def main():
# number of inputs
p_model = Preceptron(2)
# here is our training data (just some random points generated with a label)
points = []
num_points = 100
for _ in range(num_points):
points.append(Point())
# split the data into training and testing data
train_points, test_points = Point.split_points(points)
EPOCHS = 10
for epoch in range(EPOCHS):
# training happens here
for _ in range(200):
rand_num_indx = random.randrange(0, len(train_points)) # choose a random index for points
rand_train_point = train_points[rand_num_indx]
# inputs array
training_inputs = [rand_train_point.x, rand_train_point.y]
# where is when we adjust the weights by training the model
p_model.train(training_inputs, rand_train_point.label)
accuracy = p_model.accuracy(test_points)
print("Epoch:", epoch+1, "out of", EPOCHS, "accuracy:", accuracy)
# dont train further if accuracy is perfect
if accuracy == 1.0:
break;
# graph the lines and points of the models
graph_model(points, p_model)
def main():
model_3d = Preceptron(3)
points = []
num_points = 100
for _ in range(num_points):
points.append(Point())
# split the data into training and testing data
train_points, test_points = Point.split_points(points)
EPOCHS = 10
BATCH_SIZE = 300
for epoch in range(EPOCHS):
# training happens here
for _ in range(BATCH_SIZE):
rand_num_indx = random.randrange(len(train_points)) # choose a random index for points
rand_train_point = train_points[rand_num_indx]
# inputs array
training_inputs = [rand_train_point.x, rand_train_point.y, rand_train_point.z]
# where is when we adjust the weights by training the model
model_3d.train(training_inputs, rand_train_point.label)
accuracy = model_3d.accuracy(test_points)
print("Epoch: {:2d} out of {:2d} accuracy: {:.2f}".format(epoch+1, EPOCHS, accuracy))
if accuracy == 1.0:
break
graph_3d_points(points, model_3d)