def plot2(trainf):
print("Running Test 2")
nn = NeuralNet(trainf)
#nn.evaluate(folds, epochs, learning_rate)
nn.evaluate(5, 50, 0.1)
acc1 = nn.evaluate_accuracy()
nn.clean_training_data()
nn.evaluate(10, 50, 0.1)
acc2 = nn.evaluate_accuracy()
nn.clean_training_data()
nn.evaluate(15, 50, 0.1)
acc3 = nn.evaluate_accuracy()
nn.clean_training_data()
nn.evaluate(20, 50, 0.1)
acc4 = nn.evaluate_accuracy()
nn.clean_training_data()
nn.evaluate(25, 50, 0.1)
acc5 = nn.evaluate_accuracy()
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.set_title('Accuracy vs. Folds for Neural Net')
ax1.set_xlabel('Folds')
ax1.set_ylabel('Accuracy')
y = [acc1, acc2, acc3, acc4, acc5]
x = [5, 10, 15, 20, 25]
ax1.plot(x, y, c='b', marker='o')
def neuralnet(arglist):
nn = NeuralNet(arglist[1])
folds = arglist[2]
learning_rate = arglist[3]
epochs = arglist[4]
nn.evaluate(folds, epochs, learning_rate)
nn.print_results()
def plot3(trainf):
print("Running Test 3")
nn = NeuralNet(trainf)
#nn.evaluate(folds, epochs, learning_rate)
nn.evaluate(10, 50, 0.1)
x, y = nn.evaluate_roc()
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
ax2.set_title('ROC for Neural Net')
ax2.set_xlabel('False Positive Rate')
ax2.set_ylabel('True Positive Rate')
ax2.plot(x, y, c='b', marker='o')
def plot1(trainf):
print("Running Test 1")
nn = NeuralNet(trainf)
#nn.evaluate(folds, epochs, learning_rate)
nn.evaluate(10, 25, 0.1)
acc1 = nn.evaluate_accuracy()
nn.clean_training_data()
nn.evaluate(10, 50, 0.1)
acc2 = nn.evaluate_accuracy()
nn.clean_training_data()
nn.evaluate(10, 75, 0.1)
acc3 = nn.evaluate_accuracy()
nn.clean_training_data()
nn.evaluate(10, 100, 0.1)
acc4 = nn.evaluate_accuracy()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title('Accuracy vs. Epochs for Neural Net')
ax.set_xlabel('Epochs')
ax.set_ylabel('Accuracy')
y = [acc1, acc2, acc3, acc4]
x = [25, 50, 75, 100]
ax.plot(x, y, c='b', marker='o')
