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MNIST

This example shows you how to train MNIST (using the layers block).

This model will compute accuracy over 1000 random test set examples every 5 steps, plotting loss and accuracy as the model is training.

Training time can be reduced by computing accuracy over fewer examples less often.

The block algorithm was created referring to this( https://github.com/tensorflow/tfjs-examples/tree/master/mnist ) source.

Tensorflow
🔢Digit
CNN
🔰Beginner
Mnist
Blocks 0
Variables 0
10 1 10 1 123 1 1 9 12.3456 45 0 3.1 64 10 50 1 100 1 100 abc {textVariable} abc {textVariable} {textVariable} abc abc アイウエオ abc ab abc 5 {listVariable} {listVariable} {listVariable} 0 {listVariable} {listVariable} {listVariable} {listVariable} {listVariable} {listVariable} , {listVariable} brabra 100 50 0 xs Hello 0 0 1 1 2 2 2 2 1 0 9 10 0 9 5 2 5 2 3 categoricalCrossentropy 8 0.2 0.3 32 3 2 192 5 1 - - 2 2 2 0.01 0.01 32 1 4 0.45 0123456789 + 1 40 3 2048 0 Hello! 0 100 200 200 2 7 pointsOfFindContour SELECT AN IMAGE FALSE 200 200 resized grayscaled adaptive_filterd 2 7 find_contoured 4 100 0.05 10 warped resized 0 0 0 100 100 10 20 255 10 #ff0000 0 2 150 message 0 0 48px serif message 30 30 20 10 50 50 30 10 10 20 20 10 20 0 0 100 100 0 0 0 0 100 100 0 0 75 DIVIDE 1 WIDTH 1 HEIGHT 1 10000 #ffffff 1 #ffffff 1 #ffffff 1 100 #ffffff 1 100 DIVIDE 1 PI 4 0 10 20 https://amazing-ai-studio.com/three/textures/land_ocean_ice.jpg 10 10 10 1 100 8 100 250 8 helvetiker_regular Hello! 100 10 #ffffff #ffffff #ffffff 0 0 0 0 0 0 0.01 renderer camera scene renderer renderer 2 camera 75 DIVIDE 1 WIDTH 1 HEIGHT 1 10000 camera position.z 1000 scene renderer scene camera TITLE program 123 abc 512 100 TITLE information Hello world! https://hooks.slack.com/services/xxx/xxx/xxx This is a text #cc0000 https://hooks.slack.com/services/xxx/xxx/xxx Training results. acc loss 90% 1% image_element_id 0000180a-0000-1000-8000-00805f9b34fb 00002a24-0000-1000-8000-00805f9b34fb Hello! 100 C:/base/**/*.png orcl scott emp empno sal orcl scott emp orcl SELECT empno, sal FROM scott.emp

Dynamic Inputs