class NeuralNetworkClassifier:
def __init__(self, mimetype, inputUnits, outputUnits, logging = True):
"""
constructor for the neural network classifier
"""
self.outputHeader = "#nn"
self.inputUnits = inputUnits
self.outputUnits = outputUnits
self.mimetype = mimetype
self.numberOfHiddenUnits = None
self.nn = None
self.testError = None
if logging:
self.setLoggingLevel()
def setLoggingLevel(self):
"""
This function sets the logging level of the neural network
"""
logging.basicConfig(
format="%(message)s",
level=logging.INFO,
stream=sys.stdout)
def buildModel(self,numberOfLayers, listOfLayerSpecification, learningRate, numIters):
"""
This function builds the neural network layers. The input
is the number of layers and the second parameter contains thre
type of layers to be used
"""
self.numberOfHiddenUnits = numberOfLayers
layers = []
for i in range(0,numberOfLayers):
layers.append(Layer(listOfLayerSpecification[i], units = 1))
layers.append(Layer("Softmax"))
self.initializeNeuralNetwork(layers,learningRate,numIters)
def initializeNeuralNetwork(self,layers,learningRate,numIters):
"""
This will build the Neural Network and make it ready for training
"""
self.nn = Classifier(
layers= layers,
learning_rate=learningRate,
n_iter=numIters
)
def trainNetwork(self,X, Y):
"""
Training the neural network
"""
self.nn.fit(X, Y)
def validateNetwork(self,X, Y):
"""
Validate the neural network
"""
YPred = self.nn.predict(X)
print accuracy_score(Y, YPred)
def testNetwork(self,X, Y):
"""
Test the neural network
"""
YPred = self.nn.predict(X)
self.testError = 1 - accuracy_score(Y, YPred)
print accuracy_score(Y, YPred)
def dumpNeuralNetParameters(self, filename):
"""
Dump Neural Network dumpNeuralNetParameters
"""
nn_parameters = self.nn.get_parameters()
with open(filename+".nnmodel", "wb") as myfile:
myfile.write(self.outputHeader + " " + self.mimetype + " "+
str(self.inputUnits)+" "+ str(self.numberOfHiddenUnits) + " "+
str(self.outputUnits)+" "+str(self.testError)+"\n")
for i in nn_parameters:
if "hidden" in i[2]:
for j in i[0]:
with open(filename+".nnmodel", "a") as myfile:
myfile.write(str(j[0]) + "\t")
X_train = X[:train_set_size]
X_test = X[train_set_size:]
y_train = y[:train_set_size]
y_test = y[train_set_size:]
nn = Classifier(
layers = [
Layer("Rectifier", units=100),
Layer("Softmax")],
n_iter=1,
learning_rule = 'rmsprop')
#w_train = np.array((X_train.shape[0],))
#w_train = []
#w_train[y_train == 0] = 1.2
#w_train[y_train == 1] = 0.8
#nn.fit(X_train, y_train, w_train)
nn.fit(X_train, y_train)
#y_valid = nn.predict(X_valid)
nn.get_parameters()
nn.predict(X_train)
#score = nn.score(X_test, y_test)
#plt.scatter(X_train, y_train, color='black')
#plt.scatter(X_test, y_test)
#plt.show()
#plt.scatter()
#logging.basicConfig(
# format="%(message)s",
# level=logging.DEBUG,
# stream=sys.stdout)