data = {'X': [], 'y': []}
for root, dirs, files in os.walk("./data", topdown=False): #load all the data
for name in files:
if ("dataset" in name):
with open(os.path.join(root, name), 'r') as infile:
d = json.load(infile)
data['X'] += d['X']
data['y'] += d['y']
if (len(sys.argv) > 1
and sys.argv[1] == 'test'): #if there is the argument "test"
print("Testing network.pt...")
network = NN(len(data['X'][0]) - 1,
len(data['y'][0])) #load the network in network.pt
network.load_state_dict(torch.load("./network.pt"))
network.eval()
g_predict = test(data, network) #test the network on all the dataset
print(g_predict * 100, "%") #print the result
else: #else if there is not the argument test
print("Training a new network...")
network = NN(len(data['X'][0]) - 1,
len(data['y'][0])) #create a new network
optimizer = torch.optim.Adam(network.parameters(),
lr=1e-6) #define the optimizer
loss = nn.CrossEntropyLoss() #define the loss function
data_test = {'X': [], 'y': []}
UPDATE_TARGET_N = 10 BATCH_SIZE = 128 RUNS = 1500 STEPS = 500 R_MEMORY = ReplayMemory(10000) MIN_EPSILON = 0.04 successes = 0 GAMMA = 0.99 epsilon = 0.4 l_r = 0.001 state_0 = env.reset p_network = NN() target_network = NN() target_network.load_state_dict(p_network.state_dict()) target_network.eval() loss_function = nn.MSELoss() # mean squared error optimizer = optim.SGD(p_network.parameters(), lr=l_r) # Scheduler will adjust learning rate after every run with the factor gamma scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9) steps_history = [] # turn Experience Replay here on or off ####################################### Experience_Replay = False ############## ####################################### for run in trange(RUNS):
