def SIN(max_epochs, learning_rate, no_hidden):
np.random.seed(213)
inputs = []
outputs = []
for i in range(0, 200):
four_inputs_vector = list(np.random.uniform(-1.0, 1.0, 4))
four_inputs_vector = [float(four_inputs_vector[0]),float(four_inputs_vector[1]),
float(four_inputs_vector[2]),float(four_inputs_vector[3])]
inputs.append(four_inputs_vector)
inputs=np.array(inputs)
for i in range(200):
outputs.append(np.sin([inputs[i][0] - inputs[i][1] + inputs[i][2] - inputs[i][3]]))
no_in = 4
no_out = 1
NN = MLP(no_in, no_hidden, no_out)
NN.randomise()
print('\nMax Epoch:\n' + str(max_epochs), file=log)
print('\nLearning Rate:\n' + str(learning_rate), file=log)
print('\nBefore Training:\n', file=log)
for i in range(150):
NN.forward(inputs[i],'tanh')
print('Target:\t{}\t Output:\t {}'.format(str(outputs[i]),str(NN.O)), file=log)
print('Training:\n', file=log)
# training process
for i in range(0, max_epochs):
error = 0
NN.forward(inputs[:150],'tanh')
error = NN.backward(inputs[:150], outputs[:150],'tanh')
NN.updateWeights(learning_rate)
#prints error every 5% of epochs
if (i + 1) % (max_epochs / 20) == 0:
print(' Error at Epoch:\t' + str(i + 1) + '\t is \t' + str(error), file=log)
difference=float(0)
print('\n Testing :\n', file=log)
for i in range(150, len(inputs)):
NN.forward(inputs[i], 'tanh')
print('Target:\t{}\t Output:\t {}'.format(str(outputs[i]), str(NN.O)), file=log)
difference+=np.abs(outputs[i][0]-NN.O[0])
accuracy=1-(difference/50)
accuracylist.append(accuracy)
print('\nAccuracy:{}'.format(accuracy),file=log)
print('\ntestError:{}'.format(difference/50),file=log)
def XOR(max_epochs, learning_rate):
np.random.seed(1)
inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
outputs = np.array([[0], [1], [1], [0]])
NI = 2
NH = 4
NO = 1
NN = MLP(NI, NH, NO)
NN.randomise()
print('\nMax Epoch:\n' + str(max_epochs), file=log)
print('\nLearning Rate:\n' + str(learning_rate), file=log)
print('\nBefore Training:\n', file=log)
for i in range(len(inputs)):
NN.forward(inputs[i], 'sigmoid')
print('Target:\t {} Output:\t {}'.format(str(outputs[i]), str(NN.O)),
file=log)
print('\nTraining:\n', file=log)
for i in range(0, max_epochs):
NN.forward(inputs, 'sigmoid')
error = NN.backward(inputs, outputs, 'sigmoid')
NN.updateWeights(learning_rate)
if (i + 1) % (max_epochs / 20) == 0:
print(' Error at Epoch:\t' + str(i + 1) + '\t\t is \t\t' +
str(error),
file=log)
print('\n After Training :\n', file=log)
accuracy = float(0)
for i in range(len(inputs)):
NN.forward(inputs[i], 'sigmoid')
print('Target:\t {} Output:\t {}'.format(str(outputs[i]), str(NN.O)),
file=log)
if (outputs[i][0] == 0):
accuracy += 1 - NN.O[0]
elif (outputs[i][0] == 1):
accuracy += NN.O[0]
print('\nAccuracy:{}'.format(accuracy / 4), file=log)
class RL_Agent():
def __init__(self, episode_size=150):
self.model = MLP((SCREEN_HEIGHT_g, SCREEN_WIDTH_g), 300)
#self.load("models/model_1185.npz")
self.activations = []
self.frames = []
self.states_alive = []
self.episode_size = episode_size
self.episode_decisions = np.zeros((8))
self.episodes_wins = 0
self.episodes_nb = 0
self.iter = 0
def explore_exploit(self, prediction):
choice = np.random.rand()
if choice <= .8:
return prediction
return one_hot(round(np.random.rand() * 7))
def update(self, frame, is_dead):
frame = preprocessing(frame)
act_h, y_pred = self.model.forward_keep_activations(frame)
y_pred = self.explore_exploit(y_pred)
self.activations.append((act_h, y_pred))
self.frames.append(frame)
#print(y_pred)
self.episode_decisions += one_hot(y_pred)
self.iter += 1
if is_dead or self.iter == self.episode_size:
for activation, frame in zip(self.activations, self.frames):
if is_dead: # Agent is dead
#y_true = np.array([0, 0, 0, 0.5, 0.5, 0, 0, 0])
y_true = wrong_move(one_hot(activation[1]))
#print("\n", activation[1],'\n', y_true, '\n\n')
grads = self.model.gradients(frame, activation[0],
activation[1], y_true)
self.model.backward(grads)
else:
y_true = one_hot(activation[1])
grads = self.model.gradients(frame, activation[0],
activation[1], y_true)
self.model.backward(grads)
print(
f"Episode #{self.episodes_nb} : {'LOSE' if is_dead else 'WIN'} Decisions : {self.episode_decisions}"
)
self.reset()
self.episodes_nb += 1
self.episode_decisions.fill(0)
if not is_dead:
self.episodes_wins += 1
if self.episodes_nb % 100 == 0:
print(f"Last 100 episodes : {self.episodes_wins}%")
self.episodes_wins = 0
return np.argmax(y_pred)
def reset(self):
self.activations = []
self.iter = 0
def load(self, path):
loaded = np.load(path)
self.model.Wh = loaded['Wh']
self.model.Bh = loaded['Bh']
self.model.Wo = loaded['Wo']
self.model.Bo = loaded['Bo']
def save(self):
np.savez_compressed("models/model_" + str(self.episodes_nb),
Wh=self.model.Wh,
Bh=self.model.Bh,
Wo=self.model.Wo,
Bo=self.model.Bo)
def letter(max_epochs, learning_rate):
np.random.seed(1)
inputs = []
outputs = []
doutput = []
columns = [
"letter", "x-box", "y-box", "width", "height", "onpix", "x-bar",
"y-bar", "x2bar", "y2bar", "xybar", "x2ybr", "xy2br", "x-ege", "xegvy",
"y-ege", "yegvx"
]
df = pd.read_csv("letter-recognition.data", names=columns)
doutput = df["letter"]
for i in range(len(doutput)):
outputs.append(ord(str(doutput[i])) - ord('A'))
inputs = df.drop(["letter"], axis=1)
inputs = np.array(inputs)
inputs = inputs / 15 #normalization
#train set
inputs_train = inputs[:16000]
categorical_y = np.zeros((16000, 26))
for i, l in enumerate(outputs[:16000]):
categorical_y[i][l] = 1
outputs_train = categorical_y
#test set
inputs_test = inputs[16000:]
# categorical_y = np.zeros((4000, 26))
# for i, l in enumerate(outputs[16000:]):
# categorical_y[i][l] = 1
# outputs_test=categorical_y
#training process
no_in = 16
no_hidden = 10
no_out = 26
NN = MLP(no_in, no_hidden, no_out)
NN.randomise()
print('\nMax Epoch:\n' + str(max_epochs), file=log)
print('\nLearning Rate:\n' + str(learning_rate), file=log)
print('\nTraining Process:\n', file=log)
for i in range(0, max_epochs):
NN.forward(inputs_train, 'tanh')
error = NN.backward(inputs_train, outputs_train, 'tanh')
NN.updateWeights(learning_rate)
if (i + 1) % (max_epochs / 20) == 0:
print(' Error at Epoch:\t' + str(i + 1) + '\t is \t' + str(error),
file=log)
#testing process
def to_character0(outputvector):
listov = list(outputvector)
a = listov.index(max(listov))
return chr(a + ord('A'))
prediction = []
for i in range(4000):
NN.forward(inputs_test[i], 'tanh')
# print('Target:\t{}\t Output:\t{}'.format(str(outputs_test[i]),str(NN.O)))
# print('Target:\t{}\t Output:\t{}'.format(str(doutput[16000+i]),str(to_character0(NN.O))))
prediction.append(to_character0(NN.O))
def to_character(n):
return chr(int(n) + ord('A'))
correct = {to_character(i): 0 for i in range(26)}
letter_num = {to_character(i): 0 for i in range(26)}
print('==' * 30, file=log)
for i, _ in enumerate(doutput[16000:]):
letter_num[doutput[16000 + i]] += 1
# Print some predictions
if i % 300 == 0:
print('Expected: {} | Output: {}'.format(doutput[16000 + i],
prediction[i]),
file=log)
if doutput[16000 + i] == prediction[i]:
correct[prediction[i]] += 1
print('==' * 30, file=log)
# Calculate the accuracy
accuracy = sum(correct.values()) / len(prediction)
print('Test sample size: {} | Correctly predicted sample size: {}'.format(
len(prediction), sum(correct.values())),
file=log)
print('Accuracy: %.3f' % accuracy, file=log)
# Performance on each class
print('==' * 30, file=log)
for k, v in letter_num.items():
print('{} => Sample Number: {} | Correct Number: {} | Accuracy: {}'.
format(k, v, correct[k], correct[k] / v),
file=log)