def forwardPropagate(self, inputValue, output):
outputVec = 1 * (self.labelRange == output)
#propagate to the hidden layer, bias corresponds to a unit with constant ouput 1
self.hiddenLayer.inputValue = inputValue
self.hiddenLayer.linearOutput = np.dot(self.hiddenLayer.inputValue,
self.hiddenLayer.W)
self.hiddenLayer.y = self.hiddenLayer.activation(
self.hiddenLayer.linearOutput)
self.hiddenLayer.y[0] = 1
self.hiddenLayer.yPrime = self.hiddenLayer.activationGradient(
self.hiddenLayer.linearOutput)
self.hiddenLayer.yPrime[0] = 0
#propagate to the top layer, bias corresponds to a unit with constant ouput 1
self.softmaxTopLayer.inputValue = self.hiddenLayer.y
self.softmaxTopLayer.linearOutput = np.dot(
self.softmaxTopLayer.inputValue, self.softmaxTopLayer.W)
self.softmaxTopLayer.y = SoftMaxFunc(self.softmaxTopLayer.linearOutput)
self.softmaxTopLayer.predict = np.argmax(self.softmaxTopLayer.y)
self.softmaxTopLayer.delta = outputVec - self.softmaxTopLayer.y
self.softmaxTopLayer.weightGradient = self.softmaxTopLayer.weightGradient - \
np.outer(self.softmaxTopLayer.inputValue,self.softmaxTopLayer.delta)
def forward(self, inputValue, output):
t = self.t
self.x_t[t, :] = inputValue
self.preHidden[t, :] = self.hiddenLayer.preHidden
#propagate to the hidden layer, bias corresponds to a unit with constant ouput 1
#(3.30)
linearOutput = np.dot(inputValue, self.hiddenLayer.W_ih) \
+ np.dot(self.hiddenLayer.preHidden, self.hiddenLayer.W_hh) \
+ self.hiddenLayer.bias
self.a_h_t[t, :] = linearOutput
#(3.31)
y = self.hiddenLayer.activation(linearOutput)
self.b_h_t[t, :] = y
self.hiddenLayer.preHidden = y
#propagate to the top layer
linearOutput = np.dot(y, self.softmaxLayer.W) + \
self.softmaxLayer.bias
y = SoftMaxFunc(linearOutput)
#predict = np.argmax(y)
delta = output - y
self.delta_k_t[t, :] = delta
t = t + 1
self.t = t
if (t == self.T):
self.backward()
self.t = 0
def onePass(self, preHidden, inputValue):
linearOutput = np.dot(inputValue, self.hiddenLayer.W_ih) \
+ np.dot(preHidden, self.hiddenLayer.W_hh) \
+ self.hiddenLayer.bias
y = self.hiddenLayer.activation(linearOutput)
preHidden = y
linearOutput = np.dot(y, self.softmaxLayer.W) + \
self.softmaxLayer.bias
y = SoftMaxFunc(linearOutput)
return (y, preHidden)
def __init__(self, rng, n_in, n_out):
self.W = np.asarray(
rng.uniform(low=-np.sqrt(6.0 / (n_in + n_out)),
high=np.sqrt(6.0 / (n_in + n_out)),
size=(n_in, n_out)))
#W(t) - W(t - 1)
self.deltaW = np.zeros((n_in, n_out))
self.delta = np.zeros(n_out)
#\partial (E) / \partial (W)
self.weightGradient = np.zeros((n_in, n_out))
self.inputValue = np.zeros(n_in)
self.y = SoftMaxFunc(np.dot(self.inputValue, self.W))
self.predict = np.argmax(self.y)
self.params = self.W
def accuracy(self, INPUT, OUTPUT):
count = 0
for inputValue, output in zip(INPUT, OUTPUT):
linearOutputHidden = np.dot(inputValue, self.hiddenLayer.W)
yHidden = self.hiddenLayer.activation(linearOutputHidden)
yHidden[0] = 1
linearOutput = np.dot(yHidden, self.softmaxTopLayer.W)
y = SoftMaxFunc(linearOutput)
predict = np.argmax(y)
if predict == output:
count = count + 1
return (float(count) / len(OUTPUT))