def clear_history():
Layer.clear_history()
X_train, y_train = loader.load_train()
X_test, y_test = loader.load_test()
learning_rate = 0.022
model = Network()
optimizer = Optimizer()
for _ in range(epochs):
for ex in range(len(X_train)):
model.forward(X_train[ex].T / 100)
optimizer.get_gradients(y_train[ex])
optimizer.apply_gradients(learning_rate)
Layer.clear_history()
print('trained')
correct = 0
total = 0
for ex in range(len(X_test)): # len(X_test)
model.forward(X_test[ex].T)
real = y_test[ex]
pred = Layer.operation_history[6]['result'].T
real = real.argmax(axis=1)
pred = pred.argmax(axis=1)
for i in range(len(real)):
total += 1
if real[i] == pred[i]:
correct += 1
