Python Network.connectLayers方法代码示例

# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import connectLayers [as 别名]
def greaterThanTest(debug=False):
    """
    Tests to see if a network can determine if x is greater than y.

    (output)
    |
    (hidden)
    |      \
    (in1)  (in2)

    """
    network = Network(2,1)
    hiddenLayer = network.createLayer(1, name="hidden", learningRate=0.1)

    network.connectLayers(network.inputLayer, hiddenLayer, interconnected=True)
    network.connectLayers(hiddenLayer, network.outputLayer, interconnected=True)

    printLayer(network.inputLayer)
    printLayer(hiddenLayer)
    printLayer(network.outputLayer)

    def genData():
        return [[random.random(), random.random()]]
    def genExpected(x):
        return [[1.0] if y[0] > y[1] else [0.0] for y in x]
    def calcDiff(x,y):
        return sum([abs(a[0]-b[0]) for a,b in zip(x,y)])

    runTest(network, genData, genExpected, calcDiff, 100, 20, 100, debug=debug)

    printLayer(network.inputLayer)
    printLayer(hiddenLayer)
    printLayer(network.outputLayer)

    print("=" * 80)

# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import connectLayers [as 别名]
def filterTest(debug=False):
    """
    Tests to see if a network can filter negative numbers using a gating mechanism.

    (output)
    |
    x - (gate)
    |   /
    (in)

    """
    network = Network(1,1)
    gateLayer = network.createLayer(1, name="gate")

    network.connectLayers(network.inputLayer, network.outputLayer, interconnected=True)
    network.connectLayers(network.inputLayer, gateLayer, interconnected=True)
    network.gateIncomingConnections(network.outputLayer, gateLayer)

    printLayer(network.inputLayer)
    printLayer(gateLayer)
    printLayer(network.outputLayer)

    def genData():
        return [[2 * (random.random() - 0.5)] for _ in xrange(20)]
    def genExpected(x):
        return [val if val[0] > 0 else [0.0] for val in x]
    def calcDiff(x,y):
        return sum([abs(a[0]-b[0]) for a,b in zip(x,y)])

    runTest(network, genData, genExpected, calcDiff, 100, 10, 200, debug=debug)

    printLayer(network.inputLayer)
    printLayer(gateLayer)
    printLayer(network.outputLayer)

    print("=" * 80)

# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import connectLayers [as 别名]
def simpleRecall():
    network = Network(5, 4)

    memoryLayer = network.createLayer(4, name="memory")
    inputGateLayer = network.createLayer(4, name="inputGate", bias=True)
    forgetGateLayer = network.createLayer(4, name="forgetGate", bias=True)
    outputGateLayer = network.createLayer(4, name="outputGate", bias=True)

    network.connectLayers(network.inputLayer, memoryLayer, interconnected=True)
    network.connectLayers(network.inputLayer, inputGateLayer, interconnected=True)
    network.connectLayers(network.inputLayer, forgetGateLayer, interconnected=True)
    network.connectLayers(network.inputLayer, outputGateLayer, interconnected=True)

    network.connectLayers(memoryLayer, inputGateLayer, recurrent=True, interconnected=True)
    network.connectLayers(memoryLayer, forgetGateLayer, recurrent=True, interconnected=True)
    network.connectLayers(memoryLayer, outputGateLayer, recurrent=True, interconnected=True)
    network.connectLayers(memoryLayer, network.outputLayer, interconnected=False)

    network.addSelfRecurrence(memoryLayer, forgetGateLayer)

    network.gateIncomingConnections(network.outputLayer, outputGateLayer)
    network.gateIncomingConnections(memoryLayer, inputGateLayer)

    printLayer(network.inputLayer)
    printLayer(inputGateLayer)
    printLayer(memoryLayer)
    printLayer(outputGateLayer)
    printLayer(network.outputLayer)

    inputSet = []

    # Create set of input sequences.
    for _ in xrange(200):
        length = 15
        # Fill 10 blank vectors.
        inputSequence = [[0.0 for _ in xrange(5)] for __ in xrange(length)]

        # Add one relevant vector.
        relevant = [0.0, 0.0, 0.0, 0.0, 0.0]
        relevant[random.randint(0, 3)] = 1.0
        inputSequence.append(relevant)

        random.shuffle(inputSequence)

        # Add prompt
        inputSequence.append([0.0, 0.0, 0.0, 0.0, 1.0])

        # Create output sequence.
        outputSequence = [[0.0] * 4] * (length + 1)
        #outputSequence = [None] * 11
        expected = [0.0, 0.0, 0.0, 0.0]
        expected[relevant.index(1.0)] = 1.0
        outputSequence.append(expected)

        inputSet.append((inputSequence, outputSequence))

    testSet = inputSet[:20]
    inputSet = inputSet[20:]

    def calcDiff(x,y):
        diff = 0.0
        zipped = zip(x,y)
        for actual,expected in zipped[:-1]:
            for a in actual:
                if a > 0.5: diff += a
        actual,expected = zipped[-1]
        for a,e in zip(actual, expected):
            if e == 1.0 and a < 0.5:
                #print(a, " != 1")
                diff += 1.0 - a
            elif e == 0.0 and a > 0.5:
                #print(a, " != 0")
                diff += a
        return diff

    beforeDiff = calculateDiff(network, testSet, calcDiff, printValues=False)
    print("Before diff: %f" % beforeDiff)

    #for _ in xrange(100):
    iterations = 0
    while True:
        print("Iteration %d" % iterations)
        iterations += 1
        for inputSequence, outputSequence in inputSet:
            #print(".")
            output = network.learn(inputSequence, outputSequence)
        diff = calculateDiff(network, testSet, calcDiff, printValues=False)
        print(diff)
        if diff < 1: break

    afterDiff = calculateDiff(network, testSet, calcDiff, printValues=False)
    print("Before diff: %f" % beforeDiff)
    print("After diff: %f" % afterDiff)

# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import connectLayers [as 别名]
def memoryTest(debug=False):
    """
    Tests to see if a network can remember a value.
    Input sequences contain one value and one signals.
        The signal indicates that the value should be let into the
            memory cell and remembered.

    (output)
    |
    (memory) )
    |
    x ---- (gate)
    |         |
    (input) (input2)

    """
    network = Network(2,1)
    gateLayer = network.createLayer(1, name="gate", activationFunction=activation.TanH)
    memoryLayer = network.createLayer(1, name="memory", activationFunction=activation.Identity)

    network.connectLayers(network.inputLayer[:1], memoryLayer)
    network.connectLayers(network.inputLayer[1:2], gateLayer)

    network.connectLayers(memoryLayer, network.outputLayer)

    network.gateIncomingConnections(memoryLayer, gateLayer)
    network.addSelfRecurrence(memoryLayer)

    printLayer(network.inputLayer)
    printLayer(memoryLayer)
    printLayer(gateLayer)
    printLayer(network.outputLayer)

    def genData():
        sequence = [[random.random() - 0.5, 0.0] for _ in xrange(20)]
        if random.random() < 0.8:
            sequence.append([random.random() - 0.5, 1.0])
        else:
            sequence.append([random.random() - 0.5, 0.0])
        random.shuffle(sequence)
        return sequence

    def genExpected(sequence):
        val = None
        index = None
        for i, s in enumerate(sequence):
            if s[1] == 1.0:
                val = s[0]
                index = i

        expected = [[0.0] for _ in xrange(len(sequence))]
        if val is not None:
            for i in xrange(len(sequence) - index):
                expected[i + index] = [val]
        return expected

    def calcDiff(x,y):
        return sum([abs(a[0]-b[0]) if b is not None else 0.0 for a,b in zip(x,y)])

    runTest(network, genData, genExpected, calcDiff, 100, 10, 100, debug=debug)

    printLayer(network.inputLayer)
    printLayer(memoryLayer)
    printLayer(gateLayer)
    printLayer(network.outputLayer)

# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import connectLayers [as 别名]
def forgetTest(debug=False):
    """
    Tests to see if a network can remember a value and then forget it.
    Input sequences contain one value and two signals.
        The first signal indicates that the value should be let into the
            memory cell and remembered.
        The second signal indicates that the previously remembered value
            should be forgotten.

    (output)
    |
    (memory) ) -- (forget)
    |                 |
    x ---- (gate)     |
    |         |       |
    (input) (input2) (input3)

    """
    network = Network(3,1)
    gateLayer = network.createLayer(1, name="gate", activationFunction=activation.TanH)
    forgetLayer = network.createLayer(1, name="forget", bias=True)
    memoryLayer = network.createLayer(1, name="memory", activationFunction=activation.Identity)

    network.connectLayers(network.inputLayer[:1], memoryLayer)
    network.connectLayers(network.inputLayer[1:2], gateLayer)
    network.connectLayers(network.inputLayer[2:3], forgetLayer)

    network.connectLayers(memoryLayer, network.outputLayer)

    network.gateIncomingConnections(memoryLayer, gateLayer)
    network.addSelfRecurrence(memoryLayer, forgetLayer)

    printLayer(network.inputLayer)
    printLayer(memoryLayer)
    printLayer(gateLayer)
    printLayer(forgetLayer)
    printLayer(network.outputLayer)

    def genData():
        length = 50
        start = random.randint(0, length-2)
        end = 0
        while end <= start: end = random.randint(1, length-1)

        sequence = [[random.random() - 0.5, 1.0 if _ == start else 0.0, 1.0 if _ == end else 0.0] for _ in xrange(length)]
        return sequence

    def genExpected(sequence):
        expected = []
        toRemember = 0.0
        for val,remember,forget in sequence:
            if remember == 1.0: toRemember = val
            if forget == 1.0: toRemember = 0.0
            expected.append([toRemember])

        return expected

    def calcDiff(x,y):
        return sum([abs(a[0]-b[0]) for a,b in zip(x,y)])

    runTest(network, genData, genExpected, calcDiff, 100, 10, 100, debug=debug)

    printLayer(network.inputLayer)
    printLayer(memoryLayer)
    printLayer(gateLayer)
    printLayer(forgetLayer)
    printLayer(network.outputLayer)

# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import connectLayers [as 别名]
def shortTermTest():
    """
    Tests forward time recurrent connections.
    This network will project the input value forward in time.
    The output neuron is responsible for comparing the current value to the
        previous value.

              (output)
                 |
         ---->(squash)      --->
        |        |         |
              (input)------
    """

    network = Network(1, 1)
    squashLayer = network.createLayer(1, "squash", activationFunction=activation.Sigmoid)

    network.connectLayers(network.inputLayer, squashLayer)
    network.connectLayers(network.inputLayer, squashLayer, recurrent=True)
    network.connectLayers(squashLayer, network.outputLayer)

    printLayer(network.inputLayer)
    printLayer(squashLayer)
    printLayer(network.outputLayer)

    training = []
    test = []

    def genData():
        sequence = []
        for _ in xrange(20):
            sequence.append([random.random()])
        return sequence

    def genExpected(sequence):
        expected = []

        previous = 0.0
        for x in sequence:
            if x[0] > previous:
                expected.append([1.0])
            else:
                expected.append([0.0])
            previous = x[0]
        return expected

    training,test = createSequences(genData, genExpected, 1000, 1000)

    trainNetwork(network, training, 100)

    passed = 0
    totalDiff = 0.0
    for input,expected in test:
        output = network.runSequence(input)
        diff = 0.0

        for actual,exp in zip(output,expected):
            for a,e in zip(actual,exp):
                if e == 1.0 and a < 0.5:
                    diff += 1.0 - a
                elif e == 0.0 and a > 0.5:
                    diff += a
        totalDiff += diff
        if diff == 0.0: passed += 1
        else:
            '''
            print(input)
            print(output)
            print(expected)
            print(diff)
            print("")
            '''

    print("Passed %d / %d" % (passed, len(test)))
    print("Diff: %f" % totalDiff)

    printLayer(network.inputLayer)
    printLayer(squashLayer)
    printLayer(network.outputLayer)

# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import connectLayers [as 别名]
def distractedRecall():
    network = Network(10, 4)

    memoryLayer = network.createLayer(8, name="memory")
    inputGateLayer = network.createLayer(8, name="inputGate", bias=True)
    forgetGateLayer = network.createLayer(8, name="forgetGate", bias=True)
    outputGateLayer = network.createLayer(8, name="outputGate", bias=True)

    network.connectLayers(network.inputLayer, memoryLayer, interconnected=True)
    network.connectLayers(network.inputLayer, inputGateLayer, interconnected=True)
    network.connectLayers(network.inputLayer, forgetGateLayer, interconnected=True)
    network.connectLayers(network.inputLayer, outputGateLayer, interconnected=True)

    network.connectLayers(memoryLayer, inputGateLayer, recurrent=True, interconnected=True)
    network.connectLayers(memoryLayer, forgetGateLayer, recurrent=True, interconnected=True)
    network.connectLayers(memoryLayer, outputGateLayer, recurrent=True, interconnected=True)
    network.connectLayers(memoryLayer, network.outputLayer, interconnected=True)

    network.addSelfRecurrence(memoryLayer, forgetGateLayer)

    network.gateOutgoingConnections(memoryLayer, outputGateLayer)
    network.gateIncomingConnections(memoryLayer, inputGateLayer)


    inputSet = []

    # Create set of input sequences.
    for _ in xrange(100):
        length = 10

        # Create input sequence.
        inputSequence = []

        # Fill random distractors.
        for _ in xrange(length):
            inputVector = [0.0 for _ in xrange(10)]
            inputVector[random.randint(4,7)] = 1
            inputSequence.append(inputVector)

        # Select two random vectors and inject random target symbols.
        firstIndex = random.randint(0,length-1)
        secondIndex = firstIndex
        while secondIndex == firstIndex: secondIndex = random.randint(0,length-1)
        if firstIndex > secondIndex:
            temp = firstIndex
            firstIndex = secondIndex
            secondIndex = temp
        firstTarget = random.randint(0,3)
        secondTarget = random.randint(0,3)
        #print(firstIndex, secondIndex)
        #print(firstTarget, secondTarget)

        inputSequence[firstIndex] = [1.0 if x == firstTarget else 0.0 for x in xrange(10)]
        inputSequence[secondIndex] = [1.0 if x == secondTarget else 0.0 for x in xrange(10)]

        # Add prompts
        inputSequence.append([1.0 if x == 8 else 0.0 for x in xrange(10)])
        inputSequence.append([1.0 if x == 9 else 0.0 for x in xrange(10)])

        outputSequence = [[0] * 4] * length
        #outputSequence = [None] * length
        outputSequence.append([1 if x == firstTarget else 0 for x in xrange(4)])
        outputSequence.append([1 if x == secondTarget else 0 for x in xrange(4)])
        #for line in inputSequence: print(line)
        #for line in outputSequence: print(line)
        #sys.exit()

        inputSet.append((inputSequence, outputSequence))

    testSet = inputSet[:10]
    inputSet = inputSet[10:]

    def calcDiff(x,y):
        diff = 0.0
        zipped = zip(x,y)
        for actual,expected in zipped[:-1]:
            for a in actual:
                if a > 0.5: diff += a
        actual,expected = zipped[-1]
        for a,e in zip(actual, expected):
            if e == 1.0 and a < 0.5:
                print(a, " != 1")
                diff += 1.0 - a
            elif e == 0 and a > 0.5:
                print(a, " != 0")
                diff += a
        return diff


    beforeDiff = calculateDiff(network, testSet, calcDiff, printValues=False)
    print("Before diff: %f" % beforeDiff)

    #for _ in xrange(10):
    iterations = 0
    while True:
        print("Iteration %d" % iterations)
        iterations += 1
        for inputSequence, outputSequence in inputSet:
            #print(".")
            output = network.learn(inputSequence, outputSequence)
#.........这里部分代码省略.........