def test_rnn_correlated_mixture_density():
# graph holds information necessary to build layers from parents
random_state = np.random.RandomState(1999)
graph = OrderedDict()
minibatch_size = 5
X_seq = np.array([bernoulli_X for i in range(minibatch_size)])
y_seq = np.array([bernoulli_y for i in range(minibatch_size)])
X_mb, X_mb_mask = make_masked_minibatch(X_seq, slice(0, minibatch_size))
y_mb, y_mb_mask = make_masked_minibatch(y_seq, slice(0, minibatch_size))
datasets_list = [X_mb, X_mb_mask, y_mb, y_mb_mask]
names_list = ["X", "X_mask", "y", "y_mask"]
X_sym, X_mask_sym, y_sym, y_mask_sym = add_datasets_to_graph(
datasets_list, names_list, graph)
n_hid = 5
train_indices = np.arange(len(X_seq))
valid_indices = np.arange(len(X_seq))
l1 = tanh_layer([X_sym], graph, 'l1', proj_dim=n_hid,
random_state=random_state)
h = gru_recurrent_layer([l1], X_mask_sym, n_hid, graph, 'l1_rec',
random_state=random_state)
rval = bernoulli_and_correlated_log_gaussian_mixture_layer(
[h], graph, 'hw', proj_dim=2, n_components=3,
random_state=random_state)
binary, coeffs, mus, log_sigmas, corr = rval
cost = bernoulli_and_correlated_log_gaussian_mixture_cost(
binary, coeffs, mus, log_sigmas, corr, y_sym)
cost = masked_cost(cost, y_mask_sym).mean()
cost_function = theano.function([X_sym, X_mask_sym, y_sym, y_mask_sym],
[cost],
mode="FAST_COMPILE")
checkpoint_dict = create_checkpoint_dict(locals())
epoch_results = fixed_n_epochs_trainer(
cost_function, cost_function, train_indices, valid_indices,
checkpoint_dict, [X_seq, y_seq],
minibatch_size,
list_of_minibatch_functions=[make_masked_minibatch,
make_masked_minibatch],
list_of_train_output_names=["train_cost"],
valid_output_name="valid_cost",
n_epochs=1)
def test_correlated_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([bernoulli_X, bernoulli_y], ["X", "y"],
graph)
n_hid = 20
minibatch_size = len(bernoulli_X)
train_indices = np.arange(len(bernoulli_X))
valid_indices = np.arange(len(bernoulli_X))
l1 = tanh_layer([X_sym], graph, 'l1', proj_dim=n_hid,
random_state=random_state)
rval = bernoulli_and_correlated_log_gaussian_mixture_layer(
[l1], graph, 'hw', proj_dim=2, n_components=3,
random_state=random_state)
binary, coeffs, mus, log_sigmas, corr = rval
cost = bernoulli_and_correlated_log_gaussian_mixture_cost(
binary, coeffs, mus, log_sigmas, corr, 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, [bernoulli_X, bernoulli_y],
minibatch_size,
list_of_train_output_names=["train_cost"],
valid_output_name="valid_cost",
n_epochs=1)
weights = get_weights_from_graph(graph)
L2 = sum([(w**2).sum() for w in weights])
cost = nll + .0001 * L2
params, grads = get_params_and_grads(graph, cost)
learning_rate = 1E-4
momentum = 0.95
opt = rmsprop(params, learning_rate, momentum)
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], [y_pred])
checkpoint_dict = create_checkpoint_dict(locals())
def error(*args):
xargs = args[:-1]
y = args[-1]
final_args = xargs
y_pred = predict_function(*final_args)[0]
return 1 - np.mean(
(np.argmax(y_pred, axis=1).ravel()) == (np.argmax(y, axis=1).ravel()))
train_itr = minibatch_iterator([X, y],
minibatch_size,
axis=0,
stop_index=60000)
def train_loop(itr):
mb = next(itr)
X_mb, y_mb = mb[:-1], mb[1:]
cost, h = fit_function(X_mb, y_mb, train_h_init)
train_h_init[:] = h[-1, :]
return [cost]
def valid_loop(itr):
mb = next(itr)
X_mb, y_mb = mb[:-1], mb[1:]
cost, h = cost_function(X_mb, y_mb, valid_h_init)
valid_h_init[:] = h[-1, :]
return [cost]
checkpoint_dict = create_checkpoint_dict(locals())
TL = TrainingLoop(
train_loop,
train_itr,
valid_loop,
valid_itr,
n_epochs=100,
checkpoint_every_n_epochs=1,
checkpoint_dict=checkpoint_dict,
skip_minimums=True,
)
epoch_results = TL.run()
