def apply_activation_fun(data,activation="relu"):
if activation=="relu":
return A.relu(data)
elif activation == "softmax":
return A.softmax(data)
elif activation == "tanh":
return A.tanh(data)
elif activation == "softplus":
return A.softplus(data)
elif activation == "swish":
return A.swish(data)
elif activation == "sigmoid":
return A.sigmoid(data)
def feed_forward(self, layer):
"""Feeds forward the layers values"""
for i in range(len(self.bias.weights)):
layer.nodes[i].value = 0
for i in range(len(self.nodes)):
for weight in range(len(self.nodes[i].weights)):
layer.nodes[weight].value += self.nodes[i].value * self.nodes[
i].weights[weight]
for weight in range(len(self.bias.weights)):
layer.nodes[weight].value += self.bias.weights[weight]
for w in range(len(layer.nodes)):
# use tanh as our activation function
layer.nodes[w].value = Activation.tanh(layer.nodes[w].value)
