def b_3(plot=False):
units = [1, 2, 3, 10, 20, 40]
lrs = [0.09, 0.09, 0.1, 0.1, 0.1, 0.01]
# lrs = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
for unit, lr in zip(units, lrs):
print("\nNeural_Network")
model = Neural_Network(len(train_data[0]), [unit],
activation="sigmoid")
print(model)
model.train(train_data,
train_labels,
max_iter=10000,
eeta=lr,
batch_size=len(train_data),
threshold=1e-6,
decay=False)
pred = model.predict(train_data)
train_acc = accuracy_score(train_labels, pred) * 100
print("Train Set Accuracy: ", train_acc)
pred = model.predict(test_data)
test_acc = accuracy_score(test_labels, pred) * 100
print("Test Set Accuracy: ", test_acc)
if plot:
plot_decision_boundary(
model.predict, np.array(test_data), np.array(test_labels),
"Neural_Network Test Set\n Units in Hidden layers: %s\nAccuracy: %f"
% (str(model.hidden_layer_sizes), test_acc))
def b_2(plot=False, units=[5], eeta=0.1, threshold=1e-6):
print("\nNeural_Network")
model = Neural_Network(len(train_data[0]), units, activation="sigmoid")
print(model)
model.train(train_data,
train_labels,
max_iter=5000,
eeta=eeta,
batch_size=len(train_data),
threshold=threshold,
decay=False)
pred = model.predict(train_data)
train_acc = accuracy_score(train_labels, pred) * 100
print("Train Set Accuracy: ", train_acc)
pred = model.predict(test_data)
test_acc = accuracy_score(test_labels, pred) * 100
print("Test Set Accuracy: ", test_acc)
if plot:
plot_decision_boundary(
model.predict, np.array(train_data), np.array(train_labels),
"Neural_Network Train Set\n Units in Hidden layers: %s\nAccuracy: %f"
% (str(model.hidden_layer_sizes), train_acc))
plot_decision_boundary(
model.predict, np.array(test_data), np.array(test_labels),
"Neural_Network Test Set\n Units in Hidden layers: %s\nAccuracy: %f"
% (str(model.hidden_layer_sizes), test_acc))
def main():
#make a neural network with set architecture
arch = (2,4,1)
nn = Neural_Network(arch)
#XOR input data
X_train = np.array( [ [0,0], [0,1], [1,0], [1,1] ] )
#XOR output data
y_train = np.array( [[0],[1],[1],[0]] )
#set max iterations, learning rate, and convergence threshold
iters, lr, threshold = 5000, 1, 0.00001
#train the network
J_Hist = nn.train(X_train, y_train, alpha = lr, maxIter = iters, convergenceThreshold = threshold)
#forward propagate to get a prediction from the network
result = nn.forwardProp(X_train)
#print some nice information
print("\nUnfiltered Prediction:\n", result)
print("Final Prediction:\n", result >= 0.5, '\n')
print("Random init cost: ", round(J_Hist[0], 5), ", Final cost: ", round(J_Hist[-1], 5))
print("Cost reduction from random init: ", round(J_Hist[0] - J_Hist[-1], 5), '\n')
#set up subplots for the cost history and decision boundary
figure, plots = plt.subplots(ncols=2)
figure.suptitle('Neural Network Learning of XOR') #supertitle
figure.tight_layout(pad=2.5, w_pad=1.5, h_pad=0) #fix margins
drawCostHistory(J_Hist, plots[0])
drawDecisionBoundary(nn, plots[1], seperation_coefficient = 50, square_size = 1, allowNegatives = False)
#show the cool graphs :)
plt.show()
def c_2(plot=False, units=[100], activation="sigmoid", eeta=0.1):
print("\nNeural_Network MNIST")
model = Neural_Network(len(mnist_trd[0]), units, activation=activation)
print(model)
model.train(mnist_trd,
mnist_trl,
max_iter=300,
eeta=eeta,
batch_size=100,
decay=True,
threshold=1e-3)
pred = model.predict(mnist_trd)
train_acc = accuracy_score(mnist_trl, pred) * 100
print("Train Set Accuracy: ", train_acc)
pred = model.predict(mnist_ted)
test_acc = accuracy_score(mnist_tel, pred) * 100
print("Test Set Accuracy: ", test_acc)
class Classifier:
def __init__(self, classifier_type, **kwargs):
"""
Initializer. Classifier_type should be a string which refers
to the specific algorithm the current classifier is using.
Use keyword arguments to store parameters
specific to the algorithm being used. E.g. if you were
making a neural net with 30 input nodes, hidden layer with
10 units, and 3 output nodes your initalization might look
something like this:
neural_net = Classifier(weights = [], num_input=30, num_hidden=10, num_output=3)
Here I have the weight matrices being stored in a list called weights (initially empty).
"""
self.classifier_type = classifier_type
self.params = kwargs
"""
The kwargs you inputted just becomes a dictionary, so we can save
that dictionary to be used in other methods.
"""
def train(self, training_data):
"""
Data should be nx(m+1) numpy matrix where n is the
number of examples and m is the number of features
(recall that the first element of the vector is the label).
I recommend implementing the specific algorithms in a
seperate module and then determining which method to call
based on classifier_type. E.g. if you had a module called
neural_nets:
if self.classifier_type == 'neural_net':
import neural_nets
neural_nets.train_neural_net(self.params, training_data)
Note that your training algorithms should be modifying the parameters
so make sure that your methods are actually modifying self.params
You should print the accuracy, precision, and recall on the training data.
"""
if self.classifier_type == 'neural_network':
#change num_input, num_output based upon the data
self.nn = Neural_Network("neural_network",weights = [], num_input=self.params['num_input'], num_hidden=1000, num_output=self.params['num_output'], alt_weight=self.params['one']=='1', momentum=self.params['two']=='1')
self.nn.train(training_data)
elif self.classifier_type == 'naive_bayes':
self.nb = Naive_Bayes("naive_bayes")
self.nb.train(training_data)
elif self.classifier_type =='decision_tree':
self.dt = Decision_Tree("decision_tree", pruning=self.params['one']=='1',
info_gain_ratio=self.params['two']=='1')
self.dt.train(training_data)
def predict(self, data):
"""
Predict class of a single data vector
Data should be 1x(m+1) numpy matrix where m is the number of features
(recall that the first element of the vector is the label).
I recommend implementing the specific algorithms in a
seperate module and then determining which method to call
based on classifier_type.
This method should return the predicted label.
"""
def test(self, test_data):
"""
Data should be nx(m+1) numpy matrix where n is the
number of examples and m is the number of features
(recall that the first element of the vector is the label).
You should print the accuracy, precision, and recall on the test data.
"""
#pdb.set_trace()
#Accuracy, Recall, and Precision
relevant_and_retrieved, relevant, retrieved, total, hit = 0, 0, 0, 0, 0
for person in test_data:
predict = 0
if self.classifier_type == 'neural_network':
predict = self.nn.predict(person)
elif self.classifier_type == 'naive_bayes':
predict = self.nb.predict(person)
elif self.classifier_type == 'decision_tree':
predict = self.dt.predict(person)
if predict == person[0]:
if predict == 0:
relevant_and_retrieved += 1
hit += 1
if person[0] == 0:
relevant += 1
if predict == 0:
retrieved += 1
total += 1
accuracy = hit/float(total)
recall = relevant_and_retrieved/float(relevant)
precision = relevant_and_retrieved/float(retrieved)
print "Accuracy: ", accuracy
print "Precision ", precision
print "Recall: " , recall
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
#writing a circle of pixels to the array according to middle coordinates of mouse position
plotter=Plotter()
NN=Neural_Network()
if input("Wanna train model? y/N ") =='y':
layers = [800,400]
NN.initialize(layers)
NN.train(20)
if input("Wanna save model? Y/n ") !='n' :
NN.save_model()
elif input("Wanna load from file? Y/n ")!='n' :
NN.load_model()
else :
print("Exit")
exit()
# Fit the model
# Final evaluation of the model
from neural_network import Neural_Network
import load_data as ld
import pdb
nb = Neural_Network("neural_network",weights = [], num_input=16, num_hidden=1000, num_output=2) #neural_net = Classifier(weights = [], num_input=30, num_hidden=10, num_output=3)
data = ld.load_congress_data(.85)
#data = ld.load_iris(.75)
#data = ld.load_monks(3)
classify = nb.train(data[0])
#nb.train(iris[0])
#pdb.set_trace()
#nb.test(congress[1])
tot, hit = 0, 0
ones = 0
zeros = 0
twos = 0
for person in data[1]:
predict = nb.predict(person)
if predict == person[0]:
hit += 1
tot += 1
if predict == 1:
ones += 1
elif predict == 0:
zeros += 1
from neural_network import Neural_Network
from plotter import Plotter
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
#writing a circle of pixels to the array according to middle coordinates of mouse position
plotter = Plotter()
NN = Neural_Network()
if input("Wanna train model? y/N ") == 'y':
layers = [800, 400]
NN.initialize(layers)
NN.train(10)
if input("Wanna save model? Y/n ") != 'n':
NN.save_model()
elif input("Wanna load from file? Y/n ") != 'n':
NN.load_model()
else:
print("Exit")
exit()
# Fit the model
# Final evaluation of the model
if input("Wanna test model? y/N ") == 'y':
NN.test()