cnnff.py

from scipy.signal import correlate as conv
import numpy as np
from activations import relu, softmax


def cnnff(x, net):
    ''' cnnff
    
        perform feed-forward pass for convolutional network
        
        inputs: 
            x: batch of input images (N x H x W x Cin numpy array)
            net: List structure describing the network architecture (see cnn.py for details)
        
        outputs:
            net: updated net data structure that stores outputs from each layer
    '''
    
    # set input layer
    

    # loop over layers 1...L
    for n in range(1,len(net)):
        # current input
        inp = net[n-1]['output'] 
        # current layer
        layer = net[n] 

        # if layer type is Conv
        if layer['type'] is 'Conv':
            # conv followed by activation function
            ''' *** put code here *** '''    
        # if layer type is Pool
        elif layer['type'] is 'Pool':
            ''' *** put code here *** '''
    
    return net