def test_6_bools(self):
X,Y,a,b=get_data_1csv("tests/6bools.csv",1)
nn=NN([6,36,1],verbose=0,learning_rate=0.1)
nn.train(X,Y,TRIALS)
report=nn.get_report(X,Y)
self.assertEqual(report["errors"],[])
def test_3_bool_batch(self):
X,Y,a,b=get_data_1csv("tests/3bools.csv",1)
bs=100
nn=NN([3,10,1],verbose=0,learning_rate=0.1/bs)
nn.train(X,Y,TRIALS,batch_size=bs)
report=nn.get_report(X,Y)
self.assertEqual(report["errors"],[])
def test_1_hidden_6n_xor(self):
X=[[0,0],[0,1],[1,0],[1,1]]
Y=[[0],[1],[1],[0]]
nn=NN([2,6,1],verbose=0,learning_rate=0.1)
nn.train(X,Y,TRIALS)
report=nn.get_report(X,Y)
self.assertEqual(report["errors"],[])
def test_3_outputs_2_bools(self):
X,Y,a,b=get_data_1csv("tests/3outputs2bools.csv",1)
nn=NN([2,50,3],verbose=0,learning_rate=0.1)
nn.train(X,Y,TRIALS)
report=nn.get_report(X,Y)
self.assertEqual(report["errors"],[])
def test_1_hidden_2n_xor(self):
X=[[0,0],[0,1],[1,0],[1,1]]
Y=[[0],[1],[1],[0]]
nn=NN([2,2,1],verbose=0,learning_rate=0.03,final_learning_rate=0.001)
nn.train(X,Y,30000)
report=nn.get_report(X,Y)
self.assertEqual(report["errors"],[])
def test_6_bools(self):
X, Y, a, b = get_data_1csv("tests/6bools.csv", 1)
nn = NN([6, 36, 1], verbose=0, learning_rate=0.1)
nn.train(X, Y, TRIALS)
report = nn.get_report(X, Y)
self.assertEqual(report["errors"], [])
def test_1_hidden_6n_xor(self):
X = [[0, 0], [0, 1], [1, 0], [1, 1]]
Y = [[0], [1], [1], [0]]
nn = NN([2, 6, 1], verbose=0, learning_rate=0.1)
nn.train(X, Y, TRIALS)
report = nn.get_report(X, Y)
self.assertEqual(report["errors"], [])
def test_3_bool_batch(self):
X, Y, a, b = get_data_1csv("tests/3bools.csv", 1)
bs = 100
nn = NN([3, 10, 1], verbose=0, learning_rate=0.1 / bs)
nn.train(X, Y, TRIALS, batch_size=bs)
report = nn.get_report(X, Y)
self.assertEqual(report["errors"], [])
def test_3_outputs_2_bools(self):
X, Y, a, b = get_data_1csv("tests/3outputs2bools.csv", 1)
nn = NN([2, 50, 3], verbose=0, learning_rate=0.1)
nn.train(X, Y, TRIALS)
report = nn.get_report(X, Y)
self.assertEqual(report["errors"], [])
def test_1_hidden_2n_xor(self):
X = [[0, 0], [0, 1], [1, 0], [1, 1]]
Y = [[0], [1], [1], [0]]
nn = NN([2, 2, 1],
verbose=0,
learning_rate=0.03,
final_learning_rate=0.001)
nn.train(X, Y, 30000)
report = nn.get_report(X, Y)
self.assertEqual(report["errors"], [])
def main(args):
if not args["--random"]:
random.seed(123)
np.random.seed(123)
train_csv = args["<train-csv>"]
if not is_file(train_csv):
return
prediction_csv = args["<prediction-csv>"]
if not is_file(prediction_csv):
return
target = args["<target-csv>"]
if target and not is_file(target):
return
try:
trials = int(args["--trials"])
except ValueError:
print_color("Bad value for trials.", COLORS.RED)
return
try:
batch_size = int(args["--batch"])
except ValueError:
print_color("Bad value for batch.", COLORS.RED)
return
try:
learn_rate = float(args["--learn-rate"])
except ValueError:
print_color("Bad value for learn rate.", COLORS.RED)
return
try:
final_learn_rate = float(args["--final-learn-rate"])
except ValueError:
print_color("Bad value for final learn rate.", COLORS.RED)
return
try:
interval = int(args["--timer"])
except ValueError:
print_color("Bad value for timer interval.", COLORS.RED)
return
try:
sizes = [int(i) for i in args["--sizes"].split(",")]
except ValueError:
print_color("Bad value for sizes.", COLORS.RED)
return
try:
validation_ratio = float(args["--validation-ratio"])
except ValueError:
print_color("Bad value for validation ratio.", COLORS.RED)
return
print_color("Opening file: %s" % train_csv, COLORS.YELLOW)
X_train, Y_train, X_valid, Y_valid = get_data_2csv(
train_csv,
prediction_csv,
validation_ratio,
normalize=args["--normalize"])
if validation_ratio == 1 and args["--validate"]:
X_valid, Y_valid = X_train, Y_train
if sizes[0] != len(X_train[0]):
print_color(
"Bad 'sizes' parameter for this input data. sizes[0]=%s len(X[0])=%s"
% (sizes[0], len(X_train[0])), COLORS.RED)
return
start_time = time.time()
print_color("Initializing neural net.", COLORS.GREEN)
nn = NeuralNet(sizes,
learning_rate=learn_rate,
final_learning_rate=final_learn_rate,
verbose=args["--verbose"],
timer_interval=interval,
logging=args["--logging"])
nn.train(X_train, Y_train, trials, batch_size=batch_size)
report = 0
if args["--validate"]:
print_color("Starting validation.", COLORS.GREEN)
report = nn.show_report(X_train, Y_train, X_valid, Y_valid)
if args["--report"]:
if not report:
report = nn.get_report(X_train, Y_train, X_valid, Y_valid)
report["validation ratio"] = validation_ratio
report["normalized"] = args["--normalize"]
report["random"] = args["--random"]
report["duration"] = time.time() - start_time
save_report(report)
if target:
raise NotImplementedError
print_color("Making predictions.", COLORS.GREEN)
nn.make_predictions_csv(target)
print_color("Done after %s seconds." % round(time.time() - start_time, 1),
COLORS.GREEN)
def main(args):
if not args["--random"]:
random.seed(123)
np.random.seed(123)
train_csv=args["<train-csv>"]
if not is_file(train_csv):
return
prediction_csv=args["<prediction-csv>"]
if not is_file(prediction_csv):
return
target=args["<target-csv>"]
if target and not is_file(target):
return
try:
trials=int(args["--trials"])
except ValueError:
print_color("Bad value for trials.",COLORS.RED)
return
try:
batch_size=int(args["--batch"])
except ValueError:
print_color("Bad value for batch.",COLORS.RED)
return
try:
learn_rate=float(args["--learn-rate"])
except ValueError:
print_color("Bad value for learn rate.",COLORS.RED)
return
try:
final_learn_rate=float(args["--final-learn-rate"])
except ValueError:
print_color("Bad value for final learn rate.",COLORS.RED)
return
try:
interval=int(args["--timer"])
except ValueError:
print_color("Bad value for timer interval.",COLORS.RED)
return
try:
sizes=[int(i) for i in args["--sizes"].split(",")]
except ValueError:
print_color("Bad value for sizes.",COLORS.RED)
return
try:
validation_ratio=float(args["--validation-ratio"])
except ValueError:
print_color("Bad value for validation ratio.",COLORS.RED)
return
print_color("Opening file: %s"%train_csv,COLORS.YELLOW)
X_train,Y_train,X_valid,Y_valid=get_data_2csv(train_csv,prediction_csv,
validation_ratio,normalize=args["--normalize"])
if validation_ratio==1 and args["--validate"]:
X_valid,Y_valid=X_train,Y_train
if sizes[0]!=len(X_train[0]):
print_color("Bad 'sizes' parameter for this input data. sizes[0]=%s len(X[0])=%s"%(sizes[0],len(X_train[0])),COLORS.RED)
return
start_time=time.time()
print_color("Initializing neural net.",COLORS.GREEN)
nn=NeuralNet(sizes,learning_rate=learn_rate,final_learning_rate=final_learn_rate,
verbose=args["--verbose"],timer_interval=interval,
logging=args["--logging"])
nn.train(X_train,Y_train,trials,batch_size=batch_size)
report=0
if args["--validate"]:
print_color("Starting validation.",COLORS.GREEN)
report=nn.show_report(X_train,Y_train,X_valid,Y_valid)
if args["--report"]:
if not report:
report=nn.get_report(X_train,Y_train,X_valid,Y_valid)
report["validation ratio"]=validation_ratio
report["normalized"]=args["--normalize"]
report["random"]=args["--random"]
report["duration"]=time.time()-start_time
save_report(report)
if target:
raise NotImplementedError
print_color("Making predictions.",COLORS.GREEN)
nn.make_predictions_csv(target)
print_color("Done after %s seconds."%round(time.time()-start_time,1),COLORS.GREEN)
