Convolutional Neural Network¶
Danger
DO NOT USE!!! NOT WORKING!!!
Initializing a Convolutional Neural Network¶
Danger
DO NOT USE!!! NOT WORKING!!!
int input_img_channels = 3;
int input_img_height = 16;
int input_img_width = 16;
int[] input_structure = int[] {input_img_channels, input_img_height, input_img_width};
int num_conv_layers = 2;
int num_conv_nodes = 100;
int filter_size = 3;
int[] conv_block = int[] {num_conv_layers, num_conv_nodes, filter_size};
int[][] conv_blocks = int[][] {conv_block, conv_block};
int num_hidden_1 = 12;
int num_hidden_2 = 9;
int num_outputs = 3;
int[] fully_connected_layer = int[] {num_hidden_1, num_hidden_2, num_outputs};
ConvolutionalNeuralNetwork cnn = new ConvolutionalNeuralNetwork(input_structure, conv_blocks, fully_connected_layer);
Feeding Data through a Convolutional Neural Network and receiving the Output¶
Danger
DO NOT USE!!! NOT WORKING!!!
float[][] red_channel_img = // load image r
float[][] green_channel_img = // load image g
float[][] blue_channel_img = // load image b
float[][][] inputs = new float[][][] {red_channel_img, green_channel_img, blue_channel_img};
float[] outputs = cnn.feedforward(inputs);
System.out.println(outputs);
// gives : [0.5345, 0.4325, 0.6578]
Training a Convolutional Neural Network¶
Danger
DO NOT USE!!! NOT WORKING!!!
float[] answers = new float[] {1, 0, 0};
cnn.train_gradient_descent(inputs, answers);
System.out.println(cnn.feedforward(inputs));
// gives : [0.98799, 0.1203, 0.0265]