CNN_low_level v1.0

A low-level implementation of Convolutional neural networks (no deep learning framework).

This version is not Vectorized (therefore slower)

Methods included:

Number	function
1.	Conv2d forward
2.	Conv2d backward
3.	maxpool2d forward
4.	maxpool2d backward
5.	averagepool2d forward
5.	averagepool2d backward

Helper funcions:

Number	function
1.	zero_pad()
2.	conv_single_step()
3.	distribute_value()
4.	create_mask_from_window()

Examples

import matplotlib.pyplot as plt
from nn.layers import *

plt.rcParams["figure.figsize"] = (5.0, 4.0)
plt.rcParams["image.interpolation"] = "nearest"
plt.rcParams["image.cmap"] = "Accent"

x = np.random.randn(4, 3, 3, 2)
x_pad = zero_pad(x, 2)
print("x.shape =\n", x.shape)
print("x_pad.shape =\n", x_pad.shape)
print("x[1,1] =\n", x[1, 1])
print("x_pad[1,1] =\n", x_pad[1, 1])

fig, axarr = plt.subplots(1, 2)
axarr[0].set_title('x')
axarr[0].imshow(x[0, :, :, 0])
axarr[1].set_title('x_pad')
axarr[1].imshow(x_pad[0, :, :, 0])
plt.show()  

Future work

• Add compute cost function
  
• Modulate the functions
  
• Add a final model
  
• Add unittest for functions
  
• Vectorize
  

More detailed documentation will be uploaded later