Python Jupyter Notebook with Convolutional Neural Network image classifier implemented in Keras 🖼️. It's Google Colab ready.
Check out corresponding Medium article:
Structure your data as follows:
data/
training/
class_a/
class_a01.jpg
class_a02.jpg
...
class_b/
class_b01.jpg
class_b02.jpg
...
validation/
class_a/
class_a01.jpg
class_a02.jpg
...
class_b/
class_b01.jpg
class_b02.jpg
...
For binary classifications you are good to go!
For non-binary classifications:
- add other classes to training and validation directories
- change class_mode from "binary" to "categorical"
- change loss function from "binary_crossentropy" to "categorical_crossentropy"
Dataset: Dogs vs Cats
Description: Binary classification. Two classes two distinguish - dogs and cats.
Training: 10 000 images per class
Validation: 2 500 images per class
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_5 (Conv2D) (None, 198, 198, 32) 896
_________________________________________________________________
activation_9 (Activation) (None, 198, 198, 32) 0
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 99, 99, 32) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 97, 97, 32) 9248
_________________________________________________________________
activation_10 (Activation) (None, 97, 97, 32) 0
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 48, 48, 32) 0
_________________________________________________________________
flatten_3 (Flatten) (None, 73728) 0
_________________________________________________________________
dense_5 (Dense) (None, 16) 1179664
_________________________________________________________________
activation_11 (Activation) (None, 16) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 16) 0
_________________________________________________________________
dense_6 (Dense) (None, 1) 17
_________________________________________________________________
activation_12 (Activation) (None, 1) 0
=================================================================
Total params: 1,189,825
Trainable params: 1,189,825
Non-trainable params: 0
_________________________________________________________________
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_4 (Conv2D) (None, 198, 198, 32) 896
_________________________________________________________________
activation_6 (Activation) (None, 198, 198, 32) 0
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 99, 99, 32) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 97, 97, 32) 9248
_________________________________________________________________
activation_7 (Activation) (None, 97, 97, 32) 0
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 48, 48, 32) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 46, 46, 64) 18496
_________________________________________________________________
activation_8 (Activation) (None, 46, 46, 64) 0
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 23, 23, 64) 0
_________________________________________________________________
flatten_2 (Flatten) (None, 33856) 0
_________________________________________________________________
dense_3 (Dense) (None, 64) 2166848
_________________________________________________________________
activation_9 (Activation) (None, 64) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 64) 0
_________________________________________________________________
dense_4 (Dense) (None, 1) 65
_________________________________________________________________
activation_10 (Activation) (None, 1) 0
=================================================================
Total params: 2,195,553
Trainable params: 2,195,553
Non-trainable params: 0
_________________________________________________________________
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_4 (Conv2D) (None, 198, 198, 32) 896
_________________________________________________________________
activation_6 (Activation) (None, 198, 198, 32) 0
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 99, 99, 32) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 97, 97, 64) 18496
_________________________________________________________________
activation_7 (Activation) (None, 97, 97, 64) 0
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 48, 48, 64) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 46, 46, 128) 73856
_________________________________________________________________
activation_8 (Activation) (None, 46, 46, 128) 0
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 23, 23, 128) 0
_________________________________________________________________
flatten_2 (Flatten) (None, 67712) 0
_________________________________________________________________
dense_3 (Dense) (None, 64) 4333632
_________________________________________________________________
activation_9 (Activation) (None, 64) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 64) 0
_________________________________________________________________
dense_4 (Dense) (None, 1) 65
_________________________________________________________________
activation_10 (Activation) (None, 1) 0
=================================================================
Total params: 4,426,945
Trainable params: 4,426,945
Non-trainable params: 0
_________________________________________________________________
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_7 (Conv2D) (None, 198, 198, 32) 896
_________________________________________________________________
activation_11 (Activation) (None, 198, 198, 32) 0
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 99, 99, 32) 0
_________________________________________________________________
conv2d_8 (Conv2D) (None, 97, 97, 64) 18496
_________________________________________________________________
activation_12 (Activation) (None, 97, 97, 64) 0
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 48, 48, 64) 0
_________________________________________________________________
conv2d_9 (Conv2D) (None, 46, 46, 128) 73856
_________________________________________________________________
activation_13 (Activation) (None, 46, 46, 128) 0
_________________________________________________________________
max_pooling2d_9 (MaxPooling2 (None, 23, 23, 128) 0
_________________________________________________________________
flatten_3 (Flatten) (None, 67712) 0
_________________________________________________________________
dense_5 (Dense) (None, 128) 8667264
_________________________________________________________________
activation_14 (Activation) (None, 128) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 128) 0
_________________________________________________________________
dense_6 (Dense) (None, 1) 129
_________________________________________________________________
activation_15 (Activation) (None, 1) 0
=================================================================
Total params: 8,760,641
Trainable params: 8,760,641
Non-trainable params: 0
_________________________________________________________________
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 200, 200, 32) 896
_________________________________________________________________
conv2d_2 (Conv2D) (None, 200, 200, 32) 9248
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 100, 100, 32) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 100, 100, 64) 18496
_________________________________________________________________
conv2d_4 (Conv2D) (None, 100, 100, 64) 36928
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 50, 50, 64) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 50, 50, 128) 73856
_________________________________________________________________
conv2d_6 (Conv2D) (None, 50, 50, 128) 147584
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 25, 25, 128) 0
_________________________________________________________________
conv2d_7 (Conv2D) (None, 25, 25, 256) 295168
_________________________________________________________________
conv2d_8 (Conv2D) (None, 25, 25, 256) 590080
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 12, 12, 256) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 36864) 0
_________________________________________________________________
dense_1 (Dense) (None, 256) 9437440
_________________________________________________________________
dropout_1 (Dropout) (None, 256) 0
_________________________________________________________________
dense_2 (Dense) (None, 256) 65792
_________________________________________________________________
dropout_2 (Dropout) (None, 256) 0
_________________________________________________________________
dense_3 (Dense) (None, 1) 257
_________________________________________________________________
activation_1 (Activation) (None, 1) 0
=================================================================
Total params: 10,675,745
Trainable params: 10,675,745
Non-trainable params: 0
_________________________________________________________________
Greg (Grzegorz) Surma
