THEFASHIONGEEK

import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread("imgs/chapter5/sudoku.png", 0)
img = cv2.blur(img, (3, 3))
kernel = [
            [0, -1, 0],
            [-1, 5, -1],
            [0, -1, 0]
]

THEFASHIONGEEK

import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread("imgs/chapter5/sudoku.png", 0)

kernel = [
            [1, 1, 1, 1, 1],     
            [1, 1, 1, 1, 1],  
            [1, 1, 1, 1, 1],  
            [1, 1, 1, 1, 1],  

THEFASHIONGEEK

import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread("imgs/chapter5/sudoku.png", 0)

kernel = [
            [1, 4, 6, 4, 1],     #row1
            [4, 16, 24, 16, 4],  #row1*4
            [6, 24, 36, 24, 6],  #row1*6
            [4, 16, 24, 16, 4],  #row1*4

THEFASHIONGEEK

import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread("imgs/chapter5/sudoku.png", 0)
kernel = [
            [1, 2, 1], #row1
            [2, 4, 2], #row1*2
            [1, 2, 1]  #row1*1
]
kernel = np.array(kernel)/16 #Normalized kernel

THEFASHIONGEEK

network.append(gtf.flatten());
network.append(gtf.dropout(drop_probability=0.2));
network.append(gtf.fully_connected(units=1024));
network.append(gtf.dropout(drop_probability=0.2));
network.append(gtf.fully_connected(units=gtf.system_dict["dataset"]["params"]["num_classes"]));

THEFASHIONGEEK

network.append(gtf.densenet_block(bottleneck_size=4, growth_rate=64, dropout=0.2));
network.append(gtf.average_pooling(kernel_size=2));
network.append(gtf.inception_a_block(pooling_branch_channels=32, pool_type="avg"));
network.append(gtf.average_pooling(kernel_size=2));
network.append(gtf.inception_c_block(channels_7x7=3, pool_type="avg"));
network.append(gtf.average_pooling(kernel_size=2));

THEFASHIONGEEK

network.append(gtf.resnet_v1_bottleneck_block(output_channels=64, stride=1, downsample=True));
network.append(gtf.average_pooling(kernel_size=2));
network.append(gtf.resnet_v2_bottleneck_block(output_channels=64, stride=1, downsample=True));
network.append(gtf.average_pooling(kernel_size=2));

THEFASHIONGEEK

network = [];
network.append(gtf.resnet_v1_block(output_channels=32, stride=1, downsample=True));
network.append(gtf.average_pooling(kernel_size=2));
network.append(gtf.resnet_v2_block(output_channels=64, stride=1, downsample=True));
network.append(gtf.average_pooling(kernel_size=2));

THEFASHIONGEEK

gtf.Training_Params(num_epochs=5,display_progress=True,display_progress_realtime=True,
                    save_intermediate_models=False,
                    save_training_logs=True)
gtf.optimizer_sgd(0.0001)
gtf.lr_fixed()
gtf.loss_softmax_crossentropy()
gtf.Train()

THEFASHIONGEEK

network.append(gtf.convolution(output_channels=16));
network.append(gtf.batch_normalization());
network.append(gtf.relu());
network.append(gtf.max_pooling());