class NeuralNetwork:
def __init__(self, rng, n_in, n_out, hl):
# will contain basically a list of Hidden Layers objects.
self.layers = []
inp_size = n_in
for i in range(len(hl)):
HL = HiddenLayer(rng, inp_size, hl[i])
self.layers.append(HL)
inp_size = hl[i]
self.op = LogisticRegression(inp_size, n_out)
self.params = []
for l in self.layers:
self.params = self.params + l.params
self.params = self.params + self.op.params
# self.params = [l.params for l in self.layers]
# forward pass is here
def forward(self, x):
act = [x]
for i, l in enumerate(self.layers):
act.append(l.output(act[i]))
return act
def cost(self, x, y):
act = self.forward(x)
estimate = act[-1]
return self.op.cost(estimate, y)
def calcAccuracy(self, x, y):
act = self.forward(x)
ll = act[-1]
return self.op.calcAccuracy(ll, y)
