Question

Starting with a single neuron and with single layer of neurons, generate the transfer function of a multiple layer network. Examine the inside of the artificial neural network, devise a transfer function for each layer, briefly research how a topology can be obtained, analyze recurrent neural networks, and investigate forward propagation, cost function, and backward propagation. Consider the following multilayer neural network with the transfer function for each layer: $p$ Input Sat. Linear Layer Linear Layer $\sum$ $\sum$ $\sum$ $a^1 = \text{satlin}(W^1p + b^1)$ $a^2 = \text{purelin}(Wa^2 + b^2)$ Where \texttt{satlin} is defined by: $a = 0$ $n < 0$ Saturating Linear $a = n$ $0 \le n \le 1$ \texttt{satlin} $a = 1$ $n > 1$ And \texttt{purelin} is defined as: Linear $a = n$ \texttt{purelin} Using Python and the correspondent libraries, sketch the following responses (plot the indicated variable versus p for ($-3 < p < 3$)). Given that $w_{1,1} = 2, w_{2,1} = 1, b_1 = 2, b_2 = -1, w_{1,1} = 1, w_{1,2} = -1, b_1^2 = 0$ i. $n_1^1$ ii. $a_1^1$ iii. $n_1^2$ iv. $a_1^2$ v. $n_2^1$ vi. $a_2^1$

          Starting with a single neuron and with single layer of neurons, generate the transfer function of a
multiple layer network. Examine the inside of the artificial neural network, devise a transfer
function for each layer, briefly research how a topology can be obtained, analyze recurrent neural
networks, and investigate forward propagation, cost function, and backward propagation.
Consider the following multilayer neural network with the transfer function for each layer:
$p$
Input
Sat. Linear Layer
Linear Layer
$\sum$
$\sum$
$\sum$
$a^1 = \text{satlin}(W^1p + b^1)$
$a^2 = \text{purelin}(Wa^2 + b^2)$
Where \texttt{satlin} is defined by:
$a = 0$
$n < 0$
Saturating Linear
$a = n$
$0 \le n \le 1$
\texttt{satlin}
$a = 1$
$n > 1$
And \texttt{purelin} is defined as:
Linear
$a = n$
\texttt{purelin}
Using Python and the correspondent libraries, sketch the following responses (plot the indicated
variable versus p for ($-3 < p < 3$)).
Given that
$w_{1,1} = 2, w_{2,1} = 1, b_1 = 2, b_2 = -1, w_{1,1} = 1, w_{1,2} = -1, b_1^2 = 0$
i.
$n_1^1$
ii.
$a_1^1$
iii.
$n_1^2$
iv.
$a_1^2$
v.
$n_2^1$
vi.
$a_2^1$

Starting with a single neuron and with single layer of neurons, generate the transfer function of a
multiple layer network. Examine the inside of the artificial neural network, devise a transfer
function for each layer, briefly research how a topology can be obtained, analyze recurrent neural
networks, and investigate forward propagation, cost function, and backward propagation.
Consider the following multilayer neural network with the transfer function for each layer:
p
Input
Sat. Linear Layer
Linear Layer
∑
∑
∑
a^1 = satlin(W^1p + b^1)
a^2 = purelin(Wa^2 + b^2)
Where satlin is defined by:
a = 0
n < 0
Saturating Linear
a = n
0 ≤ n ≤ 1
satlin
a = 1
n > 1
And purelin is defined as:
Linear
a = n
purelin
Using Python and the correspondent libraries, sketch the following responses (plot the indicated
variable versus p for (-3 < p < 3)).
Given that
w1,1 = 2, w2,1 = 1, b1 = 2, b2 = -1, w1,1 = 1, w1,2 = -1, b1^2 = 0
i.
n1^1
ii.
a1^1
iii.
n1^2
iv.
a1^2
v.
n2^1
vi.
a2^1

Added by Thomas A.

Question

Please give Ace some feedback