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)
y_mb, y_mb_mask = make_masked_minibatch(y, slice(0, minibatch_size))
datasets_list = [X_mb, X_mb_mask, y_mb, y_mb_mask]
names_list = ["X", "X_mask", "y", "y_mask"]
graph = OrderedDict()
X_sym, X_mask_sym, y_sym, y_mask_sym = add_datasets_to_graph(
datasets_list, names_list, graph)
l1 = relu_layer([X_sym], graph, 'l1', proj_dim=n_hid,
random_state=random_state)
h = lstm_recurrent_layer([l1], X_mask_sym, rnn_dim, graph, 'l1_rec',
random_state=random_state)
l2 = relu_layer([h], graph, 'l2', proj_dim=n_hid,
random_state=random_state)
rval = bernoulli_and_correlated_log_gaussian_mixture_layer(
[l2], graph, 'hw', proj_dim=2, n_components=20, random_state=random_state)
binary, coeffs, mus, sigmas, corr = rval
cost = bernoulli_and_correlated_log_gaussian_mixture_cost(
binary, coeffs, mus, sigmas, corr, y_sym)
cost = masked_cost(cost, y_mask_sym).sum(axis=0).mean()
params, grads = get_params_and_grads(graph, cost)
learning_rate = 0.0003
opt = adam(params, learning_rate)
clipped_grads = gradient_clipping(grads)
updates = opt.updates(params, clipped_grads)
fit_function = theano.function([X_sym, X_mask_sym, y_sym, y_mask_sym], [cost],
updates=updates)
cost_function = theano.function([X_sym, X_mask_sym, y_sym, y_mask_sym], [cost])
predict_function = theano.function([X_sym, X_mask_sym],
X_mb, X_mb_mask, y_mb, y_mb_mask = next(train_itr)
train_itr.reset()
datasets_list = [X_mb, X_mb_mask, y_mb, y_mb_mask]
names_list = ["X", "X_mask", "y", "y_mask"]
graph = OrderedDict()
X_sym, X_mask_sym, y_sym, y_mask_sym = add_datasets_to_graph(
datasets_list, names_list, graph)
l1 = relu_layer([X_sym], graph, 'l1', proj_dim=n_hid,
random_state=random_state)
h = lstm_recurrent_layer([l1], X_mask_sym, rnn_dim, graph, 'l1_rec',
random_state=random_state)
l2 = relu_layer([h], graph, 'l2', proj_dim=n_hid,
random_state=random_state)
rval = bernoulli_and_correlated_log_gaussian_mixture_layer(
[l2], graph, 'hw', proj_dim=2, n_components=20, random_state=random_state)
binary, coeffs, mus, sigmas, corr = rval
cost = bernoulli_and_correlated_log_gaussian_mixture_cost(
binary, coeffs, mus, sigmas, corr, y_sym)
cost = masked_cost(cost, y_mask_sym).sum(axis=0).mean()
params, grads = get_params_and_grads(graph, cost)
learning_rate = 0.0003
opt = adam(params, learning_rate)
clipped_grads = gradient_clipping(grads)
updates = opt.updates(params, clipped_grads)
fit_function = theano.function([X_sym, X_mask_sym, y_sym, y_mask_sym], [cost],
updates=updates)
cost_function = theano.function([X_sym, X_mask_sym, y_sym, y_mask_sym], [cost])
predict_function = theano.function([X_sym, X_mask_sym],
