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],