Deep Learning and Neural Networks Example

Examples of NN models. DL algorithms

Task description

Given test and train data contains images of cats and non-cats. Need to create the model, which defines a cat picture(1) and a non-cat picture(0).

Outputs:

• For two-layer network:

  (there is a bird picture)

  y = 0. It's a non-cat picture.
  
  Number of training examples: 209
  Number of testing examples: 50
  
  Each image is of size: (64, 64, 3)
  train_x_orig shape: (209, 64, 64, 3)
  train_y shape: (1, 209)
  test_x_orig shape: (50, 64, 64, 3)
  test_y shape: (1, 50)
  train_x's shape: (12288, 209)
  test_x's shape: (12288, 50)
  
  Cost after iteration 0: 0.693049735659989
  Cost after iteration 100: 0.6464320953428849
  ...                       ...
  Cost after iteration 2400: 0.04855478562877019
  
  Accuracy: 0.9999999999999998
  Accuracy: 0.72```
  
  

• For L-layer network (4-layer):

  (there is a bird picture)

  y = 0. It's a non-cat picture.
  
  Number of training examples: 209
  Number of testing examples: 50
  
  Each image is of size: (64, 64, 3)
  train_x_orig shape: (209, 64, 64, 3)
  train_y shape: (1, 209)
  test_x_orig shape: (50, 64, 64, 3)
  test_y shape: (1, 50)
  train_x's shape: (12288, 209)
  test_x's shape: (12288, 50)
  
  Cost after iteration 0: 0.771749
  Cost after iteration 100: 0.672053
  ...                       ...
  Cost after iteration 2400: 0.092878
  
  Accuracy: 0.985645933014
  Accuracy: 0.8```