def train(x, target, learning_rate, iterate):
inputNodes = x.shape[-1]
outputNodes = target.shape[-1]
weight = np.full((inputNodes, outputNodes), 1.0)
bias = np.zeros((outputNodes))
for i in range(iterate):
predict = linear.forward(x, weight, bias)
error = (predict - target)
mse = np.average(error**2)
print_summary(i, mse)
error, weight_delta, bias_delta = linear.backward(
x, error, weight, bias)
weight -= (learning_rate * weight_delta)
bias -= (learning_rate * bias_delta)
return weight, bias
def train(x, target, learning_rate, iterate):
inputNodes = x.shape[-1]
outputNodes = target.shape[-1]
h1_units = 25
h1_weight = np.random.normal(size=(inputNodes, h1_units))
h1_bias = np.zeros((h1_units))
h2_weight = np.random.normal(size=(h1_units, outputNodes))
h2_bias = np.zeros((outputNodes))
for i in range(iterate):
y1 = linear.forward(x, h1_weight, h1_bias)
s1 = sigmoid.forward(y1)
y2 = linear.forward(s1, h2_weight, h2_bias)
predict = sigmoid.forward(y2)
error = (predict - target)
mse = np.average(error**2)
print_summary(i, mse)
error = sigmoid.backward(y2, error)
error, h2_weight_delta, h2_bias_delta = linear.backward(
s1, error, h2_weight, h2_bias)
h2_weight -= (learning_rate * h2_weight_delta)
h2_bias -= (learning_rate * h2_bias_delta)
error = sigmoid.backward(y1, error)
error, h1_weight_delta, h1_bias_delta = linear.backward(
x, error, h1_weight, h1_bias)
h1_weight -= (learning_rate * h1_weight_delta)
h1_bias -= (learning_rate * h1_bias_delta)
return h1_weight, h1_bias, h2_weight, h2_bias
def train(x, target, learning_rate, iterate):
inputNodes = x.shape[-1]
outputNodes = target.shape[-1]
weight = np.zeros((inputNodes, outputNodes))
bias = np.zeros((outputNodes))
for i in range(iterate):
y = linear.forward(x, weight, bias)
p = sigmoid.forward(y)
p_error = (p - target)
loss = np.average(p_error**2)
print_numpy('w', weight)
print_numpy('b', bias)
print_numpy('y', y)
print_numpy('p', p)
print_numpy('L', loss)
y_error = sigmoid.backward(y, p_error)
x_error, w_error, b_error = linear.backward(x, y_error, weight, bias)
weight -= (learning_rate * w_error)
bias -= (learning_rate * b_error)
print_numpy('~p', p_error)
print_numpy('~y', y_error)
print_numpy('~x', x_error)
print_numpy('~w', w_error)
print_numpy('~b', b_error)
print('----- epoch : ', (i + 1), '------')
return weight, bias
weight = np.full((inputNodes, outputNodes), 1.0)
bias = np.zeros((outputNodes))
for i in range(iterate):
predict = linear.forward(x, weight, bias)
error = (predict - target)
mse = np.average(error**2)
print_summary(i, mse)
error, weight_delta, bias_delta = linear.backward(
x, error, weight, bias)
weight -= (learning_rate * weight_delta)
bias -= (learning_rate * bias_delta)
return weight, bias
train_x = np.array([[2.0]])
target = np.array([[11.0]])
weight, bias = train(train_x, target, learning_rate=0.1, iterate=5)
predict = linear.forward(train_x, weight, bias)
print('predict : ', predict)
h2_weight -= (learning_rate * h2_weight_delta)
h2_bias -= (learning_rate * h2_bias_delta)
error = sigmoid.backward(y1, error)
error, h1_weight_delta, h1_bias_delta = linear.backward(
x, error, h1_weight, h1_bias)
h1_weight -= (learning_rate * h1_weight_delta)
h1_bias -= (learning_rate * h1_bias_delta)
return h1_weight, h1_bias, h2_weight, h2_bias
train_x = np.array([[0, 0], [1, 1]])
target = np.array([[0], [1]])
h1_weight, h1_bias, h2_weight, h2_bias = train(train_x,
target,
learning_rate=0.01,
iterate=5000)
y1 = linear.forward(train_x, h1_weight, h1_bias)
s1 = sigmoid.forward(y1)
y2 = linear.forward(s1, h2_weight, h2_bias)
predict = sigmoid.forward(y2)
print('predict : ', predict)