本文整理汇总了Python中layer.Layer.setPrevLayer方法的典型用法代码示例。如果您正苦于以下问题：Python Layer.setPrevLayer方法的具体用法？Python Layer.setPrevLayer怎么用？Python Layer.setPrevLayer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layer.Layer的用法示例。
在下文中一共展示了Layer.setPrevLayer方法的1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: initNetwork
# 需要导入模块: from layer import Layer [as 别名]
# 或者: from layer.Layer import setPrevLayer [as 别名]
def initNetwork():
#
# Initialize neural network
# The parameter sendt in is the learningRate of ther neural network,
# in this case we set it to 0.001
#
nn = ConvolutionalNeuralNetwork(0.001)
#
# Layer 0, the input layer
#
layer0 = Layer("layer0")
# Creates the neurons in the layer0 and adds them into the layer.
for i in range(0,841):
layer0.addNeuron()
# Adds the layer into the neural network
nn.addLayer(layer0)
#
# Layer 1: Convolutional layer
# 6 feature maps. Each feature map is 13x13, and each unit in the feature map is a 5x5 convolutional kernel
# from the input layer.
# So there are 13x13x6 = 1014 neurons, (5x5+1)x6 weights
#
layer1 = Layer("layer1")
# Sets the previous layer as layer0
layer1.setPrevLayer(layer0)
# Add the neurons
for i in range(0,1014):
layer1.addNeuron()
# Add weights from layer0 to layer1
for i in range(0,156):
# Uniform random distribution
initWeight = 0.05*random.uniform(-1,1)
layer1.addWeight(initWeight)
# interconnections with previous layer: this is difficult
# The previous layer is a top-down bitmap
# image that has been padded to size 29x29
# Each neuron in this layer is connected
# to a 5x5 kernel in its feature map, which
# is also a top-down bitmap of size 13x13.
# We move the kernel by TWO pixels, i.e., we
# skip every other pixel in the input image
kernelTemplate = [0,1,2,3,4,29,30,31,32,33,58,59,60,61,62,87,88,89,90,91,116,117,118,119,120]
#Feature maps
for fm in range(0,6):
for i in range(0,13):
for j in range(0,13):
# 26 is the number of weights per featuremaps
iNumWeights = fm * 26;
# Bias weight
layer1.neurons[fm*169+j+i*13].addConnection(-10000,iNumWeights)
iNumWeights +=1
for k in range(0,25):
layer1.neurons[fm*169+j+i*13].addConnection(2*j+58*i+kernelTemplate[k],iNumWeights)
iNumWeights +=1
# Add layer to network
nn.addLayer(layer1)
#
# Layer two: This layer is a convolutional layer
# 50 feature maps. Each feature map is 5x5, and each unit in the feature maps is a 5x5 convolutional kernel of
# corresponding areas of all 6 of the previous layers, each of which is a 13x13 feature map.
# So, there are 5x5x50 = 1250 neurons, (5X5+1)x6x50 = 7800 weights
layer2 = Layer("layer2")
layer2.setPrevLayer(layer1)
# Add the neurons
for i in range(0,1250):
layer2.addNeuron()
# Add weights
for i in range(0,7800):
# Uniform random distribution
initWeight = 0.05*random.uniform(-1,1)
#.........这里部分代码省略.........