def train(self, dataset):
#datasets = dataset.split_dataset(0.7)
self.training_set = dataset
#self.validation_set = datasets[1]
#while True:
for i in range(self.iterations):
predictions = []
for j in range(self.training_set.data.__len__()):
prediction = self.__feed_forward(self.training_set.data[j])
predictions.append(prediction)
#print("PREDICTION: " + str(prediction))
#print("REAL: " + str(self.training_set.target[j]))
self.__set_targets(self.training_set.target[j])
if prediction != self.training_set.target[j]:
#print("Bad prediction")
self.__back_propagate()
accuracy = get_accuracy(predictions, self.training_set.target)
self.accuracies.append(accuracy)
#print("Current Accuracy: " + str(accuracy) + "%")
self.training_set.randomize()
def train(self, dataset):
#datasets = dataset.split_dataset(0.7)
self.training_set = dataset
#self.validation_set = datasets[1]
#while True:
for i in range(self.iterations):
predictions = []
for j in range(self.training_set.data.__len__()):
prediction = self.__feed_forward(self.training_set.data[j])
predictions.append(prediction)
#print("PREDICTION: " + str(prediction))
#print("REAL: " + str(self.training_set.target[j]))
self.__set_targets(self.training_set.target[j])
if prediction != self.training_set.target[j]:
#print("Bad prediction")
self.__back_propagate()
accuracy = get_accuracy(predictions, self.training_set.target)
self.accuracies.append(accuracy)
#print("Current Accuracy: " + str(accuracy) + "%")
self.training_set.randomize()
def main():
while True:
data_set_name = input(
"Please provide the name of the data set you want to work with: ")
# Load, Randomize, Normalize, Discretize Dataset
data_set = Dataset()
data_set.read_file_into_dataset(
"C:\\Users\\Grant\\Documents\\School\\Winter 2016\\CS 450\\Neural\\"
+ data_set_name)
data_set.randomize()
data_set.data = normalize(data_set.data)
#data_set.discretize()
#print(data_set.data)
data_set.set_missing_data()
# Split Dataset
split_percentage = 0.7
data_sets = data_set.split_dataset(split_percentage)
training_set = data_sets[0]
testing_set = data_sets[1]
# Create Custom Classifier, Train Dataset, Predict Target From Testing Set
iterations = int(input("How many iterations do you want to do? "))
layers = int(
input("How many layers do you want in your neural network? "))
num_nodes = []
for i in range(layers):
if i + 1 == layers:
number = int(input("How many nodes on the output layer? "))
else:
number = int(
input("How many nodes on the " + str(i) + " layer? "))
num_nodes.append(number)
neuralNetwork = NeuralNetwork(iterations)
neuralNetwork.create_layered_network(
num_nodes, training_set.feature_names.__len__())
#neuralNetwork.display_network()
neuralNetwork.train(training_set)
predictions = neuralNetwork.predict(testing_set)
# Check Results
my_accuracy = get_accuracy(predictions, testing_set.target)
print("Accuracy: " + str(my_accuracy) + "%")
# Compare To Existing Implementations
layers_objs = []
for i in range(layers):
if i + 1 == layers:
layers_objs.append(Layer("Softmax", units=num_nodes[i]))
else:
layers_objs.append(Layer("Sigmoid", units=num_nodes[i]))
mlp_nn = Classifier(layers=layers_objs,
learning_rate=0.4,
n_iter=iterations)
mlp_nn.fit(np.array(training_set.data), np.array(training_set.target))
predictions = mlp_nn.predict(np.array(testing_set.data))
mlp_nn_accuracy = get_accuracy(predictions, testing_set.target)
print("NN Accuracy: " + str(mlp_nn_accuracy) + "%")
create_csv_file(
neuralNetwork.accuracies,
"C:\\Users\\Grant\\Documents\\School\\Winter 2016\\CS 450\\Neural\\"
+ data_set_name + ".csv")
# Do another or not
toContinue = False
while True:
another = input("Do you want to examine another dataset? (y / n) ")
if another != 'y' and another != 'n':
print("Please provide you answer in a 'y' or 'n' format.")
elif another == 'y':
toContinue = True
break
else:
toContinue = False
break
if not toContinue:
break
def main():
while True:
data_set_name = input("Please provide the name of the data set you want to work with: ")
# Load, Randomize, Normalize, Discretize Dataset
data_set = Dataset()
data_set.read_file_into_dataset("C:\\Users\\Grant\\Documents\\School\\Winter 2016\\CS 450\\Neural\\" + data_set_name)
data_set.randomize()
data_set.data = normalize(data_set.data)
#data_set.discretize()
#print(data_set.data)
data_set.set_missing_data()
# Split Dataset
split_percentage = 0.7
data_sets = data_set.split_dataset(split_percentage)
training_set = data_sets[0]
testing_set = data_sets[1]
# Create Custom Classifier, Train Dataset, Predict Target From Testing Set
iterations = int(input("How many iterations do you want to do? "))
layers = int(input("How many layers do you want in your neural network? "))
num_nodes = []
for i in range(layers):
if i + 1 == layers:
number = int(input("How many nodes on the output layer? "))
else:
number = int(input("How many nodes on the " + str(i) + " layer? "))
num_nodes.append(number)
neuralNetwork = NeuralNetwork(iterations)
neuralNetwork.create_layered_network(num_nodes, training_set.feature_names.__len__())
#neuralNetwork.display_network()
neuralNetwork.train(training_set)
predictions = neuralNetwork.predict(testing_set)
# Check Results
my_accuracy = get_accuracy(predictions, testing_set.target)
print("Accuracy: " + str(my_accuracy) + "%")
# Compare To Existing Implementations
layers_objs = []
for i in range(layers):
if i + 1 == layers:
layers_objs.append(Layer("Softmax", units=num_nodes[i]))
else:
layers_objs.append(Layer("Sigmoid", units=num_nodes[i]))
mlp_nn = Classifier(layers=layers_objs, learning_rate=0.4, n_iter=iterations)
mlp_nn.fit(np.array(training_set.data), np.array(training_set.target))
predictions = mlp_nn.predict(np.array(testing_set.data))
mlp_nn_accuracy = get_accuracy(predictions, testing_set.target)
print("NN Accuracy: " + str(mlp_nn_accuracy) + "%")
create_csv_file(neuralNetwork.accuracies, "C:\\Users\\Grant\\Documents\\School\\Winter 2016\\CS 450\\Neural\\" + data_set_name + ".csv")
# Do another or not
toContinue = False
while True:
another = input("Do you want to examine another dataset? (y / n) ")
if another != 'y' and another != 'n':
print("Please provide you answer in a 'y' or 'n' format.")
elif another == 'y':
toContinue = True
break
else:
toContinue = False
break
if not toContinue:
break
