def test_mixture_density():
# graph holds information necessary to build layers from parents
random_state = np.random.RandomState(1999)
graph = OrderedDict()
X_sym, y_sym = add_datasets_to_graph([X, y], ["X", "y"], graph)
n_hid = 20
n_out = 1
minibatch_size = len(X)
train_indices = np.arange(len(X))
valid_indices = np.arange(len(X))
l1 = tanh_layer([X_sym], graph, 'l1', proj_dim=n_hid,
random_state=random_state)
coeffs, mus, log_sigmas = log_gaussian_mixture_layer(
[l1], graph, 'mdn', proj_dim=1, n_components=2,
random_state=random_state)
cost = log_gaussian_mixture_cost(coeffs, mus, log_sigmas, y_sym).mean()
params, grads = get_params_and_grads(graph, cost)
learning_rate = 1E-6
opt = sgd(params, learning_rate)
updates = opt.updates(params, grads)
fit_function = theano.function([X_sym, y_sym], [cost], updates=updates,
mode="FAST_COMPILE")
cost_function = theano.function([X_sym, y_sym], [cost],
mode="FAST_COMPILE")
checkpoint_dict = create_checkpoint_dict(locals())
epoch_results = fixed_n_epochs_trainer(
fit_function, cost_function, train_indices, valid_indices,
checkpoint_dict, [X, y],
minibatch_size,
list_of_train_output_names=["train_cost"],
valid_output_name="valid_cost",
n_epochs=1)
X = sine_x
y = sine_y
# graph holds information necessary to build layers from parents
graph = OrderedDict()
X_sym, y_sym = add_datasets_to_graph([X, y], ["X", "y"], graph, list_of_test_values=[sine_x, sine_y])
# random state so script is deterministic
random_state = np.random.RandomState(1999)
minibatch_size = len(sine_y) / 20
n_hid = 20
n_out = 1
l1 = tanh_layer([X_sym], graph, "l1", proj_dim=n_hid, random_state=random_state)
coeffs, mus, log_sigmas = log_gaussian_mixture_layer(
[l1], graph, "mdn", proj_dim=1, n_components=24, random_state=random_state
)
cost = log_gaussian_mixture_cost(coeffs, mus, log_sigmas, y_sym).mean()
params, grads = get_params_and_grads(graph, cost)
opt = adadelta(params)
updates = opt.updates(params, grads)
fit_function = theano.function([X_sym, y_sym], [cost], updates=updates)
cost_function = theano.function([X_sym, y_sym], [cost])
predict_function = theano.function([X_sym], [coeffs, mus, log_sigmas])
checkpoint_dict = create_checkpoint_dict(locals())
train_itr = minibatch_iterator([X, y], minibatch_size, axis=0)
valid_itr = minibatch_iterator([X, y], minibatch_size, axis=0)
list_of_test_values=[sine_x, sine_y])
# random state so script is deterministic
random_state = np.random.RandomState(1999)
minibatch_size = len(sine_y) / 20
n_hid = 20
n_out = 1
l1 = tanh_layer([X_sym],
graph,
'l1',
proj_dim=n_hid,
random_state=random_state)
coeffs, mus, log_sigmas = log_gaussian_mixture_layer([l1],
graph,
'mdn',
proj_dim=1,
n_components=24,
random_state=random_state)
cost = log_gaussian_mixture_cost(coeffs, mus, log_sigmas, y_sym).mean()
params, grads = get_params_and_grads(graph, cost)
opt = adadelta(params)
updates = opt.updates(params, grads)
fit_function = theano.function([X_sym, y_sym], [cost], updates=updates)
cost_function = theano.function([X_sym, y_sym], [cost])
predict_function = theano.function([X_sym], [coeffs, mus, log_sigmas])
checkpoint_dict = create_checkpoint_dict(locals())
train_itr = minibatch_iterator([X, y], minibatch_size, axis=0)
