NN_forwardpass_backprop

	A	B	C	D	E	F	G	H	I	J	K
1	input
2	x	y
3	1	1
4	Epoch 0
5	Forward Pass
6	layer	w1	b1	z1	w2	b2	z2
7	0	-0.00748682	-0.82304513	-0.83053195	0.5364436	-0.735939	-0.1994954
8	Activation	a1	a2
9		0	0
10	layer	w3	w5	b3	z3	w4	w6	b4	z4
11	2	-0.27234524	0.1896159	-0.06275153	-0.06275153	-0.01401001	0.56065756	0.18710935	0.18710935
12	Activation	a3	a4
13		0	0.18710935
14	layer	w7	w8	b5	yhat
15	4	-0.2136969	-0.13899273	-0.6755334	-0.7015402394
16	Loss
17	2.895239186
18	backprop: Gradients
19	dl/dyhat	a3	a4	dl/dw7	dl/dw8	dl/db5
20	-3.403080479	0	0.18710935	0	-0.6367481764	-3.403080479
21		w7	a1	a2	da3/dz3	dl/dw3	dl/dw5	dl/db3
22		-0.2136969	0	0	0	0	0	0
23		w8	a1	a2	da4/dz4	dl/dw6	dl/dw4	dl/db4
24		-0.13899273	0	0	1	0	0	0.4730034461
25		w3	w7	w4	w8	x	da3/dz3	da4/dz4	da1/dz1	dl/dw1	dl/db1
26		-0.27234524	-0.2136969	-0.01401001	-0.13899273	1	0	1	0	0	0
27		w5	w7	w6	w8	x	da3/dz3	da4/dz4	da2/dz2	dl/dw2	dl/db2
28		0.1896159	-0.2136969	0.56065756	-0.13899273	1	0	1	0	0	0
29	Epoch 1
30	lr	b4_new	w8_new	b5_new
31	0.01	0.1823793155	-0.1326252482	-0.6415025952
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