Python FullConnection示例

示例#1

文件： gaitRNNmodel.py 项目： reedanders/pyNeuralNetwork

def buildNetwork(rnn_size):
   """
   Build an RNN with softmax output
   """

   net = RecurrentNeuralNetwork()

   input_layer = InputLayer(27)       # Input is 27 unit vector
   recurrent_layer = RecurrentLayer(rnn_size)
   output_layer = SoftmaxLayer(34)

   input_to_recurrent = FullConnection(input_layer.output, recurrent_layer.input)
   recurrent_to_output = FullConnection(recurrent_layer.output, output_layer.input)
   recurrent_bias = Bias(recurrent_layer.input)
   output_bias = Bias(output_layer.input) 

   target_layer = InputLayer(34)      # 34 classes!
   objective_layer = CrossEntropyObjective(output_layer.output, target_layer.output)

   net.addLayer(input_layer)
   net.addLayer(target_layer)
   net.addConnection(input_to_recurrent)
   net.addConnection(recurrent_bias)
   net.addRecurrentLayer(recurrent_layer)
   net.addConnection(recurrent_to_output)
   net.addConnection(output_bias)
   net.addLayer(output_layer)
   net.addObjective(objective_layer)

   net.setInputLayer(input_layer)
   net.setTargetLayer(target_layer)
   net.setOutputLayer(output_layer)
   net.setObjective(objective_layer)

   net.randomize()

   return net

示例#2

from nn.models.RecurrentNeuralNetwork import *
from trainers.SGD import SGDTrainer

import numpy as np

# Build a network

net = RecurrentNeuralNetwork()

inp = InputLayer(1)
rec = RecurrentLayer(5)
out = SigmoidLayer(1)

rec.zeroInitialHistoryBatch(2)

i2r = FullConnection(inp.output, rec.input)
r2o = FullConnection(rec.output, out.input)
rbias = Bias(rec.input)
obias = Bias(out.input)

tgt = InputLayer(1)
obj = MSEObjective(out.output, tgt.output)

i2r.randomize()
r2o.randomize()
rbias.randomize()
obias.randomize()
rec.recurrentConnection.randomize()

net.addLayer(inp)
net.addLayer(tgt)

示例#3

文件： tmp.py 项目： reedanders/pyNeuralNetwork

from trainers.SGD import SGDTrainer
from datasets.iris import *

dataset, targets = load_iris_data()

# Create a neural network
net = NeuralNetwork()

# Create layers
input_layer = InputLayer(4)
target_layer = InputLayer(3)
hidden_layer = TanhLayer(5)
output_layer = SoftmaxLayer(3)

# Make the connection from input to output
conn1 = FullConnection(input_layer.output, hidden_layer.input)
bias1 = Bias(hidden_layer.input)
conn2 = FullConnection(hidden_layer.output, output_layer.input)
bias2 = Bias(output_layer.input)

# Add the objective
objective = CrossEntropyObjective(output_layer.output, target_layer.output)

net.addLayer(input_layer)
net.addLayer(target_layer)
net.addConnection(conn1)
net.addConnection(bias1)
net.addLayer(hidden_layer)
net.addConnection(conn2)
net.addConnection(bias2)
net.addLayer(output_layer)

示例#4

文件： recurrentTest.py 项目： openube/pyNeuralNetwork

from nn.models.RecurrentNeuralNetwork import *
from trainers.SGD import SGDTrainer

import numpy as np

# Build a network

net = RecurrentNeuralNetwork()

inp = InputLayer(1)
rec = RecurrentLayer(5)
out = SigmoidLayer(1)

rec.zeroInitialHistoryBatch(2)

i2r = FullConnection(inp.output, rec.input)
r2o = FullConnection(rec.output, out.input)
rbias = Bias(rec.input)
obias = Bias(out.input)

tgt = InputLayer(1)
obj = MSEObjective(out.output, tgt.output)


i2r.randomize()
r2o.randomize()
rbias.randomize()
obias.randomize()
rec.recurrentConnection.randomize()

net.addLayer(inp)

示例#5

文件： historyTest.py 项目： ReedAnders/pyNeuralNetwork

from nn.components.layers.RecurrentLayer import *
from nn.components.layers.LinearLayer import *
from nn.components.layers.InputLayer import *
from nn.components.connections.FullConnection import *
from nn.components.objectives.MSEObjective import *

import numpy as np

inp = InputLayer(1)
hist = HistoryLayer(3, np.zeros((1,3)))
conn = FullConnection(inp.output, hist.input)
tgt = InputLayer(3)
obj = MSEObjective(hist.output, tgt.output)

conn.parameters[0,0] = -0.3
conn.parameters[0,1] = 0.5
conn.parameters[0,2] = -0.2

inp.setInput(np.array([[1]]))

def print_state():
   print "  inp.output: ", inp.output.value
   print "  conn.output: ", conn.getOutput()
   print "  hist.output: ", hist.output.value
   print "  obj.delta: ", obj.getDelta()
   print "  hist.delta: ", hist.input.getDelta()
   print "  Current History: "
   for i in range(len(hist.history)):
      print "    ", hist.history[i]
   print

示例#6

from nn.components.layers.RecurrentLayer import *
from nn.components.layers.LinearLayer import *
from nn.components.layers.InputLayer import *
from nn.components.connections.FullConnection import *
from nn.components.objectives.MSEObjective import *

import numpy as np

inp = InputLayer(1)
hist = HistoryLayer(3, np.zeros((1, 3)))
conn = FullConnection(inp.output, hist.input)
tgt = InputLayer(3)
obj = MSEObjective(hist.output, tgt.output)

conn.parameters[0, 0] = -0.3
conn.parameters[0, 1] = 0.5
conn.parameters[0, 2] = -0.2

inp.setInput(np.array([[1]]))


def print_state():
    print "  inp.output: ", inp.output.value
    print "  conn.output: ", conn.getOutput()
    print "  hist.output: ", hist.output.value
    print "  obj.delta: ", obj.getDelta()
    print "  hist.delta: ", hist.input.getDelta()
    print "  Current History: "
    for i in range(len(hist.history)):
        print "    ", hist.history[i]
    print