def test_embedding_layer():
random_state = np.random.RandomState(1999)
graph = OrderedDict()
max_index = 100
proj_dim = 12
fake_str_int = [[1, 5, 7, 1, 6, 2], [2, 3, 6, 2], [3, 3, 3, 3, 3, 3, 3]]
minibatch, mask = make_embedding_minibatch(
fake_str_int, slice(0, 3))
(emb_slices,), (emb_mask,) = add_embedding_datasets_to_graph(
[minibatch], [mask], "emb", graph)
emb = embedding_layer(emb_slices, max_index, proj_dim, graph,
'emb', random_state)
followup_dim = 17
proj = linear_layer([emb], graph, 'proj', followup_dim,
random_state=random_state)
f = theano.function(emb_slices, [proj], mode="FAST_COMPILE")
out, = f(*minibatch)
assert(out.shape[-1] == 17)
assert(out.shape[-2] == len(fake_str_int))
def test_embedding_layer():
random_state = np.random.RandomState(1999)
graph = OrderedDict()
max_index = 100
proj_dim = 12
fake_str_int = [[1, 5, 7, 1, 6, 2], [2, 3, 6, 2], [3, 3, 3, 3, 3, 3, 3]]
minibatch, mask = make_embedding_minibatch(fake_str_int, slice(0, 3))
(emb_slices, ), (emb_mask, ) = add_embedding_datasets_to_graph([minibatch],
[mask],
"emb",
graph)
emb = embedding_layer(emb_slices, max_index, proj_dim, graph, 'emb',
random_state)
followup_dim = 17
proj = linear_layer([emb], graph, 'proj', followup_dim, random_state)
f = theano.function(emb_slices, [proj], mode="FAST_COMPILE")
out, = f(*minibatch)
assert (out.shape[-1] == 17)
assert (out.shape[-2] == len(fake_str_int))
n_hid = 100
X_story_mb, X_story_mask = make_embedding_minibatch(
X_story, slice(0, minibatch_size))
X_query_mb, X_query_mask = make_embedding_minibatch(
X_query, slice(0, minibatch_size))
embedding_datasets = [X_story_mb, X_query_mb]
masks = [X_story_mask, X_query_mask]
r = add_embedding_datasets_to_graph(embedding_datasets, masks, "babi_data",
graph)
(X_story_syms, X_query_syms), (X_story_mask_sym, X_query_mask_sym) = r
y_sym = add_datasets_to_graph([y_answer], ["y"], graph)
l1_story = embedding_layer(X_story_syms, vocab_size, n_emb, graph, 'l1_story',
random_state)
masked_story = X_story_mask_sym.dimshuffle(0, 1, 'x') * l1_story
h_story = gru_recurrent_layer([masked_story], X_story_mask_sym, n_hid, graph,
'story_rec', random_state)
l1_query = embedding_layer(X_query_syms, vocab_size, n_emb, graph, 'l1_query',
random_state)
h_query = gru_recurrent_layer([l1_query], X_query_mask_sym, n_hid, graph,
'query_rec', random_state)
y_pred = softmax_layer([h_query[-1], h_story[-1]], graph, 'y_pred',
y_answer.shape[1], random_state)
cost = categorical_crossentropy(y_pred, y_sym).mean()
params, grads = get_params_and_grads(graph, cost)
opt = adam(params)
learning_rate = 0.001
n_hid = 100
X_story_mb, X_story_mask = make_embedding_minibatch(
X_story, slice(0, minibatch_size))
X_query_mb, X_query_mask = make_embedding_minibatch(
X_query, slice(0, minibatch_size))
embedding_datasets = [X_story_mb, X_query_mb]
masks = [X_story_mask, X_query_mask]
r = add_embedding_datasets_to_graph(embedding_datasets, masks, "babi_data",
graph)
(X_story_syms, X_query_syms), (X_story_mask_sym, X_query_mask_sym) = r
y_sym = add_datasets_to_graph([y_answer], ["y"], graph)
l1_story = embedding_layer(X_story_syms, vocab_size, n_emb, graph, 'l1_story',
random_state=random_state)
masked_story = X_story_mask_sym.dimshuffle(0, 1, 'x') * l1_story
h_story = gru_recurrent_layer([masked_story], X_story_mask_sym, n_hid, graph,
'story_rec', random_state)
l1_query = embedding_layer(X_query_syms, vocab_size, n_emb, graph, 'l1_query',
random_state)
h_query = gru_recurrent_layer([l1_query], X_query_mask_sym, n_hid, graph,
'query_rec', random_state)
y_pred = softmax_layer([h_query[-1], h_story[-1]], graph, 'y_pred',
y_answer.shape[1], random_state=random_state)
cost = categorical_crossentropy(y_pred, y_sym).mean()
params, grads = get_params_and_grads(graph, cost)
opt = adadelta(params)
updates = opt.updates(params, grads)
