# load data
data = scipy.io.loadmat('../datasets/regularization.mat')
X = data['X']
Y = data['y']
# init model
model = miniml.Model()
model.dense(20, 'relu', 'xavier')
model.dense(3, 'relu', 'xavier')
model.dense(1, 'sigmoid', 'xavier')
# init params
rate = 0.3
epochs = 30000
lamb = 0.7
# train model
optimizer = miniml.GradDescent(cost='bce',
epochs=epochs,
init_seed=3,
dropout_seed=1,
store=1000,
verbose=10000)
costs = optimizer.train(model, X, Y, rate, lamb=lamb)
# plot results
miniml.print_accuracy(model, X, Y)
miniml.plot_costs(epochs, costs=costs)
miniml.plot_boundaries(model, X, Y)
Y_train = np.array(train_dataset["train_set_y"][:]).reshape(-1, 1)
test_dataset = h5py.File('../datasets/test_happy.h5', "r")
X_test = np.array(test_dataset["test_set_x"][:]).astype('float32') / 255.
Y_test = np.array(test_dataset["test_set_y"][:]).reshape(-1, 1)
# create model
model = miniml.Model()
model.conv2d(8, ksize=7, stride=1, activation='tanh')
model.flatten()
model.dense(1, 'sigmoid', 'plain')
# init params
rate = 0.001
epochs = 10
optimizer = miniml.Adam(cost='bce',
epochs=epochs,
batch_size=16,
batch_seed=3,
init_seed=42,
store=1,
verbose=1)
costs = optimizer.train(model, X_train, Y_train, rate)
# plot results
miniml.print_accuracy(model, X_train, Y_train)
miniml.print_accuracy(model, X_test, Y_test, label="Test")
miniml.plot_costs(epochs, costs=costs)