stackedAutoencoder.py
def stack2Params(stack):

    """ Initialize an empty list of parameters """

    params = []
    num_layers = len(stack) / 2
	
    """ For each layer in the neural network, append the corresponding parameters """
	
    for i in range(num_layers):
    
        params = numpy.concatenate((params, numpy.array(stack[i, "W"]).flatten()))
        params = numpy.concatenate((params, numpy.array(stack[i, "b"]).flatten()))
        
    return params
 Initialize an empty list of parameters 
Initialize an empty list of parameters
Initialize an empty list of parameters
def stack2Params(stack):
    params = []
    num_layers = len(stack) / 2
    for i in range(num_layers):
        params = numpy.concatenate((params, numpy.array(stack[i, "W"]).flatten()))
        params = numpy.concatenate((params, numpy.array(stack[i, "b"]).flatten()))
    return params
def stack2Params ( stack ) : params = [ ] num_layers = len ( stack ) / 2 """ For each layer in the neural network, append the corresponding parameters """ for i in range ( num_layers ) : params = numpy . concatenate ( ( params , numpy . array ( stack [ i , "W" ] ) . flatten ( ) ) ) params = numpy . concatenate ( ( params , numpy . array ( stack [ i , "b" ] ) . flatten ( ) ) ) return params
Initialize an empty list of parameters
Initialize an empty list of parameters
""" Initialize an empty list of parameters """ """ For each layer in the neural network, append the corresponding parameters """
stack