Build your own Image classifier with Tensorflow and Keras
Arun Prakash
28127

Hello,

I am new to python and tensor flow , so i took your code and provide it with train images and test images of mine which comprises of pictures from 1–10

The output which i am getting is , something which is unfamiliar with your. Please help me to understand what it actually is

The output is as follows:

C:\Users\fsipl>python D:\SD\TFTrainData.py
Using TensorFlow backend.
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 61.80it/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 55.30it/s]
Epoch 1/50
8/8 [==============================] — 0s 59ms/step — loss: 5.4134 — acc: 0.3750
Epoch 2/50
8/8 [==============================] — 0s 7ms/step — loss: 4.0365 — acc: 0.7500
Epoch 3/50
8/8 [==============================] — 0s 7ms/step — loss: 6.7436 — acc: 0.5000
Epoch 4/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 5/50
8/8 [==============================] — 0s 7ms/step — loss: 6.0443 — acc: 0.6250
Epoch 6/50
8/8 [==============================] — 0s 7ms/step — loss: 6.0443 — acc: 0.6250
Epoch 7/50
8/8 [==============================] — 0s 7ms/step — loss: 13.7752 — acc: 0.1250
Epoch 8/50
8/8 [==============================] — 0s 7ms/step — loss: 4.2469 — acc: 0.6250
Epoch 9/50
8/8 [==============================] — 0s 7ms/step — loss: 6.5616 — acc: 0.5000
Epoch 10/50
8/8 [==============================] — 0s 7ms/step — loss: 9.2427 — acc: 0.3750
Epoch 11/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 12/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 13/50
8/8 [==============================] — 0s 7ms/step — loss: 7.8979 — acc: 0.5000
Epoch 14/50
8/8 [==============================] — 0s 7ms/step — loss: 8.8769 — acc: 0.3750
Epoch 15/50
8/8 [==============================] — 0s 7ms/step — loss: 10.0738 — acc: 0.3750
Epoch 16/50
8/8 [==============================] — 0s 7ms/step — loss: 12.0887 — acc: 0.2500
Epoch 17/50
8/8 [==============================] — 0s 7ms/step — loss: 10.0738 — acc: 0.3750
Epoch 18/50
8/8 [==============================] — 0s 7ms/step — loss: 7.3921 — acc: 0.5000
Epoch 19/50
8/8 [==============================] — 0s 6ms/step — loss: 9.2717 — acc: 0.2500
Epoch 20/50
8/8 [==============================] — 0s 7ms/step — loss: 6.0443 — acc: 0.6250
Epoch 21/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 22/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 23/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 24/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 25/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 26/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 27/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 28/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 29/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 30/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 31/50
8/8 [==============================] — 0s 6ms/step — loss: 8.0590 — acc: 0.5000
Epoch 32/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 33/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 34/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 35/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 36/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 37/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 38/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 39/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 40/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 41/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 42/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 43/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 44/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 45/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 46/50
8/8 [==============================] — 0s 7ms/step — loss: 8.0590 — acc: 0.5000
Epoch 47/50
8/8 [==============================] — 0s 8ms/step — loss: 8.0590 — acc: 0.5000
Epoch 48/50
8/8 [==============================] — 0s 9ms/step — loss: 8.0590 — acc: 0.5000
Epoch 49/50
8/8 [==============================] — 0s 8ms/step — loss: 8.0590 — acc: 0.5000
Epoch 50/50
8/8 [==============================] — 0s 9ms/step — loss: 8.0590 — acc: 0.5000
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 64, 64, 32) 832
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 13, 13, 50) 40050
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 3, 3, 50) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 3, 3, 80) 100080
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 1, 1, 80) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 1, 1, 80) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 80) 0
_________________________________________________________________
dense_1 (Dense) (None, 512) 41472
_________________________________________________________________
dropout_2 (Dropout) (None, 512) 0
_________________________________________________________________
dense_2 (Dense) (None, 2) 1026
=================================================================
Total params: 183,460
Trainable params: 183,460
Non-trainable params: 0
_________________________________________________________________