class MLGUI:
def __init__(self, master):
self.master = master
master.title("My ML GUI")
self.network = MyNetwork()
self.label = Label(master, text="This is ML GUI")
self.label.pack()
self.load_button = Button(master, text="Load Data", command=self.load)
self.load_button.pack()
self.learn_button = Button(master, text="Teach Network", command=self.teach)
self.learn_button.pack()
self.test_button = Button(master, text="Test Network", command=self.test)
self.test_button.pack()
self.close_button = Button(master, text="Close", command=master.quit)
self.close_button.pack()
def load(self):
self.network.load_data()
print("Im loading")
def teach(self):
print("Im teaching")
self.network.learn()
def test(self):
print("Im testing")
predictions = self.network.predict()
show_image(predictions, self.network.test_labels, self.network.test_images)
# t_batch = t_train[batch_mask]
x_batch = x_train
t_batch = t_train
# Calculate Gradient
grad = network.gradient(x_batch, t_batch)
# Update Gradient
for key in ('W1', 'b1', 'aa1', 'bb1', 'W2', 'b2'):
network.params[key] -= learning_rate * grad[key]
loss = network.loss(x_batch, t_batch)
train_loss_list.append(loss)
if i % iter_per_epoch == 0:
train_acc = network.accuracy(x_train, t_train)
train_acc_list.append(train_acc)
test_acc = network.accuracy(x_test, t_test)
test_acc_list.append(test_acc)
print(train_acc, test_acc)
print(network.predict(x_train))
print(network.params['aa1'], network.params['bb1'])
plt.style.use('seaborn-whitegrid')
plt.plot(train_loss_list, label='Loss')
plt.title("Learning Result")
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.show()