# Shape Images Classification Using NN

100 triangles, 100 squares, and 100 circles in processing. each png image is 28×28 px, the images are in 3 folders labeled squares, circles, and triangles pretty straightforward

We have to find the shape that falls in its category by training the model using Neural Networks.

## Data Preprocessing

Checking the number of content inside the data by making result, image, and files variable.

`300`
`300`
```[\'shapes/triangles/drawing(3).png\',
\'shapes/triangles/drawing(16).png\',
\'shapes/triangles/drawing(71).png\',
\'shapes/triangles/drawing(68).png\',
\'shapes/triangles/drawing(75).png\']```
`300`

## Data Visualization

Visually checking the data in the form of tables

Shuffling the dataset

Assigning values to category of shapes

## Train Test Split

Splitting the data in 80–20 split

## Model Architecture

```Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
conv2d (Conv2D)              (None, 28, 28, 32)        320
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 26, 26, 32)        9248
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0
_________________________________________________________________
dropout (Dropout)            (None, 13, 13, 32)        0
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 11, 11, 64)        18496
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0
_________________________________________________________________
flatten (Flatten)            (None, 1600)              0
_________________________________________________________________
dense (Dense)                (None, 64)                102464
_________________________________________________________________
dropout_1 (Dropout)          (None, 64)                0
_________________________________________________________________
dense_1 (Dense)              (None, 3)                 195
=================================================================
Total params: 130,723
Trainable params: 130,723
Non-trainable params: 0
_________________________________________________________________```

## Training the model

```Epoch 1/100
12/12 [==============================] - 0s 17ms/step - loss: 1.1123 - accuracy: 0.3042 - val_loss: 1.0959 - val_accuracy: 0.3167
Epoch 2/100
12/12 [==============================] - 0s 5ms/step - loss: 1.0974 - accuracy: 0.3417 - val_loss: 1.0921 - val_accuracy: 0.4333
Epoch 3/100
12/12 [==============================] - 0s 4ms/step - loss: 1.0963 - accuracy: 0.3333 - val_loss: 1.0907 - val_accuracy: 0.3500
Epoch 4/100
12/12 [==============================] - 0s 5ms/step - loss: 1.0819 - accuracy: 0.4333 - val_loss: 1.0654 - val_accuracy: 0.5167
Epoch 5/100
12/12 [==============================] - 0s 4ms/step - loss: 1.0621 - accuracy: 0.4542 - val_loss: 1.0147 - val_accuracy: 0.4833
Epoch 6/100
12/12 [==============================] - 0s 6ms/step - loss: 0.9976 - accuracy: 0.5167 - val_loss: 0.9249 - val_accuracy: 0.5333
Epoch 7/100
12/12 [==============================] - 0s 6ms/step - loss: 0.9264 - accuracy: 0.5917 - val_loss: 0.8490 - val_accuracy: 0.5833
Epoch 8/100
12/12 [==============================] - 0s 5ms/step - loss: 0.8427 - accuracy: 0.5750 - val_loss: 0.7379 - val_accuracy: 0.7000
Epoch 9/100
12/12 [==============================] - 0s 5ms/step - loss: 0.7102 - accuracy: 0.7292 - val_loss: 0.6010 - val_accuracy: 0.8167
Epoch 10/100
12/12 [==============================] - 0s 4ms/step - loss: 0.5572 - accuracy: 0.7917 - val_loss: 0.4865 - val_accuracy: 0.8167
Epoch 11/100
12/12 [==============================] - 0s 5ms/step - loss: 0.5107 - accuracy: 0.8167 - val_loss: 0.4676 - val_accuracy: 0.8500
Epoch 12/100
12/12 [==============================] - 0s 4ms/step - loss: 0.3793 - accuracy: 0.8833 - val_loss: 0.4695 - val_accuracy: 0.7833
Epoch 13/100
12/12 [==============================] - 0s 4ms/step - loss: 0.3837 - accuracy: 0.8583 - val_loss: 0.3716 - val_accuracy: 0.8333
Epoch 14/100
12/12 [==============================] - 0s 4ms/step - loss: 0.3282 - accuracy: 0.8833 - val_loss: 0.3677 - val_accuracy: 0.8500
Epoch 15/100
12/12 [==============================] - 0s 5ms/step - loss: 0.2777 - accuracy: 0.9125 - val_loss: 0.3662 - val_accuracy: 0.8667
Epoch 16/100
12/12 [==============================] - 0s 4ms/step - loss: 0.2536 - accuracy: 0.9208 - val_loss: 0.3261 - val_accuracy: 0.8667
Epoch 17/100
12/12 [==============================] - 0s 3ms/step - loss: 0.2008 - accuracy: 0.9458 - val_loss: 0.3115 - val_accuracy: 0.8500
Epoch 18/100
12/12 [==============================] - 0s 6ms/step - loss: 0.1649 - accuracy: 0.9458 - val_loss: 0.3499 - val_accuracy: 0.8667
Epoch 19/100
12/12 [==============================] - 0s 5ms/step - loss: 0.1265 - accuracy: 0.9583 - val_loss: 0.3479 - val_accuracy: 0.8833
Epoch 20/100
12/12 [==============================] - 0s 4ms/step - loss: 0.1588 - accuracy: 0.9458 - val_loss: 0.3476 - val_accuracy: 0.8667
Epoch 21/100
12/12 [==============================] - 0s 3ms/step - loss: 0.1662 - accuracy: 0.9458 - val_loss: 0.3211 - val_accuracy: 0.8667
Epoch 22/100
12/12 [==============================] - 0s 3ms/step - loss: 0.1309 - accuracy: 0.9417 - val_loss: 0.3586 - val_accuracy: 0.8500
Epoch 23/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0895 - accuracy: 0.9625 - val_loss: 0.3904 - val_accuracy: 0.8500
Epoch 24/100
12/12 [==============================] - 0s 3ms/step - loss: 0.1067 - accuracy: 0.9625 - val_loss: 0.5581 - val_accuracy: 0.8167
Epoch 25/100
12/12 [==============================] - 0s 6ms/step - loss: 0.1033 - accuracy: 0.9667 - val_loss: 0.3578 - val_accuracy: 0.9000
Epoch 26/100
12/12 [==============================] - 0s 4ms/step - loss: 0.0626 - accuracy: 0.9917 - val_loss: 0.3928 - val_accuracy: 0.8833
Epoch 27/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0536 - accuracy: 0.9917 - val_loss: 0.3721 - val_accuracy: 0.9000
Epoch 28/100
12/12 [==============================] - 0s 4ms/step - loss: 0.0527 - accuracy: 0.9875 - val_loss: 0.4973 - val_accuracy: 0.8667
Epoch 29/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0808 - accuracy: 0.9708 - val_loss: 0.4575 - val_accuracy: 0.8833
Epoch 30/100
12/12 [==============================] - 0s 3ms/step - loss: 0.1053 - accuracy: 0.9667 - val_loss: 0.4273 - val_accuracy: 0.8667
Epoch 31/100
12/12 [==============================] - 0s 5ms/step - loss: 0.0559 - accuracy: 0.9958 - val_loss: 0.4082 - val_accuracy: 0.9167
Epoch 32/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0435 - accuracy: 0.9875 - val_loss: 0.5094 - val_accuracy: 0.8667
Epoch 33/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0429 - accuracy: 0.9917 - val_loss: 0.4551 - val_accuracy: 0.9167
Epoch 34/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0357 - accuracy: 0.9917 - val_loss: 0.5260 - val_accuracy: 0.8833
Epoch 35/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0365 - accuracy: 0.9958 - val_loss: 0.4955 - val_accuracy: 0.9000
Epoch 36/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0282 - accuracy: 1.0000 - val_loss: 0.3949 - val_accuracy: 0.9167
Epoch 37/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0303 - accuracy: 0.9875 - val_loss: 0.4732 - val_accuracy: 0.9167
Epoch 38/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0169 - accuracy: 1.0000 - val_loss: 0.4634 - val_accuracy: 0.9000
Epoch 39/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0188 - accuracy: 0.9958 - val_loss: 0.4746 - val_accuracy: 0.9167
Epoch 40/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0140 - accuracy: 0.9958 - val_loss: 0.5232 - val_accuracy: 0.8833
Epoch 41/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0119 - accuracy: 0.9958 - val_loss: 0.4813 - val_accuracy: 0.9167
Epoch 42/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0168 - accuracy: 0.9958 - val_loss: 0.4460 - val_accuracy: 0.9167
Epoch 43/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0163 - accuracy: 0.9958 - val_loss: 0.4505 - val_accuracy: 0.9167
Epoch 44/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0164 - accuracy: 0.9958 - val_loss: 0.4882 - val_accuracy: 0.9167
Epoch 45/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0250 - accuracy: 0.9917 - val_loss: 0.5581 - val_accuracy: 0.9167
Epoch 46/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0245 - accuracy: 0.9958 - val_loss: 0.4527 - val_accuracy: 0.9167
Epoch 47/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0253 - accuracy: 0.9917 - val_loss: 0.4838 - val_accuracy: 0.9000
Epoch 48/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0148 - accuracy: 0.9958 - val_loss: 0.4740 - val_accuracy: 0.9000
Epoch 49/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0104 - accuracy: 1.0000 - val_loss: 0.4538 - val_accuracy: 0.9167
Epoch 50/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0127 - accuracy: 0.9958 - val_loss: 0.5382 - val_accuracy: 0.9000
Epoch 51/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0139 - accuracy: 0.9958 - val_loss: 0.4694 - val_accuracy: 0.9167
Epoch 52/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0361 - accuracy: 0.9917 - val_loss: 0.4307 - val_accuracy: 0.9167
Epoch 53/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0125 - accuracy: 0.9958 - val_loss: 0.5136 - val_accuracy: 0.9167
Epoch 54/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0076 - accuracy: 1.0000 - val_loss: 0.4797 - val_accuracy: 0.9000
Epoch 55/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0231 - accuracy: 0.9875 - val_loss: 0.5369 - val_accuracy: 0.9167
Epoch 56/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0115 - accuracy: 1.0000 - val_loss: 0.5223 - val_accuracy: 0.9167
Epoch 57/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0115 - accuracy: 0.9958 - val_loss: 0.6016 - val_accuracy: 0.9000
Epoch 58/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0081 - accuracy: 1.0000 - val_loss: 0.7670 - val_accuracy: 0.8500
Epoch 59/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0194 - accuracy: 0.9958 - val_loss: 0.5915 - val_accuracy: 0.9167
Epoch 60/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.5328 - val_accuracy: 0.9000
Epoch 61/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.5192 - val_accuracy: 0.9167
Epoch 62/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0102 - accuracy: 0.9958 - val_loss: 0.5077 - val_accuracy: 0.9167
Epoch 63/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0063 - accuracy: 1.0000 - val_loss: 0.5320 - val_accuracy: 0.9000
Epoch 64/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0095 - accuracy: 0.9958 - val_loss: 0.6002 - val_accuracy: 0.8833
Epoch 65/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0232 - accuracy: 0.9958 - val_loss: 1.0127 - val_accuracy: 0.8333
Epoch 66/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0255 - accuracy: 0.9917 - val_loss: 0.5754 - val_accuracy: 0.9167
Epoch 67/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0201 - accuracy: 0.9917 - val_loss: 0.5047 - val_accuracy: 0.8833
Epoch 68/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0189 - accuracy: 0.9958 - val_loss: 0.6710 - val_accuracy: 0.8833
Epoch 69/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0140 - accuracy: 1.0000 - val_loss: 0.5965 - val_accuracy: 0.9000
Epoch 70/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0070 - accuracy: 1.0000 - val_loss: 0.5595 - val_accuracy: 0.9167
Epoch 71/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0087 - accuracy: 1.0000 - val_loss: 0.5850 - val_accuracy: 0.8833
Epoch 72/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.5876 - val_accuracy: 0.9167
Epoch 73/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0120 - accuracy: 0.9958 - val_loss: 0.6837 - val_accuracy: 0.9167
Epoch 74/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 0.6401 - val_accuracy: 0.9000
Epoch 75/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0105 - accuracy: 0.9958 - val_loss: 0.6296 - val_accuracy: 0.9000
Epoch 76/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0069 - accuracy: 1.0000 - val_loss: 0.5819 - val_accuracy: 0.9000
Epoch 77/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.5993 - val_accuracy: 0.9167
Epoch 78/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.6399 - val_accuracy: 0.9167
Epoch 79/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0117 - accuracy: 0.9958 - val_loss: 0.7028 - val_accuracy: 0.9000
Epoch 80/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0067 - accuracy: 1.0000 - val_loss: 0.6796 - val_accuracy: 0.8833
Epoch 81/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0148 - accuracy: 0.9958 - val_loss: 0.8375 - val_accuracy: 0.8667
Epoch 82/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0425 - accuracy: 0.9833 - val_loss: 1.1823 - val_accuracy: 0.8167
Epoch 83/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0220 - accuracy: 0.9958 - val_loss: 0.7019 - val_accuracy: 0.9000
Epoch 84/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.8417 - val_accuracy: 0.8667
Epoch 85/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0077 - accuracy: 1.0000 - val_loss: 0.7788 - val_accuracy: 0.8500
Epoch 86/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 0.7818 - val_accuracy: 0.9000
Epoch 87/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0123 - accuracy: 0.9958 - val_loss: 0.7559 - val_accuracy: 0.9000
Epoch 88/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0090 - accuracy: 0.9958 - val_loss: 0.7779 - val_accuracy: 0.9000
Epoch 89/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0159 - accuracy: 0.9958 - val_loss: 0.9408 - val_accuracy: 0.8500
Epoch 90/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0065 - accuracy: 1.0000 - val_loss: 0.7213 - val_accuracy: 0.9167
Epoch 91/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0096 - accuracy: 1.0000 - val_loss: 0.6934 - val_accuracy: 0.9000
Epoch 92/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0045 - accuracy: 1.0000 - val_loss: 0.7442 - val_accuracy: 0.9167
Epoch 93/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0154 - accuracy: 0.9958 - val_loss: 0.7858 - val_accuracy: 0.9167
Epoch 94/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0046 - accuracy: 1.0000 - val_loss: 0.8481 - val_accuracy: 0.9000
Epoch 95/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 0.7743 - val_accuracy: 0.9167
Epoch 96/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0138 - accuracy: 0.9958 - val_loss: 0.7438 - val_accuracy: 0.9000
Epoch 97/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 0.7356 - val_accuracy: 0.8833
Epoch 98/100
12/12 [==============================] - 0s 5ms/step - loss: 0.0086 - accuracy: 0.9958 - val_loss: 0.8468 - val_accuracy: 0.9333
Epoch 99/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0112 - accuracy: 0.9917 - val_loss: 0.8711 - val_accuracy: 0.9333
Epoch 100/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0091 - accuracy: 0.9958 - val_loss: 0.8142 - val_accuracy: 0.9167```

## Result and Prediction

`[0.23137156665325165, 0.9833333492279053]`

```array([1, 0, 1, 1, 0, 0, 1, 2, 2, 2, 1, 0, 2, 1, 2, 1, 2, 2, 0, 2, 1, 0,
1, 0, 0, 2, 1, 1, 2, 1, 1, 2, 2, 0, 0, 0, 1, 0, 1, 0, 0, 2, 1, 1,
1, 1, 0, 1, 2, 0, 2, 2, 2, 1, 2, 1, 0, 2, 0, 2])```

## Testing

`array([0., 1., 0.], dtype=float32)`

`1`

## DeepCC

```[INFO]
Reading [keras model] \'best.h5\'
[SUCCESS]
Saved \'best_deepC/best.onnx\'
[INFO]
Reading [onnx model] \'best_deepC/best.onnx\'
[INFO]
Model info:
ir_vesion : 5
doc       :
[WARNING]
[ONNX]: graph-node conv2d\'s attribute auto_pad has no meaningful data.
[WARNING]
[ONNX]: terminal (input/output) conv2d_input\'s shape is less than 1. Changing it to 1.
[WARNING]
[ONNX]: terminal (input/output) dense_1\'s shape is less than 1. Changing it to 1.
WARN (GRAPH): found operator node with the same name (dense_1) as io node.
[INFO]
Running DNNC graph sanity check ...
[SUCCESS]
Passed sanity check.
[INFO]
Writing C++ file \'best_deepC/best.cpp\'
[INFO]
deepSea model files are ready in \'best_deepC/\'
[RUNNING COMMAND]
g++ -std=c++11 -O3 -fno-rtti -fno-exceptions -I. -I/opt/tljh/user/lib/python3.7/site-packages/deepC-0.13-py3.7-linux-x86_64.egg/deepC/include -isystem /opt/tljh/user/lib/python3.7/site-packages/deepC-0.13-py3.7-linux-x86_64.egg/deepC/packages/eigen-eigen-323c052e1731 "best_deepC/best.cpp" -D_AITS_MAIN -o "best_deepC/best.exe"
[RUNNING COMMAND]
size "best_deepC/best.exe"
text	   data	    bss	    dec	    hex	filename
690061	   3784	    760	 694605	  a994d	best_deepC/best.exe
[SUCCESS]
Saved model as executable "best_deepC/best.exe"```

There we have the model which can detect the hand-drawn shape using a neural network.

Notebook Link- Here

Credits- Siddharth Ganjoo

Also Read: Lower Back Pain Symptoms Detection using NN