本文整理汇总了Python中neuron.Neuron.activate方法的典型用法代码示例。如果您正苦于以下问题：Python Neuron.activate方法的具体用法？Python Neuron.activate怎么用？Python Neuron.activate使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类neuron.Neuron的用法示例。
在下文中一共展示了Neuron.activate方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from neuron import Neuron [as 别名]
# 或者: from neuron.Neuron import activate [as 别名]
def main(train, test, out):
TRAIN_FILE = train
TEST_FILE = test
OUT_FILE = out
img = Image.open(TRAIN_FILE) # read double moons image from .png file
pixels = img.load() # generate pixel map
width = img.size[0]
height = img.size[1]
training_set = dict()
for i in range(width):
for j in range(height):
if pixels[i,j] == BLUE: # if pixel is blue
training_set[i,j] = BOT # set value to bottom
elif pixels[i,j] == RED: # if pixel is red
training_set[i,j] = TOP # set value to top
# create neuron with 2 input nodes
n = Neuron(2) # x-input, y-input
print "Neuron created."
# training
print "Training..."
counter = 0
while True:
errors = 0
for p in training_set:
errors += n.train_step(p, training_set[p])
counter += 1
print "====="
if errors < n.get_margin() * len(training_set):
break
print "Length of training set: " + str(len(training_set))
print "Iterations: " + str(counter)
# test cases
img = Image.open(TEST_FILE)
pixels = img.load()
width = img.size[0]
height = img.size[1]
for i in range(width):
for j in range(height):
if pixels[i,j] == BLACK:
n.set_input(0, i)
n.set_input(1, j)
n.activate()
ans = n.get_output()
if ans == TOP:
pixels[i,j] = RED
elif ans == BOT:
pixels[i,j] = BLUE
img.save(OUT_FILE, "PNG")示例2: __init__
# 需要导入模块: from neuron import Neuron [as 别名]
# 或者: from neuron.Neuron import activate [as 别名]
class Network:
"""
Represents a neural network using sigmoidal activations.
"""
def __init__(self, layerCounts, activation=sigmoid.Tanh):
"""Constructs a neural network with a set of layers.
>>> n = Network([2,3,2])
>>> len(n.neurons)
3
>>> len(n.neurons[0])
2
>>> len(n.neurons[1])
3
>>> len(n.neurons[2])
2
>>> len(n.connections)
2
>>> len(n.connections[0])
9
>>> len(n.connections[1])
8
"""
self.bias = Neuron(sigmoid.Constant)
self.neurons = []
self.connections = []
for layer in range(len(layerCounts)):
neuron_layer = []
connection_layer = []
for i in range(layerCounts[layer]):
# Input neurons shouldn't activate their input.
cur_neuron = None
if layer is 0:
# input neurons do not activate.
cur_neuron = Neuron(sigmoid.Linear)
else:
# hidden and output neurons use normal sigmoids
cur_neuron = Neuron(activation)
# for every neuron to the left
for anterior in self.neurons[layer-1]:
#create an anterior connection to CUR_NEURON
connection = Connection(anterior, cur_neuron)
connection_layer.append(connection)
# add this connection to the posterior connections
# of ANTERIOR.
anterior.posteriors.append(connection)
cur_neuron.anteriors.append(connection)
# do the same for the BIAS connection.
bias_connection = Connection(self.bias, cur_neuron)
connection_layer.append(bias_connection)
self.bias.posteriors.append(bias_connection)
# add the current neuron.
neuron_layer.append(cur_neuron)
#if connections were made
if layer != 0:
self.connections.append(connection_layer)
# append the neural layer.
self.neurons.append(neuron_layer)
def train(self, datapair, rate):
"""Trains the network with a certain learning rate on a datapair.
Returns the net error across ouytput neurons.
Assunmes input matches network size."""
inp = datapair[0]
desired = datapair[1]
self.feedforward(inp)
error = self.backpropagate(desired, rate)
return error
def feedforward(self, inputs):
""" Passes the input data through
the network and creates the output """
assert len(inputs) == len(self.neurons[0]), \
"Input vector does not match the network intut layer"
for i in range(len(inputs)):
self.neurons[0][i].feed(inputs[i])
self.bias.activate()
for layer in self.neurons:
for neuron in layer:
neuron.activate()
return [x.output for x in self.neurons[-1]]
def backpropagate(self, desired, learning_rate):
#.........这里部分代码省略.........