def on_end(self):
plot_costs(self.costs, unit=5, learning_rate=0.0001)
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# load image dataset: blue/red dots in circles
train_x, train_y, test_x, test_y = load_dataset()
plt.show()
m_train = train_x.shape[1]
m_test = test_x.shape[1]
n = train_x.shape[0]
for initializer in NNModel.initializers.keys():
# Model
model = NNModel(n, initializer)
train_x = train_x.astype("float32")
train_y = train_y.astype("float32")
costs = model.train(train_x, train_y, print_cost=True)
plot_costs(costs, unit=ITERATION_UNIT, learning_rate=0.01)
# Predict
train_predictions = model.predict(train_x)
train_accuracy = m.accuracy(train_predictions, train_y)
print(f"Train accuracy = {train_accuracy}%")
test_predictions = model.predict(test_x)
test_accuracy = m.accuracy(test_predictions, test_y)
print(f"Test accuracy = {test_accuracy}%")
# Plot
plot_boundary(initializer, model, train_x, train_y)
def on_end(self):
print("time = ", time.time() - start_time)
plot_costs(self.costs, unit=1000, learning_rate=0.3)
print("=== Train ===")
pixels = num_px * num_px * 3
model = LogisticRegressionModel(pixels)
lr = 0.005
train_set_y = train_set_y.astype(np.float32)
start_time = time.time()
costs = model.train(train_set_x,
train_set_y,
learning_rate=lr,
num_iterations=2000,
print_cost=True)
print("training time = ", time.time() - start_time)
plot_costs(costs, unit=ITERATIONS_UNIT, learning_rate=lr)
# Predict
train_predictions = model.predict(train_set_x)
test_predictions = model.predict(test_set_x)
print("Train accuracy = ", m.accuracy(train_predictions, train_set_y), "%")
print("Test accuracy = ", m.accuracy(test_predictions, test_set_y), "%")
# Analyze
# Example of a picture that was wrongly classified.
errors = (index for index, (
p, r) in enumerate(zip(test_predictions[0, :], test_set_y[0, :]))
if p != r)
index = next(errors)