def main():
nnfs.init()
X, y = spiral_data(samples=1000, classes=3)
dense1 = Layer(2,
512,
weight_regularizer_l2=5e-4,
bias_regularizer_l2=5e-4)
activation1 = ReLU()
dense2 = Layer(512, 3)
cost_act = Soft_Ce()
optimizer = Adam(learning_rate=0.02, decay=5e-7)
for epoch in range(10001):
dense1.forwardProp(X)
activation1.forwardProp(dense1.output)
dense2.forwardProp(activation1.output)
data_cost = cost_act.forwardProp(dense2.output, y)
regularization_cost = \
cost_act.cost.regularization_cost(dense1) + \
cost_act.cost.regularization_cost(dense2)
cost = data_cost + regularization_cost
predictions = np.argmax(cost_act.output, axis=1)
if len(y.shape) == 2:
y = np.argmax(y, axis=1)
accuracy = np.mean(predictions == y)
if not epoch % 100:
print(f'epoch: {epoch}, ' + f'acc: {accuracy:.3f}, ' +
f'cost: {cost:.3f}, (' + f'data_cost: {data_cost:.3f}, ' +
f'reg_cost: {regularization_cost:.3f}), ' +
f'lr: {optimizer.curr_learning_rate}')
cost_act.backProp(cost_act.output, y)
dense2.backProp(cost_act.dinputs)
activation1.backProp(dense2.dinputs)
dense1.backProp(activation1.dinputs)
optimizer.pre_update_params()
optimizer.update_params(dense1)
optimizer.update_params(dense2)
optimizer.post_update_params()
X_test, y_test = spiral_data(samples=100, classes=3)
dense1.forwardProp(X_test)
activation1.forwardProp(dense1.output)
dense2.forwardProp(activation1.output)
cost = cost_act.forwardProp(dense2.output, y_test)
predictions = np.argmax(cost_act.output, axis=1)
if len(y.shape) == 2:
y_test = np.argmax(y_test, axis=1)
accuracy = np.mean(predictions == y_test)
print(f'validation, acc: {accuracy:.3f}, cost: {cost:.3f}')
