# print("loss= ", loss)
print("===========")
lr_schedular.step()
# save_weights(model, path)
e = Evaluation(10)
for image, label in dataloader_test:
image = image/255
predicted = model(image)
probs = softMax(predicted)
pred = np.argmax(probs,axis=0)
e.add_prediction(pred[np.newaxis],label)
print("the confusion Matrix:\n",e.get_confusion_Matrix())
print("the Mean F1 Score:\n",e.evaluate())
model1 = Model()
model1.add(Dense(784, 90))
model1.add(ReLU())
model1.add(Dense(90, 45))
model1.add(ReLU())
model1.add(Dense(45, 10))
model1.set_loss(CrossEntropyLoss())
optimizer1 = GradientDecent(model1.parameters(), learning_rate = 0.01)
epochs = 6
for epoch in range(epochs):
