def test_maxout_layer(): random_state = np.random.RandomState(42) graph = OrderedDict() X_sym, y_sym = add_datasets_to_graph([X, y], ["X", "y"], graph) single_o = maxout_layer([X_sym], graph, 'single', proj_dim=5, random_state=random_state) concat_o = maxout_layer([X_sym, y_sym], graph, 'concat', proj_dim=5, random_state=random_state) # Check that strict mode raises an error if repeated assert_raises(AssertionError, maxout_layer, [X_sym], graph, 'concat') f = theano.function([X_sym, y_sym], [single_o, concat_o], mode="FAST_COMPILE") single, concat = f(X, y)
valid_indices = data["valid_indices"] minibatch_size = 10 X_mb, X_mb_mask = make_masked_minibatch(X, slice(0, minibatch_size)) y_mb, y_mb_mask = make_masked_minibatch(y, slice(0, minibatch_size)) n_hid = 500 n_out = vocab_size + 1 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) l1 = maxout_layer([X_sym], graph, 'l1', n_hid, random_state=random_state) h = bidirectional_gru_recurrent_layer([l1], X_mask_sym, n_hid, graph, 'l1_rec', random_state=random_state) l2 = maxout_layer([h], graph, 'l2', n_hid, random_state=random_state) y_pred = softmax_layer([l2], graph, 'softmax', n_out, random_state=random_state) cost = log_ctc_cost(y_sym, y_mask_sym, y_pred, X_mask_sym).mean() params, grads = get_params_and_grads(graph, cost) opt = adadelta(params) updates = opt.updates(params, grads) checkpoint_dict = {} fit_function = theano.function([X_sym, X_mask_sym, y_sym, y_mask_sym], [cost], updates=updates)
valid_indices = data["valid_indices"] minibatch_size = 10 X_mb, X_mb_mask = make_masked_minibatch(X, slice(0, minibatch_size)) y_mb, y_mb_mask = make_masked_minibatch(y, slice(0, minibatch_size)) n_hid = 500 n_out = vocab_size + 1 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) l1 = maxout_layer([X_sym], graph, 'l1', n_hid, random_state=random_state) h = bidirectional_gru_recurrent_layer([l1], X_mask_sym, n_hid, graph, 'l1_rec', random_state=random_state) l2 = maxout_layer([h], graph, 'l2', n_hid, random_state=random_state) y_pred = softmax_layer([l2], graph, 'softmax', n_out, random_state=random_state) cost = log_ctc_cost(y_sym, y_mask_sym, y_pred, X_mask_sym).mean() params, grads = get_params_and_grads(graph, cost)