def run(num_classes,learning_rate,width,depth,mini_batch_size):
precision = accuracy = recall = f_score = np.array([])
X_train,X_test,y_train,y_test,unknown_data = dp.load_data()
X_train,X_test,y_train,y_test,unknown_data,dtype = dp.prepare_data(X_train,X_test,y_train,y_test,unknown_data)
for _ in range(1):
model = NN.Net1(num_classes,depth=depth,width=width).type(dtype)
opt = optim.SGD(params=model.parameters(),lr=learning_rate,momentum=rp.m,nesterov=True)
train_losses,test_losses = model.train_validate(X_train,y_train,X_test,y_test,opt,mini_batch_size,dtype)
model = torch.load("Models/Best_Model.pkl")
y_pred,_ = model.test(X_test)
# Calculate metrics
y_true = y_test.data.cpu().numpy()
y_pred = y_pred.data.cpu().numpy()
a,p,r,f = m.compute_metrics(y_true,y_pred)
accuracy = np.append(accuracy,a)
precision = np.append(precision,p)
recall = np.append(recall,r)
f_score = np.append(f_score,f)
accuracy = np.mean(accuracy)
precision = np.mean(precision)
recall = np.mean(recall)
f_score = np.mean(f_score)
m.show_results(accuracy,precision,recall,f_score,num_classes,train_losses,test_losses)
#g.generate_graph(model,X_train)
fw.create_data_csv(learning_rate,depth,width,mini_batch_size,rp.m,len(test_losses)-10,accuracy)
# Store unknown_data prediction
y_pred,_ = model.test(unknown_data)
fw.store_prediction(y_pred.data.cpu().numpy())
