 3, which are the dimensions of the previous output tensor.
How to create a fully convolutional network in Java?
However, the input to the last layer (Softmax activation layer), after the 1×1 convolutions, must be of fixed length (number of classes). The main ingredient: GlobalMaxPooling2D () / GlobalAveragePooling2D ().
How to create a fully convolutional network in keras?
These layers in Keras convert an input of dimension (height, width, num_of_filters) to (1, 1, num_of_filters) essentially taking max or average of the values along height and width dimensions for every filter along num_of_filters dimension. The code includes dense layers (commented out) and 1×1 convolutions.