def build_network(self, vocab_size, input_var, mask_var, W_init):
l_in = L.InputLayer(shape=(None, None, 1), input_var=input_var)
l_mask = L.InputLayer(shape=(None, None), input_var=mask_var)
l_embed = L.EmbeddingLayer(l_in,
input_size=vocab_size,
output_size=EMBED_DIM,
W=W_init)
l_fwd_1 = L.LSTMLayer(l_embed,
NUM_HIDDEN,
grad_clipping=GRAD_CLIP,
mask_input=l_mask,
gradient_steps=GRAD_STEPS,
precompute_input=True)
l_bkd_1 = L.LSTMLayer(l_embed,
NUM_HIDDEN,
grad_clipping=GRAD_CLIP,
mask_input=l_mask,
gradient_steps=GRAD_STEPS,
precompute_input=True,
backwards=True)
l_all_1 = L.concat([l_fwd_1, l_bkd_1], axis=2)
l_fwd_2 = L.LSTMLayer(l_all_1,
NUM_HIDDEN,
grad_clipping=GRAD_CLIP,
mask_input=l_mask,
gradient_steps=GRAD_STEPS,
precompute_input=True)
l_bkd_2 = L.LSTMLayer(l_all_1,
NUM_HIDDEN,
grad_clipping=GRAD_CLIP,
mask_input=l_mask,
gradient_steps=GRAD_STEPS,
precompute_input=True,
backwards=True)
l_fwd_1_slice = L.SliceLayer(l_fwd_1, -1, 1)
l_bkd_1_slice = L.SliceLayer(l_bkd_1, 0, 1)
y_1 = L.ElemwiseSumLayer([l_fwd_1_slice, l_bkd_1_slice])
l_fwd_2_slice = L.SliceLayer(l_fwd_2, -1, 1)
l_bkd_2_slice = L.SliceLayer(l_bkd_2, 0, 1)
y_2 = L.ElemwiseSumLayer([l_fwd_2_slice, l_bkd_2_slice])
y = L.concat([y_1, y_2], axis=1)
g = L.DenseLayer(y,
num_units=EMBED_DIM,
nonlinearity=lasagne.nonlinearities.tanh)
l_out = L.DenseLayer(g,
num_units=vocab_size,
W=l_embed.W.T,
nonlinearity=lasagne.nonlinearities.softmax)
return l_out
def build_autoencoder_network():
input_var = T.tensor4('input_var');
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 120, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 120, filter_size=(1,1), stride=1, pad='same', nonlinearity=leaky_rectify));
pool1 = layers.MaxPool2DLayer(layer, (2, 2), 2);
layer = batch_norm(layers.Conv2DLayer(pool1, 240, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 320, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 320, filter_size=(1,1), stride=1, pad='same', nonlinearity=leaky_rectify));
pool2 = layers.MaxPool2DLayer(layer, (2, 2), 2);
layer = batch_norm(layers.Conv2DLayer(pool2, 640, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
prely = batch_norm(layers.Conv2DLayer(layer, 1024, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
featm = batch_norm(layers.Conv2DLayer(prely, 640, filter_size=(1,1), nonlinearity=leaky_rectify));
feat_map = batch_norm(layers.Conv2DLayer(featm, 100, filter_size=(1,1), nonlinearity=rectify, name="feat_map"));
maskm = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
mask_rep = batch_norm(layers.Conv2DLayer(maskm, 1, filter_size=(1,1), nonlinearity=None), beta=None, gamma=None);
mask_map = SoftThresPerc(mask_rep, perc=90.0, alpha=0.1, beta=init.Constant(0.5), tight=100.0, name="mask_map");
layer = ChInnerProdMerge(feat_map, mask_map, name="encoder");
layer = batch_norm(layers.Deconv2DLayer(layer, 1024, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 640, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(1,1), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.InverseLayer(layer, pool2);
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 120, filter_size=(1,1), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.InverseLayer(layer, pool1);
layer = batch_norm(layers.Deconv2DLayer(layer, 120, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.Deconv2DLayer(layer, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
glblf = batch_norm(layers.Conv2DLayer(prely, 128, filter_size=(1,1), nonlinearity=leaky_rectify));
glblf = layers.Pool2DLayer(glblf, pool_size=(5,5), stride=5, mode='average_inc_pad');
glblf = batch_norm(layers.Conv2DLayer(glblf, 64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Conv2DLayer(glblf, 5, filter_size=(1,1), nonlinearity=rectify), name="global_feature");
glblf = batch_norm(layers.Deconv2DLayer(glblf, 256, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(9,9), stride=5, crop=(2,2), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = layers.Deconv2DLayer(glblf, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
layer = layers.ElemwiseSumLayer([layer, glblf]);
network = ReshapeLayer(layer, ([0], -1));
mask_var = lasagne.layers.get_output(mask_map);
output_var = lasagne.layers.get_output(network);
return network, input_var, mask_var, output_var;
def build_network(self):
l_char1_in = L.InputLayer(shape=(None, None, self.max_word_len),
input_var=self.inps[0])
l_char2_in = L.InputLayer(shape=(None, None, self.max_word_len),
input_var=self.inps[1])
l_mask1_in = L.InputLayer(shape=(None, None, self.max_word_len),
input_var=self.inps[2])
l_mask2_in = L.InputLayer(shape=(None, None, self.max_word_len),
input_var=self.inps[3])
l_char_in = L.ConcatLayer([l_char1_in, l_char2_in],
axis=1) # B x (ND+NQ) x L
l_char_mask = L.ConcatLayer([l_mask1_in, l_mask2_in], axis=1)
shp = (self.inps[0].shape[0],
self.inps[0].shape[1] + self.inps[1].shape[1],
self.inps[1].shape[2])
l_index_reshaped = L.ReshapeLayer(l_char_in,
(shp[0] * shp[1], shp[2])) # BN x L
l_mask_reshaped = L.ReshapeLayer(l_char_mask,
(shp[0] * shp[1], shp[2])) # BN x L
l_lookup = L.EmbeddingLayer(l_index_reshaped, self.num_chars,
self.char_dim) # BN x L x D
l_fgru = L.GRULayer(l_lookup,
2 * self.char_dim,
grad_clipping=10,
gradient_steps=-1,
precompute_input=True,
only_return_final=True,
mask_input=l_mask_reshaped)
l_bgru = L.GRULayer(l_lookup,
2 * self.char_dim,
grad_clipping=10,
gradient_steps=-1,
precompute_input=True,
backwards=True,
only_return_final=True,
mask_input=l_mask_reshaped) # BN x 2D
l_fwdembed = L.DenseLayer(l_fgru,
self.embed_dim / 2,
nonlinearity=None) # BN x DE
l_bckembed = L.DenseLayer(l_bgru,
self.embed_dim / 2,
nonlinearity=None) # BN x DE
l_embed = L.ElemwiseSumLayer([l_fwdembed, l_bckembed], coeffs=1)
l_char_embed = L.ReshapeLayer(l_embed,
(shp[0], shp[1], self.embed_dim / 2))
l_embed1 = L.SliceLayer(l_char_embed,
slice(0, self.inps[0].shape[1]),
axis=1)
l_embed2 = L.SliceLayer(l_char_embed,
slice(-self.inps[1].shape[1], None),
axis=1)
return l_embed1, l_embed2
def build_autoencoder_network():
input_var = T.tensor4('input_var');
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 120, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = layers.Pool2DLayer(layer, pool_size=(2,2), stride=2, mode='average_inc_pad');
layer = batch_norm(layers.Conv2DLayer(layer, 240, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 320, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = layers.Pool2DLayer(layer, pool_size=(2,2), stride=2, mode='average_inc_pad');
layer = batch_norm(layers.Conv2DLayer(layer, 640, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
prely = batch_norm(layers.Conv2DLayer(layer, 1024, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
featm = batch_norm(layers.Conv2DLayer(prely, 640, filter_size=(1,1), nonlinearity=leaky_rectify));
feat_map = batch_norm(layers.Conv2DLayer(featm, 100, filter_size=(1,1), nonlinearity=rectify, name="feat_map"));
layer = feat_map;
layer = batch_norm(layers.Deconv2DLayer(layer, 1024, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 640, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 640, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 240, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 120, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.Deconv2DLayer(layer, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
glblf = batch_norm(layers.Conv2DLayer(prely, 128, filter_size=(1,1), nonlinearity=leaky_rectify));
glblf = layers.Pool2DLayer(glblf, pool_size=(5,5), stride=5, mode='average_inc_pad');
glblf = batch_norm(layers.Conv2DLayer(glblf, 64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Conv2DLayer(glblf, 5, filter_size=(1,1), nonlinearity=rectify), name="global_feature");
glblf = batch_norm(layers.Deconv2DLayer(glblf, 256, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(9,9), stride=5, crop=(2,2), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = layers.Deconv2DLayer(glblf, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
layer = layers.ElemwiseSumLayer([layer, glblf]);
network = ReshapeLayer(layer, ([0], -1));
output_var = lasagne.layers.get_output(network);
return network, input_var, output_var;
def get_conv_input(self, sidx, tidx, avg=False):
suf = '_avg' if avg else ''
feat_embs = [
self.manager.feats[name].get_emb_layer(sidx, tidx, avg=avg)
for name in self.args.source_feats
]
# TODO: change the meaning
if self.args.lex == 'mix':
concat_emb = L.ElemwiseSumLayer(feat_embs) # (100, 15, 256)
else:
concat_emb = L.concat(feat_embs, axis=2) # (100, 15, 256+100)
pos = np.array([0] * (self.args.window_size / 2) + [1] + [0] *
(self.args.window_size / 2)).astype(
theano.config.floatX)
post = theano.shared(pos[np.newaxis, :, np.newaxis],
borrow=True) # (1, 15, 1)
posl = L.InputLayer(
(None, self.args.window_size, 1),
input_var=T.extra_ops.repeat(post, sidx.shape[0],
axis=0)) # (100, 15, 1)
conv_in = L.concat([concat_emb, posl], axis=2) # (100, 15, 256+1)
if self.args.pos_emb:
posint = L.flatten(
L.ExpressionLayer(posl,
lambda x: T.cast(x, 'int64'))) # (100, 15)
pos_emb = L.EmbeddingLayer(
posint,
self.args.window_size,
8,
name='epos' + suf,
W=Normal(0.01) if not avg else Constant()) # (100, 15, 8)
pos_emb.params[pos_emb.W].remove('regularizable')
conv_in = L.concat([concat_emb, posl, pos_emb],
axis=2) # (100, 15, 256+1+8)
# # squeeze
# if self.args.squeeze:
# conv_in = L.DenseLayer(conv_in, num_units=self.args.squeeze, name='squeeze'+suf, num_leading_axes=2,
# W=HeNormal('relu')) # (100, 15, 256)
conv_in = L.dimshuffle(conv_in, (0, 2, 1)) # (100, 256+1, 15)
return conv_in
def build_model(n_input,
n_hidden,
optimizer=adagrad,
l2_weight=1e-4,
l1_weight=1e-2):
'''
build NN model to estimating model function
'''
global LR
input_A = L.InputLayer((None, n_input), name='A')
layer_A = L.DenseLayer(input_A, n_hidden, b=None, nonlinearity=identity)
input_B = L.InputLayer((None, n_input), name='B')
layer_B = L.DenseLayer(input_B, n_hidden, b=None, nonlinearity=identity)
merge_layer = L.ElemwiseSumLayer((layer_A, layer_B))
output_layer = L.DenseLayer(merge_layer, 1, b=None,
nonlinearity=identity) # output is scalar
x1 = T.matrix('x1')
x2 = T.matrix('x2')
y = T.matrix('y')
out = L.get_output(output_layer, {input_A: x1, input_B: x2})
params = L.get_all_params(output_layer)
loss = T.mean(squared_error(out, y))
# add l1 penalty
l1_penalty = regularize_layer_params([layer_A, layer_B, output_layer], l1)
# add l2 penalty
l2_penalty = regularize_layer_params([layer_A, layer_B, output_layer], l2)
# get loss + penalties
loss = loss + l1_penalty * l1_weight + l2_penalty * l2_weight
updates_sgd = optimizer(loss, params, learning_rate=LR)
updates = apply_momentum(updates_sgd, params, momentum=0.9)
# updates = optimizer(loss,params,learning_rate=LR)
f_train = theano.function([x1, x2, y], loss, updates=updates)
f_test = theano.function([x1, x2, y], loss)
f_out = theano.function([x1, x2], out)
return f_train, f_test, f_out, output_layer
def _build_network(self, state_var, action_var):
"""Builds critic network:; inputs: (state, action), outputs: Q-val."""
# States -> Hidden
state_in = nn.InputLayer((None, ) + self.state_shape, state_var)
states = nn.DenseLayer(state_in, 30, W_init, b_init, relu)
states = nn.DenseLayer(states, 30, W_init, b_init, nonlinearity=None)
# Actions -> Hidden
action_in = nn.InputLayer((None, self.num_actions), action_var)
actions = nn.DenseLayer(action_in,
30,
W_init,
b=None,
nonlinearity=None)
# States_h + Actions_h -> Output
net = nn.ElemwiseSumLayer([states, actions])
net = nn.NonlinearityLayer(net, relu)
return nn.DenseLayer(net, 1, W_out, b_out, nonlinearity=None)
def model(self, query_input, batch_size, query_vocab_size,
context_vocab_size, emb_dim_size):
l_input = L.InputLayer(shape=(batch_size, ), input_var=query_input)
l_embed_continuous = L.EmbeddingLayer(l_input,
input_size=query_vocab_size,
output_size=emb_dim_size)
l_values_discrete = L.EmbeddingLayer(l_input,
input_size=query_vocab_size,
output_size=emb_dim_size)
l_probabilities_discrete = L.NonlinearityLayer(
l_values_discrete, nonlinearity=lasagne.nonlinearities.softmax)
l_embed_discrete = StochasticLayer(l_probabilities_discrete,
estimator='MF')
l_merge = L.ElemwiseSumLayer([l_embed_continuous, l_embed_discrete])
l_out = L.DenseLayer(l_merge,
num_units=emb_dim_size,
nonlinearity=lasagne.nonlinearities.softmax)
l_merge_2 = L.ElemwiseMergeLayer([l_out, l_embed_discrete],
merge_function=T.mul)
l_final_out = L.DenseLayer(l_merge_2, num_units=context_vocab_size)
return l_values_discrete, l_final_out
def build_segmenter_simple_absurd_res():
sys.setrecursionlimit(1500)
inp = ll.InputLayer(shape=(None, 1, None, None), name='input')
n_layers = 64 # should get a 128 x 128 receptive field
layers = [inp]
for i in range(n_layers):
# every 2 layers, add a skip connection
layers.append(
ll.Conv2DLayer(layers[-1],
num_filters=8,
filter_size=(3, 3),
pad='same',
W=Orthogonal(),
nonlinearity=linear,
name='conv%d' % (i + 1)))
layers.append(ll.BatchNormLayer(layers[-1], name='bn%i' % (i + 1)))
if (i % 2 == 0) and (i != 0):
layers.append(
ll.ElemwiseSumLayer([
layers[-1], # prev layer
layers[-6],
] # 3 actual layers per block, skip the previous block
))
layers.append(ll.NonlinearityLayer(layers[-1], nonlinearity=rectify))
# our output layer is also convolutional, remember that our Y is going to be the same exact size as the
conv_final = ll.Conv2DLayer(layers[-1],
num_filters=2,
filter_size=(3, 3),
pad='same',
W=Orthogonal(),
name='conv_final',
nonlinearity=linear)
# we need to reshape it to be a (batch*n*m x 3), i.e. unroll s.t. the feature dimension is preserved
softmax = Softmax4D(conv_final, name='4dsoftmax')
return [softmax]
def build_autoencoder_network():
input_var = T.tensor4('input_var')
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var)
layer = batch_norm(
layers.Conv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
prely = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
featm = batch_norm(
layers.Conv2DLayer(prely,
180,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
feat_map = batch_norm(
layers.Conv2DLayer(featm,
120,
filter_size=(1, 1),
nonlinearity=rectify,
name="feat_map"))
maskm = batch_norm(
layers.Conv2DLayer(prely,
120,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
mask_rep = batch_norm(layers.Conv2DLayer(maskm,
1,
filter_size=(1, 1),
nonlinearity=None),
beta=None,
gamma=None)
mask_map = SoftThresPerc(mask_rep,
perc=99.9,
alpha=0.5,
beta=init.Constant(0.5),
tight=100.0,
name="mask_map")
layer = ChInnerProdMerge(feat_map, mask_map, name="encoder")
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = layers.Deconv2DLayer(layer,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
glblf = batch_norm(
layers.Conv2DLayer(prely,
100,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
glblf = layers.Pool2DLayer(glblf,
pool_size=(5, 5),
stride=5,
mode='average_inc_pad')
glblf = batch_norm(
layers.Conv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(layers.Conv2DLayer(glblf,
3,
filter_size=(1, 1),
nonlinearity=rectify),
name="global_feature")
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(9, 9),
stride=5,
crop=(2, 2),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
48,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
48,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
48,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = layers.Deconv2DLayer(glblf,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
layer = layers.ElemwiseSumLayer([layer, glblf])
network = ReshapeLayer(layer, ([0], -1))
layers.set_all_param_values(network,
pickle.load(open(filename_model_ae, 'rb')))
feat_var = lasagne.layers.get_output(feat_map, deterministic=True)
mask_var = lasagne.layers.get_output(mask_map, deterministic=True)
outp_var = lasagne.layers.get_output(network, deterministic=True)
return network, input_var, feat_var, mask_var, outp_var
def build_autoencoder_network():
input_var = T.tensor4('input_var')
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var)
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
120,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = layers.Pool2DLayer(layer,
pool_size=(2, 2),
stride=2,
mode='average_inc_pad')
layer = batch_norm(
layers.Conv2DLayer(layer,
240,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = layers.Pool2DLayer(layer,
pool_size=(2, 2),
stride=2,
mode='average_inc_pad')
layer = batch_norm(
layers.Conv2DLayer(layer,
640,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
prely = batch_norm(
layers.Conv2DLayer(layer,
1024,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
featm = batch_norm(
layers.Conv2DLayer(prely,
640,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
feat_map = batch_norm(
layers.Conv2DLayer(featm,
100,
filter_size=(1, 1),
nonlinearity=rectify,
name="feat_map"))
mask_map = SoftThresPerc(feat_map,
perc=98.4,
alpha=0.1,
beta=init.Constant(0.5),
tight=20.0,
name="mask_map")
layer = mask_map
layer = batch_norm(
layers.Deconv2DLayer(layer,
1024,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
640,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
640,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
240,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
120,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = layers.Deconv2DLayer(layer,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
glblf = batch_norm(
layers.Conv2DLayer(prely,
128,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
glblf = layers.Pool2DLayer(glblf,
pool_size=(5, 5),
stride=5,
mode='average_inc_pad')
glblf = batch_norm(
layers.Conv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(layers.Conv2DLayer(glblf,
5,
filter_size=(1, 1),
nonlinearity=rectify),
name="global_feature")
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
256,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(9, 9),
stride=5,
crop=(2, 2),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = layers.Deconv2DLayer(glblf,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
layer = layers.ElemwiseSumLayer([layer, glblf])
network = ReshapeLayer(layer, ([0], -1))
mask_var = lasagne.layers.get_output(mask_map)
output_var = lasagne.layers.get_output(network)
return network, input_var, mask_var, output_var
def build_autoencoder_network():
input_var = T.tensor4('input_var')
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var)
layer = batch_norm(
layers.Conv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
prely = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
featm = batch_norm(
layers.Conv2DLayer(prely,
180,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
feat_map = batch_norm(
layers.Conv2DLayer(featm,
120,
filter_size=(1, 1),
nonlinearity=rectify,
name="feat_map"))
maskm = batch_norm(
layers.Conv2DLayer(prely,
100,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
mask_rep = batch_norm(layers.Conv2DLayer(maskm,
1,
filter_size=(1, 1),
nonlinearity=None),
beta=None,
gamma=None)
mask_map = SoftThresPerc(mask_rep,
perc=90.0,
alpha=0.5,
beta=init.Constant(0.1),
tight=100.0,
name="mask_map")
layer = ChInnerProdMerge(feat_map, mask_map, name="encoder")
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = layers.Deconv2DLayer(layer,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
glblf = batch_norm(
layers.Conv2DLayer(prely,
100,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
glblf = layers.Pool2DLayer(glblf,
pool_size=(20, 20),
stride=20,
mode='average_inc_pad')
glblf = batch_norm(
layers.Conv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(layers.Conv2DLayer(glblf,
3,
filter_size=(1, 1),
nonlinearity=rectify),
name="global_feature")
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = layers.Upscale2DLayer(glblf, scale_factor=20)
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
48,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
48,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
48,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = layers.Deconv2DLayer(glblf,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
layer = layers.ElemwiseSumLayer([layer, glblf])
network = ReshapeLayer(layer, ([0], -1))
mask_var = lasagne.layers.get_output(mask_map)
output_var = lasagne.layers.get_output(network)
return network, input_var, mask_var, output_var
def build_model(vocab_size,
doc_var,
qry_var,
doc_mask_var,
qry_mask_var,
W_init=lasagne.init.Normal()):
l_doc_in = L.InputLayer(shape=(None, None, 1), input_var=doc_var)
l_qry_in = L.InputLayer(shape=(None, None, 1), input_var=qry_var)
l_doc_embed = L.EmbeddingLayer(l_doc_in, vocab_size, EMBED_DIM, W=W_init)
l_qry_embed = L.EmbeddingLayer(l_qry_in,
vocab_size,
EMBED_DIM,
W=l_doc_embed.W)
l_doc_mask = L.InputLayer(shape=(None, None), input_var=doc_mask_var)
l_qry_mask = L.InputLayer(shape=(None, None), input_var=qry_mask_var)
l_doc_fwd = L.LSTMLayer(l_doc_embed,
NUM_HIDDEN,
grad_clipping=GRAD_CLIP,
mask_input=l_doc_mask,
gradient_steps=GRAD_STEPS,
precompute_input=True)
l_doc_bkd = L.LSTMLayer(l_doc_embed,
NUM_HIDDEN,
grad_clipping=GRAD_CLIP,
mask_input=l_doc_mask,
gradient_steps=GRAD_STEPS,
precompute_input=True,
backwards=True)
l_qry_fwd = L.LSTMLayer(l_qry_embed,
NUM_HIDDEN,
grad_clipping=GRAD_CLIP,
mask_input=l_qry_mask,
gradient_steps=GRAD_STEPS,
precompute_input=True)
l_qry_bkd = L.LSTMLayer(l_qry_embed,
NUM_HIDDEN,
grad_clipping=GRAD_CLIP,
mask_input=l_qry_mask,
gradient_steps=GRAD_STEPS,
precompute_input=True,
backwards=True)
l_doc_fwd_slice = L.SliceLayer(l_doc_fwd, -1, 1)
l_doc_bkd_slice = L.SliceLayer(l_doc_bkd, 0, 1)
l_qry_fwd_slice = L.SliceLayer(l_qry_fwd, -1, 1)
l_qry_bkd_slice = L.SliceLayer(l_qry_bkd, 0, 1)
r = L.DenseLayer(L.ElemwiseSumLayer([l_doc_fwd_slice, l_doc_bkd_slice]),
num_units=NUM_HIDDEN,
nonlinearity=lasagne.nonlinearities.tanh)
u = L.DenseLayer(L.ElemwiseSumLayer([l_qry_fwd_slice, l_qry_bkd_slice]),
num_units=NUM_HIDDEN,
nonlinearity=lasagne.nonlinearities.tanh)
g = L.DenseLayer(L.concat([r, u], axis=1),
num_units=EMBED_DIM,
W=lasagne.init.GlorotNormal(),
nonlinearity=lasagne.nonlinearities.tanh)
l_out = L.DenseLayer(g,
num_units=vocab_size,
W=l_doc_embed.W.T,
nonlinearity=lasagne.nonlinearities.softmax,
b=None)
return l_out
def build_network_from_ae(classn):
input_var = T.tensor4('input_var');
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 120, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = layers.Pool2DLayer(layer, pool_size=(2,2), stride=2, mode='average_inc_pad');
layer = batch_norm(layers.Conv2DLayer(layer, 240, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 320, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = layers.Pool2DLayer(layer, pool_size=(2,2), stride=2, mode='average_inc_pad');
layer = batch_norm(layers.Conv2DLayer(layer, 640, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
prely = batch_norm(layers.Conv2DLayer(layer, 1024, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
featm = batch_norm(layers.Conv2DLayer(prely, 640, filter_size=(1,1), nonlinearity=leaky_rectify));
feat_map = batch_norm(layers.Conv2DLayer(featm, 100, filter_size=(1,1), nonlinearity=rectify, name="feat_map"));
maskm = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
mask_rep = batch_norm(layers.Conv2DLayer(maskm, 1, filter_size=(1,1), nonlinearity=None), beta=None, gamma=None);
mask_map = SoftThresPerc(mask_rep, perc=0.0, alpha=0.1, beta=init.Constant(0.5), tight=100.0, bias=-10, name="mask_map");
enlyr = ChInnerProdMerge(feat_map, mask_map, name="encoder");
layer = batch_norm(layers.Deconv2DLayer(enlyr, 1024, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 640, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 640, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 240, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 120, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.Deconv2DLayer(layer, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
glblf = batch_norm(layers.Conv2DLayer(prely, 128, filter_size=(1,1), nonlinearity=leaky_rectify));
glblf = layers.Pool2DLayer(glblf, pool_size=(5,5), stride=5, mode='average_inc_pad');
glblf = batch_norm(layers.Conv2DLayer(glblf, 64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
gllyr = batch_norm(layers.Conv2DLayer(glblf, 5, filter_size=(1,1), nonlinearity=rectify), name="global_feature");
glblf = batch_norm(layers.Deconv2DLayer(gllyr, 256, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(9,9), stride=5, crop=(2,2), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = layers.Deconv2DLayer(glblf, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
layer = layers.ElemwiseSumLayer([layer, glblf]);
network = ReshapeLayer(layer, ([0], -1));
layers.set_all_param_values(network, pickle.load(open(filename_model_ae, 'rb')));
mask_map.beta.set_value(np.float32(-10.0*mask_map.beta.get_value()));
# Adding more layers
aug_var = T.matrix('aug_var');
target_var = T.imatrix('targets');
add_a = layers.Conv2DLayer(enlyr, 320, filter_size=(1,1), nonlinearity=leaky_rectify);
add_b = layers.Conv2DLayer(add_a, 320, filter_size=(1,1), nonlinearity=leaky_rectify);
add_c = layers.Conv2DLayer(add_b, 320, filter_size=(1,1), nonlinearity=leaky_rectify);
add_d = layers.Conv2DLayer(add_c, 320, filter_size=(1,1), nonlinearity=leaky_rectify);
add_0 = layers.Pool2DLayer(add_d, pool_size=(15,15), stride=15, mode='average_inc_pad');
add_1 = layers.DenseLayer(add_0, 100, nonlinearity=leaky_rectify);
add_2 = layers.DenseLayer(gllyr, 320, nonlinearity=leaky_rectify);
add_3 = layers.DenseLayer(add_2, 320, nonlinearity=leaky_rectify);
add_4 = layers.DenseLayer(add_3, 100, nonlinearity=leaky_rectify);
aug_layer = layers.InputLayer(shape=(None, aug_fea_n), input_var=aug_var);
cat_layer = lasagne.layers.ConcatLayer([add_1, add_4, aug_layer], axis=1);
hidden_layer = layers.DenseLayer(cat_layer, 80, nonlinearity=leaky_rectify);
network = layers.DenseLayer(hidden_layer, classn, nonlinearity=sigmoid);
new_params = [add_a.W, add_a.b, add_b.W, add_b.b, add_c.W, add_c.b, add_d.W, add_d.b, add_1.W, add_1.b, add_2.W, add_2.b, add_3.W, add_3.b, add_4.W, add_4.b, hidden_layer.W, hidden_layer.b, network.W, network.b];
return network, new_params, input_var, aug_var, target_var;
def build_network_from_ae(classn):
input_var = T.tensor4('input_var');
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 120, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = layers.Pool2DLayer(layer, pool_size=(2,2), stride=2, mode='average_inc_pad');
layer = batch_norm(layers.Conv2DLayer(layer, 240, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 320, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = layers.Pool2DLayer(layer, pool_size=(2,2), stride=2, mode='average_inc_pad');
layer = batch_norm(layers.Conv2DLayer(layer, 640, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
prely = batch_norm(layers.Conv2DLayer(layer, 1024, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
featm = batch_norm(layers.Conv2DLayer(prely, 640, filter_size=(1,1), nonlinearity=leaky_rectify));
feat_map = batch_norm(layers.Conv2DLayer(featm, 100, filter_size=(1,1), nonlinearity=rectify, name="feat_map"));
maskm = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
mask_rep = batch_norm(layers.Conv2DLayer(maskm, 1, filter_size=(1,1), nonlinearity=None), beta=None, gamma=None);
mask_map = SoftThresPerc(mask_rep, perc=98.4, alpha=0.1, beta=init.Constant(0.5), tight=100.0, name="mask_map");
enlyr = ChInnerProdMerge(feat_map, mask_map, name="encoder");
layer = batch_norm(layers.Deconv2DLayer(enlyr, 1024, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 640, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 640, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 240, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 120, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.Deconv2DLayer(layer, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
glblf = batch_norm(layers.Conv2DLayer(prely, 128, filter_size=(1,1), nonlinearity=leaky_rectify));
glblf = layers.Pool2DLayer(glblf, pool_size=(5,5), stride=5, mode='average_inc_pad');
glblf = batch_norm(layers.Conv2DLayer(glblf, 64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
gllyr = batch_norm(layers.Conv2DLayer(glblf, 5, filter_size=(1,1), nonlinearity=rectify), name="global_feature");
glblf = batch_norm(layers.Deconv2DLayer(gllyr, 256, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(9,9), stride=5, crop=(2,2), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = layers.Deconv2DLayer(glblf, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
layer = layers.ElemwiseSumLayer([layer, glblf]);
network = ReshapeLayer(layer, ([0], -1));
layers.set_all_param_values(network, pickle.load(open(filename_model_ae, 'rb')));
old_params = layers.get_all_params(network, trainable=True);
# Adding more layers
aug_var = T.matrix('aug_var');
target_var = T.imatrix('targets');
add_a = batch_norm(layers.Conv2DLayer(enlyr, 320, filter_size=(1,1), nonlinearity=leaky_rectify));
add_b = batch_norm(layers.Conv2DLayer(add_a, 320, filter_size=(1,1), nonlinearity=leaky_rectify));
add_c = batch_norm(layers.Conv2DLayer(add_b, 320, filter_size=(1,1), nonlinearity=leaky_rectify));
add_d = batch_norm(layers.Conv2DLayer(add_c, 320, filter_size=(1,1), nonlinearity=leaky_rectify));
add_0 = layers.Pool2DLayer(add_d, pool_size=(25,25), stride=25, mode='average_inc_pad');
add_1 = batch_norm(layers.DenseLayer(add_0, 100, nonlinearity=leaky_rectify));
add_2 = batch_norm(layers.DenseLayer(gllyr, 320, nonlinearity=leaky_rectify));
add_3 = batch_norm(layers.DenseLayer(add_2, 320, nonlinearity=leaky_rectify));
add_4 = batch_norm(layers.DenseLayer(add_3, 100, nonlinearity=leaky_rectify));
aug_layer = layers.InputLayer(shape=(None, aug_fea_n), input_var=aug_var);
cat_layer = lasagne.layers.ConcatLayer([add_1, add_4, aug_layer], axis=1);
hidden_layer = layers.DenseLayer(cat_layer, 80, nonlinearity=leaky_rectify);
network = layers.DenseLayer(hidden_layer, classn, nonlinearity=sigmoid);
layers.set_all_param_values(network, pickle.load(open('model_vals/deep_conv_classification_alt48_luad10_skcm10_lr0.py_e32_cv0.pkl', 'rb')));
all_params = layers.get_all_params(network, trainable=True);
new_params = [x for x in all_params if x not in old_params];
return network, new_params, input_var, aug_var, target_var;
def build_1Dregression_v1(input_var=None,
input_width=None,
nin_units=12,
h_num_units=[64, 64],
h_grad_clip=1.0,
output_width=1):
"""
A stacked bidirectional RNN network for regression, alternating
with dense layers and merging of the two directions, followed by
a feature mean pooling in the time direction, with a linear
dim-reduction layer at the start
Args:
input_var (theano 3-tensor): minibatch of input sequence vectors
input_width (int): length of input sequences
nin_units (list): number of NIN features
h_num_units (int list): no. of units in hidden layer in each stack
from bottom to top
h_grad_clip (float): gradient clipping maximum value
output_width (int): size of output layer (e.g. =1 for 1D regression)
Returns:
output layer (Lasagne layer object)
"""
# Non-linearity hyperparameter
nonlin = lasagne.nonlinearities.LeakyRectify(leakiness=0.15)
# Input layer
l_in = LL.InputLayer(shape=(None, 22, input_width), input_var=input_var)
batchsize = l_in.input_var.shape[0]
# NIN-layer
l_in = LL.NINLayer(l_in,
num_units=nin_units,
nonlinearity=lasagne.nonlinearities.linear)
l_in_1 = LL.DimshuffleLayer(l_in, (0, 2, 1))
# RNN layers
for h in h_num_units:
# Forward layers
l_forward_0 = LL.RecurrentLayer(l_in_1,
nonlinearity=nonlin,
num_units=h,
backwards=False,
learn_init=True,
grad_clipping=h_grad_clip,
unroll_scan=True,
precompute_input=True)
l_forward_0a = LL.ReshapeLayer(l_forward_0, (-1, h))
l_forward_0b = LL.DenseLayer(l_forward_0a,
num_units=h,
nonlinearity=nonlin)
l_forward_0c = LL.ReshapeLayer(l_forward_0b,
(batchsize, input_width, h))
# Backward layers
l_backward_0 = LL.RecurrentLayer(l_in_1,
nonlinearity=nonlin,
num_units=h,
backwards=True,
learn_init=True,
grad_clipping=h_grad_clip,
unroll_scan=True,
precompute_input=True)
l_backward_0a = LL.ReshapeLayer(l_backward_0, (-1, h))
l_backward_0b = LL.DenseLayer(l_backward_0a,
num_units=h,
nonlinearity=nonlin)
l_backward_0c = LL.ReshapeLayer(l_backward_0b,
(batchsize, input_width, h))
l_in_1 = LL.ElemwiseSumLayer([l_forward_0c, l_backward_0c])
# Output layers
network_0a = LL.ReshapeLayer(l_in_1, (-1, h_num_units[-1]))
network_0b = LL.DenseLayer(network_0a,
num_units=output_width,
nonlinearity=nonlin)
network_0c = LL.ReshapeLayer(network_0b,
(batchsize, input_width, output_width))
output_net_1 = LL.FlattenLayer(network_0c, outdim=2)
output_net_2 = LL.FeaturePoolLayer(output_net_1,
pool_size=input_width,
pool_function=T.mean)
return output_net_2
def run_experiment(args):
import os
# set environment variables for theano
os.environ['THEANO_FLAGS'] = "lib.cnmem=" + str(args.mem) + ",device=gpu" + str(args.gpu)
import threading
import Queue
import inspect
import shutil
import time
import logging
import six
import collections
import itertools
import random
import numpy as np
import scipy
import theano
import theano.tensor as T
import lasagne
import lasagne.layers as ll
import lasagne.nonlinearities as ln
import parmesan
import layers
import utils
import cfdataset
#----------------------------------------------------------------
# Arguments and Settings
floatX = theano.config.floatX
logger = logging.getLogger()
np.random.seed(args.seed)
# copy file for reproducibility
dirname = utils.setup_logging(args.message, args.loglv)
script_src = os.path.abspath(inspect.getfile(inspect.currentframe()))
script_dst = os.path.join(dirname, os.path.split(script_src)[1])
shutil.copyfile(script_src, script_dst)
# print arguments
args_dict = collections.OrderedDict(sorted(vars(args).items()))
for k, v in six.iteritems(args_dict):
logger.info(" %20s: %s" % (k, v))
# get arguments
D_u, D_v = args.D_u, args.D_v
lr = args.lr
weight_decay = args.weight_decay
lookahead = args.lookahead
max_epoch = args.max_epoch
batch_size_u, batch_size_v = args.batch_size_u, args.batch_size_v
nonlin_enc = layers.get_nonlin(args.nonlin_enc)
nonlin_dec = layers.get_nonlin(args.nonlin_dec)
negative_ratio = args.negative_ratio
#----------------------------------------------------------------
# Dataset
dataset = cfdataset.CF_implicit_data(name=args.dataset)
N_u, N_v = dataset.N_users, dataset.N_items
T_matrix = dataset.T_matrix.astype(floatX)
R_matrix = dataset.R_matrix.astype(floatX)
R_negative_matrix = 1 - R_matrix
assert np.all(R_matrix == (T_matrix > 0.5))
assert np.all((R_negative_matrix == 1) == (T_matrix == 0))
R_test = dataset.R_latest
T_matrix[np.arange(N_u), R_test] = 0
R_matrix[np.arange(N_u), R_test] = 0
assert np.all(R_matrix == (T_matrix > 0.5))
R_matrix_for_test = R_matrix.copy()
R_valid = dataset.R_2nd_latest
T_matrix[np.arange(N_u), R_valid] = 0
R_matrix[np.arange(N_u), R_valid] = 0
assert np.all(R_matrix == (T_matrix > 0.5))
N_interaction = dataset.N_interaction - N_u * 2
assert np.all(R_valid != R_test)
assert np.all(R_matrix_for_test[np.arange(N_u), R_valid] == 1)
assert np.all(R_matrix_for_test[np.arange(N_u), R_test] == 0)
assert np.all(R_matrix[np.arange(N_u), R_valid] == 0)
assert np.all(R_matrix[np.arange(N_u), R_test] == 0)
assert np.all(T_matrix[np.arange(N_u), R_valid] == 0)
assert np.all(T_matrix[np.arange(N_u), R_test] == 0)
assert N_interaction == np.count_nonzero(R_matrix)
assert N_interaction + N_u == np.count_nonzero(R_matrix_for_test)
logger.info("%d users, %d items, %d training interactions (%d total, 2 * %d held out for validation and test)." % (N_u, N_v, N_interaction, dataset.N_interaction, N_u))
#----------------------------------------------------------------
# numpy variables
# encoded vectors
np_enc_u_h = np.zeros((N_u, D_u), dtype=floatX)
np_enc_v_h = np.zeros((N_v, D_v), dtype=floatX)
#----------------------------------------------------------------
# Symbolic variables
sym_lr = T.fscalar('lr')
sym_Ru_pos = T.fmatrix('Ru_pos')
sym_dr_Ru_pos = T.fscalar('dr_Ru_pos')
sym_uid_origin_pos = T.ivector('uid_origin_pos')
sym_uid_minibatch_pos = T.ivector('uid_minibatch_pos')
sym_Ru_neg = T.fmatrix('Ru_neg')
sym_dr_Ru_neg = T.fscalar('dr_Ru_neg')
sym_uid_origin_neg = T.ivector('uid_origin_neg')
sym_uid_minibatch_neg = T.ivector('uid_minibatch_neg')
sym_Rv = T.fmatrix('Rv')
sym_dr_Rv = T.fscalar('dr_Rv')
sym_vid_origin_pos = T.ivector('vid_origin_pos')
sym_vid_minibatch_pos = T.ivector('vid_minibatch_pos')
sym_vid_origin_neg = T.ivector('vid_origin_neg')
sym_vid_minibatch_neg = T.ivector('vid_minibatch_neg')
sym_R_minibatch = T.fvector('R_minibatch')
#----------------------------------------------------------------
# Model setup (training model)
logger.info("Setting up model ...")
# Input layers
l_in_Ru_pos = ll.InputLayer((None, N_v), input_var=sym_Ru_pos, name='l_in_Ru_pos')
l_in_uid_origin_pos = ll.InputLayer((None,), input_var=sym_uid_origin_pos, name='l_in_uid_origin_pos')
l_in_uid_minibatch_pos = ll.InputLayer((None,), input_var=sym_uid_minibatch_pos, name='l_in_uid_minibatch_pos')
l_in_Ru_neg = ll.InputLayer((None, N_v), input_var=sym_Ru_neg, name='l_in_Ru_neg')
l_in_uid_origin_neg = ll.InputLayer((None,), input_var=sym_uid_origin_neg, name='l_in_uid_origin_neg')
l_in_uid_minibatch_neg = ll.InputLayer((None,), input_var=sym_uid_minibatch_neg, name='l_in_uid_minibatch_neg')
l_in_Rv = ll.InputLayer((None, N_u), input_var=sym_Rv, name='l_in_Rv')
l_in_vid_origin_pos = ll.InputLayer((None,), input_var=sym_vid_origin_pos, name='l_in_vid_origin_pos')
l_in_vid_minibatch_pos = ll.InputLayer((None,), input_var=sym_vid_minibatch_pos, name='l_in_vid_minibatch_pos')
l_in_vid_origin_neg = ll.InputLayer((None,), input_var=sym_vid_origin_neg, name='l_in_vid_origin_neg')
l_in_vid_minibatch_neg = ll.InputLayer((None,), input_var=sym_vid_minibatch_neg, name='l_in_vid_minibatch_neg')
# Dropout layers
l_in_Ru_pos = ll.DropoutLayer(l_in_Ru_pos, p=sym_dr_Ru_pos, rescale=False, name='Dropout-l_in_Ru_pos')
l_in_Ru_neg = ll.DropoutLayer(l_in_Ru_neg, p=sym_dr_Ru_neg, rescale=False, name='Dropout-l_in_Ru_neg')
l_in_Rv = ll.DropoutLayer(l_in_Rv, p=sym_dr_Rv, rescale=False, name='Dropout-l_in_Rv')
# User encoder model h(Ru)
l_enc_u_h_pos = ll.DenseLayer(l_in_Ru_pos, num_units=D_u, nonlinearity=nonlin_enc, name='l_enc_u_h_pos')
l_enc_u_h_neg = ll.DenseLayer(l_in_Ru_neg, num_units=D_u, nonlinearity=nonlin_enc, W=l_enc_u_h_pos.W, b=l_enc_u_h_pos.b, name='l_enc_u_h_neg')
# Item encoder model h(Rv)
l_enc_v_h = ll.DenseLayer(l_in_Rv, num_units=D_v, nonlinearity=nonlin_enc, name='l_enc_v_h')
# User decoder model s(h(Ru))
l_dec_u_s_pos = layers.SimpleDecodeLayer([l_enc_u_h_pos, l_in_vid_origin_pos, l_in_uid_minibatch_pos], num_units=N_v, nonlinearity=None, name='l_dec_u_s_pos')
l_dec_u_s_neg = layers.SimpleDecodeLayer([l_enc_u_h_neg, l_in_vid_origin_neg, l_in_uid_minibatch_neg], num_units=N_v, V=l_dec_u_s_pos.V, Q=l_dec_u_s_pos.Q, b=l_dec_u_s_pos.b, nonlinearity=None, name='l_dec_u_s_neg')
l_dec_u_s_all = ll.ConcatLayer([l_dec_u_s_pos ,l_dec_u_s_neg], axis=0)
# Item decoder model s(h(Rv))
l_dec_v_s_pos = layers.SimpleDecodeLayer([l_enc_v_h, l_in_uid_origin_pos, l_in_vid_minibatch_pos], num_units=N_u, nonlinearity=None, name='l_dec_v_s_pos')
l_dec_v_s_neg = layers.SimpleDecodeLayer([l_enc_v_h, l_in_uid_origin_neg, l_in_vid_minibatch_neg], num_units=N_u, V=l_dec_v_s_pos.V, Q=l_dec_v_s_pos.Q, b=l_dec_v_s_pos.b, nonlinearity=None, name='l_dec_v_s_neg')
l_dec_v_s_all = ll.ConcatLayer([l_dec_v_s_pos ,l_dec_v_s_neg], axis=0)
# Likelihood model p(R)
l_uv_s_train = ll.ElemwiseSumLayer([l_dec_u_s_all, l_dec_v_s_all], name='l_uv_s_train')
l_r_train = ll.NonlinearityLayer(l_uv_s_train, nonlinearity=ln.sigmoid, name='l_r_train')
l_uv_s_test = ll.ElemwiseSumLayer([l_dec_u_s_pos, l_dec_v_s_pos], name='l_uv_s_test')
l_r_test = ll.NonlinearityLayer(l_uv_s_test, nonlinearity=ln.sigmoid, name='l_r_test')
#----------------------------------------------------------------
# Likelihood and RMSE
# training
p_r_train, = ll.get_output([l_r_train], deterministic=False)
log_p_r = T.mean(parmesan.distributions.log_bernoulli(sym_R_minibatch, p_r_train, eps=1e-6))
regularization = lasagne.regularization.regularize_network_params([l_r_train], lasagne.regularization.l2)
cost_function = - log_p_r + weight_decay * regularization
SE_train = T.sum(T.sqr(sym_R_minibatch - p_r_train))
# test
sym_enc_u_h = T.fmatrix('enc_u_h')
sym_enc_v_h = T.fmatrix('enc_v_h')
enc_u_h_out, enc_v_h_out = ll.get_output([l_enc_u_h_pos, l_enc_v_h], deterministic=True)
p_r_test, = ll.get_output([l_r_test], inputs={l_enc_u_h_pos:sym_enc_u_h, l_enc_v_h:sym_enc_v_h}, deterministic=True)
test_scores = p_r_test.reshape((-1, 101))
ranking = test_scores.argsort()[:,::-1].argmin(axis=1)
#----------------------------------------------------------------
# Gradients
clip_grad = 1
max_norm = 5
params = ll.get_all_params([l_r_train,], trainable=True)
for p in params:
logger.debug("%s: %s" % (p, p.get_value().shape))
grads = T.grad(cost_function, params)
mgrads = lasagne.updates.total_norm_constraint(grads, max_norm=max_norm)
cgrads = [T.clip(g, -clip_grad, clip_grad) for g in mgrads]
#updates = lasagne.updates.adam(cgrads, params, beta1=0.9, beta2=0.999, epsilon=1e-4, learning_rate=sym_lr)
updates, sym_vars_list = utils.adam(cgrads, params, beta1=0.9, beta2=0.999, epsilon=1e-4, learning_rate=sym_lr)
# moving average
params_avg=[]
for param in params:
value = param.get_value(borrow=True)
params_avg.append(theano.shared(np.zeros(value.shape, dtype=value.dtype),
broadcastable=param.broadcastable,
name=param.name + '_avg'))
avg_updates = [(a, a + 0.01 * (p - a)) for p, a in zip(params, params_avg)]
avg_givens = [(p, a) for p, a in zip(params, params_avg)]
all_updates = updates.items() + avg_updates
#----------------------------------------------------------------
# Compile
# training function
logger.info("Compiling train_model ...")
train_model = theano.function(
inputs=[sym_lr,
sym_uid_origin_pos, sym_uid_minibatch_pos, sym_vid_origin_pos, sym_vid_minibatch_pos,
sym_uid_origin_neg, sym_uid_minibatch_neg, sym_vid_origin_neg, sym_vid_minibatch_neg,
sym_Ru_pos, sym_Ru_neg, sym_Rv,
sym_R_minibatch, sym_dr_Ru_pos, sym_dr_Ru_neg, sym_dr_Rv],
outputs=[log_p_r, SE_train],
updates=all_updates,
)
# encoders
logger.info("Compiling encode_model ...")
u_encode_model = theano.function(inputs=[sym_Ru_pos], outputs=enc_u_h_out)
v_encode_model = theano.function(inputs=[sym_Rv], outputs=enc_v_h_out)
u_encode_avg_model = theano.function(inputs=[sym_Ru_pos], outputs=enc_u_h_out, givens=avg_givens, on_unused_input='ignore')
v_encode_avg_model = theano.function(inputs=[sym_Rv], outputs=enc_v_h_out, givens=avg_givens, on_unused_input='ignore')
# test function
logger.info("Compiling test_model ...")
test_model = theano.function(
inputs=[sym_uid_origin_pos, sym_uid_minibatch_pos, sym_vid_origin_pos, sym_vid_minibatch_pos, sym_enc_u_h, sym_enc_v_h],
outputs=[ranking],
)
test_avg_model = theano.function(
inputs=[sym_uid_origin_pos, sym_uid_minibatch_pos, sym_vid_origin_pos, sym_vid_minibatch_pos, sym_enc_u_h, sym_enc_v_h],
outputs=[ranking],
givens=avg_givens, on_unused_input='ignore',
)
#----------------------------------------------------------------
# Predict function
def compute_hidden_for(for_which_set='test', avg_model=False):
assert for_which_set in ['valid', 'test']
if for_which_set == 'valid':
R_matrix_cond = R_matrix
else:
R_matrix_cond = R_matrix_for_test
# preconpute hidden representation
u_end = 0
while u_end < N_u:
u_start, u_end = u_end, min(u_end + batch_size_u, N_u)
# create user mini-batch
u_batch_ids = np.arange(u_start, u_end).astype('int32')
# create conditionals
Ru_minibatch = R_matrix_cond[u_batch_ids,:]
# encode
if avg_model:
np_enc_u_h[u_batch_ids] = u_encode_avg_model(Ru_minibatch)
else:
np_enc_u_h[u_batch_ids] = u_encode_model(Ru_minibatch)
v_end = 0
while v_end < N_v:
v_start, v_end = v_end, min(v_end + batch_size_v, N_v)
# create item mini-batch
v_batch_ids = np.arange(v_start, v_end).astype('int32')
# create conditionals
Rv_minibatch = R_matrix_cond[:,v_batch_ids].T
# encode
if avg_model:
np_enc_v_h[v_batch_ids] = v_encode_avg_model(Rv_minibatch)
else:
np_enc_v_h[v_batch_ids] = v_encode_model(Rv_minibatch)
def predict_once(which_set='test', avg_model=False):
assert which_set in ['valid', 'test']
if which_set == 'valid':
R_predict = R_valid
else:
R_predict = R_test
# test statistics
rankings = []
# loop users
u_end = 0
while u_end < N_u:
u_start, u_end = u_end, min(u_end + batch_size_u, N_u)
# create user mini-batch and item mini-batch
u_batch_ids = np.arange(u_start, u_end).astype('int32')
vid_negative = np.asarray([np.random.choice(np.where(row)[0], 100, replace=False) for row in R_negative_matrix[u_batch_ids]], dtype='int32')
vid = np.concatenate([R_predict[u_batch_ids].reshape(-1,1), vid_negative], axis=1).flatten()
uid_origin = np.repeat(u_batch_ids, 101)
uid_minibatch = uid_origin - u_start
# get encoded vectors
Ru_encoded = np_enc_u_h[u_batch_ids]
if avg_model:
rankings_minibatch, = test_avg_model(uid_origin, uid_minibatch, vid, vid, Ru_encoded, np_enc_v_h)
else:
rankings_minibatch, = test_model(uid_origin, uid_minibatch, vid, vid, Ru_encoded, np_enc_v_h)
rankings.append(rankings_minibatch)
rankings = np.concatenate(rankings)
HR = np.mean(rankings < 10)
NDCG = np.mean((rankings < 10) / np.log2(rankings + 2))
return HR, NDCG
def predict(which_set='test', avg=10, avg_model=False):
compute_hidden_for(for_which_set=which_set, avg_model=avg_model)
HR_list = []
NDCG_list = []
for i in range(avg):
hr, ndcg = predict_once(which_set=which_set, avg_model=avg_model)
HR_list.append(hr)
NDCG_list.append(ndcg)
HR_mean = np.mean(HR_list)
NDCG_mean = np.mean(NDCG_list)
HR_std = np.std(HR_list)
NDCG_std = np.std(NDCG_list)
# print info after test finished
eval_msg = which_set if not avg_model else which_set + ' (avg model)'
logger.critical("%-20s HR = %.3f +- %.3f, NDCG = %.3f +- %.3f." % (eval_msg, HR_mean, HR_std, NDCG_mean, NDCG_std))
return HR_mean, NDCG_mean
#----------------------------------------------------------------
# Training
best_valid_result = - np.inf
best_model = None
best_auxiliary = None
n_epocs_without_improvement = 0
minibatch_queue = Queue.Queue(maxsize=10)
# function for preparing minibatches
def prepare_minibatch(minibatch_list):
# loop mini-batches
for u_batch_ids, v_batch_ids in minibatch_list:
Rv_minibatch = R_matrix[:,v_batch_ids].T
Rv_minibatch[:,u_batch_ids] = 0
Ru_minibatch_neg = R_matrix[u_batch_ids,:]
#Ru_minibatch_neg[:,v_batch_ids] = 0
# create training samples mini-batch
T_matrix_minibatch = T_matrix[np.ix_(u_batch_ids, v_batch_ids)]
T_matrix_minibatch_sparse = scipy.sparse.coo_matrix(T_matrix_minibatch)
n_interactions_minibatch = T_matrix_minibatch_sparse.count_nonzero()
Ru_minibatch_pos = ((T_matrix[u_batch_ids[T_matrix_minibatch_sparse.row]] < T_matrix_minibatch_sparse.data.reshape(n_interactions_minibatch, 1)) & (T_matrix[u_batch_ids[T_matrix_minibatch_sparse.row]] > 0)).astype(floatX)
uid_minibatch_pos = np.arange(n_interactions_minibatch).astype('int32')
uid_origin_pos = u_batch_ids[T_matrix_minibatch_sparse.row]
vid_minibatch_pos = T_matrix_minibatch_sparse.col
vid_origin_pos = v_batch_ids[vid_minibatch_pos]
R_matrix_negative_minibatch = 1 - R_matrix[np.ix_(u_batch_ids, v_batch_ids)]
R_matrix_negative_minibatch_sparse = scipy.sparse.coo_matrix(R_matrix_negative_minibatch)
n_negative_total = R_matrix_negative_minibatch_sparse.count_nonzero()
assert n_negative_total + n_interactions_minibatch == u_batch_ids.size * v_batch_ids.size
choice_negative = np.random.choice(n_negative_total, min(n_negative_total, np.int(n_interactions_minibatch * negative_ratio)), replace=False)
uid_minibatch_neg = R_matrix_negative_minibatch_sparse.row[choice_negative]
uid_origin_neg = u_batch_ids[uid_minibatch_neg]
vid_minibatch_neg = R_matrix_negative_minibatch_sparse.col[choice_negative]
vid_origin_neg = v_batch_ids[vid_minibatch_neg]
R_minibatch = np.concatenate([np.ones_like(T_matrix_minibatch_sparse.data), R_matrix_negative_minibatch_sparse.data[choice_negative] * 0])
n_pred_step = R_minibatch.shape[0]
if n_pred_step == 0:
raise ValueError('No interactions in this minibatch.')
dr_Ru_pos = min(max(1 - 2 * np.random.rand(), 0), 0.8)
dr_Ru_neg = 0.2
dr_Rv = min(max(1 - 2 * np.random.rand(), 0), 0.8)
# package everything into a tuple
data_minibatch_package = (
uid_origin_pos, uid_minibatch_pos, vid_origin_pos, vid_minibatch_pos,
uid_origin_neg, uid_minibatch_neg, vid_origin_neg, vid_minibatch_neg,
Ru_minibatch_pos, Ru_minibatch_neg, Rv_minibatch,
R_minibatch, dr_Ru_pos, dr_Ru_neg, dr_Rv)
# enqueue
minibatch_queue.put((n_pred_step, data_minibatch_package))
logger.warning("Training started.")
# loop epoch
for epoch in range(1, 1+max_epoch):
epoch_start_time = time.time()
# training statistics
LL_epoch, SE_epoch= 0, 0
n_pred_epoch = 0
u_order = np.array_split(np.random.permutation(N_u).astype('int32'), N_u // batch_size_u + 1)
v_order = np.array_split(np.random.permutation(N_v).astype('int32'), N_v // batch_size_v + 1)
minibatch_order = list(itertools.product(u_order, v_order))
random.shuffle(minibatch_order)
n_threads = 5
n_minibatch_thread = len(minibatch_order) // n_threads + 1
for t in range(n_threads):
thr = threading.Thread(target=prepare_minibatch, args=(minibatch_order[t*n_minibatch_thread:(t+1)*n_minibatch_thread],))
thr.setDaemon(True)
thr.start()
for step in range(len(minibatch_order)):
n_pred_step, data_minibatch_package = minibatch_queue.get()
# update parameters and calculate likelihood and RMSE
LL_step, SE_step = train_model(lr, *data_minibatch_package)
minibatch_queue.task_done()
LL_epoch += LL_step * n_pred_step
SE_epoch += SE_step
n_pred_epoch += n_pred_step
assert minibatch_queue.qsize() == 0
# print info after epoch finished
LL_epoch /= n_pred_epoch
RMSE_epoch = np.sqrt(SE_epoch/n_pred_epoch)
epoch_end_time = time.time()
logger.info("Epoch %d, training RMSE = %f, LL = %f (%d training ratings). Elapsed time %.1fs." % (epoch, RMSE_epoch, LL_epoch, n_pred_epoch, epoch_end_time-epoch_start_time))
# validation
HR_valid, NDCG_valid = predict('valid')
HR_test, NDCG_test = predict('test')
HR_test, NDCG_test = predict('test', avg_model=True)
# termination
#if NDCG_valid > best_valid_result:
if HR_valid > best_valid_result:
n_epocs_without_improvement = 0
#best_valid_result = NDCG_valid
best_valid_result = HR_valid
best_model = ll.get_all_param_values([l_r_train,], trainable=True)
best_auxiliary = utils.get_all_shvar_values(sym_vars_list)
logger.debug("New best model found!")
else:
n_epocs_without_improvement += 1
if n_epocs_without_improvement >= lookahead:
ll.set_all_param_values([l_r_train,], best_model, trainable=True)
utils.set_all_shvar_values(sym_vars_list, best_auxiliary)
if lr > 1e-5:
n_epocs_without_improvement = 0
lr /= 4
logger.error("Learning rate = %f now." % lr)
else:
logger.error("Training finished.")
break
#----------------------------------------------------------------
# Test
HR_test, NDCG_test = predict('test')
HR_test, NDCG_test = predict('test', avg_model=True)
#----------------------------------------------------------------
# Summarization
for k, v in six.iteritems(args_dict):
logger.info(" %20s: %s" % (k, v))
def build_network_from_ae(classn):
input_var = T.tensor4('input_var')
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var)
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
120,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = layers.Pool2DLayer(layer,
pool_size=(2, 2),
stride=2,
mode='average_inc_pad')
layer = batch_norm(
layers.Conv2DLayer(layer,
240,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = layers.Pool2DLayer(layer,
pool_size=(2, 2),
stride=2,
mode='average_inc_pad')
layer = batch_norm(
layers.Conv2DLayer(layer,
640,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
prely = batch_norm(
layers.Conv2DLayer(layer,
1024,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
featm = batch_norm(
layers.Conv2DLayer(prely,
640,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
feat_map = batch_norm(
layers.Conv2DLayer(featm,
100,
filter_size=(1, 1),
nonlinearity=rectify,
name="feat_map"))
mask_map = feat_map
enlyr = feat_map
layer = batch_norm(
layers.Deconv2DLayer(enlyr,
1024,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
640,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
640,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
240,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
120,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = layers.Deconv2DLayer(layer,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
glblf = batch_norm(
layers.Conv2DLayer(prely,
128,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
glblf = layers.Pool2DLayer(glblf,
pool_size=(5, 5),
stride=5,
mode='average_inc_pad')
glblf = batch_norm(
layers.Conv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
gllyr = batch_norm(layers.Conv2DLayer(glblf,
5,
filter_size=(1, 1),
nonlinearity=rectify),
name="global_feature")
glblf = batch_norm(
layers.Deconv2DLayer(gllyr,
256,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(9, 9),
stride=5,
crop=(2, 2),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = layers.Deconv2DLayer(glblf,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
layer = layers.ElemwiseSumLayer([layer, glblf])
network = ReshapeLayer(layer, ([0], -1))
layers.set_all_param_values(network,
pickle.load(open(filename_model_ae, 'rb')))
old_params = layers.get_all_params(network, trainable=True)
# Adding more layers
aug_var = T.matrix('aug_var')
target_var = T.imatrix('targets')
add_a = batch_norm(
layers.Conv2DLayer(enlyr,
320,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
add_b = batch_norm(
layers.Conv2DLayer(add_a,
320,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
add_c = batch_norm(
layers.Conv2DLayer(add_b,
320,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
add_d = batch_norm(
layers.Conv2DLayer(add_c,
320,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
add_0 = layers.Pool2DLayer(add_d,
pool_size=(15, 15),
stride=15,
mode='average_inc_pad')
add_1 = batch_norm(
layers.DenseLayer(add_0, 100, nonlinearity=leaky_rectify))
add_2 = batch_norm(
layers.DenseLayer(gllyr, 320, nonlinearity=leaky_rectify))
add_3 = batch_norm(
layers.DenseLayer(add_2, 320, nonlinearity=leaky_rectify))
add_4 = batch_norm(
layers.DenseLayer(add_3, 100, nonlinearity=leaky_rectify))
aug_layer = layers.InputLayer(shape=(None, aug_fea_n), input_var=aug_var)
cat_layer = lasagne.layers.ConcatLayer([add_1, add_4, aug_layer], axis=1)
hidden_layer = layers.DenseLayer(cat_layer, 80, nonlinearity=leaky_rectify)
network = layers.DenseLayer(hidden_layer, classn, nonlinearity=sigmoid)
all_params = layers.get_all_params(network, trainable=True)
new_params = [x for x in all_params if x not in old_params]
return network, new_params, input_var, aug_var, target_var
def build_network(self, K, vocab_size, W_init):
l_docin = L.InputLayer(shape=(None, None, 1), input_var=self.inps[0])
l_doctokin = L.InputLayer(shape=(None, None), input_var=self.inps[1])
l_qin = L.InputLayer(shape=(None, None, 1), input_var=self.inps[2])
l_qtokin = L.InputLayer(shape=(None, None), input_var=self.inps[3])
l_docmask = L.InputLayer(shape=(None, None), input_var=self.inps[6])
l_qmask = L.InputLayer(shape=(None, None), input_var=self.inps[7])
l_tokin = L.InputLayer(shape=(None, MAX_WORD_LEN),
input_var=self.inps[8])
l_tokmask = L.InputLayer(shape=(None, MAX_WORD_LEN),
input_var=self.inps[9])
l_featin = L.InputLayer(shape=(None, None), input_var=self.inps[11])
doc_shp = self.inps[1].shape
qry_shp = self.inps[3].shape
l_docembed = L.EmbeddingLayer(l_docin,
input_size=vocab_size,
output_size=self.embed_dim,
W=W_init) # B x N x 1 x DE
l_doce = L.ReshapeLayer(
l_docembed, (doc_shp[0], doc_shp[1], self.embed_dim)) # B x N x DE
l_qembed = L.EmbeddingLayer(l_qin,
input_size=vocab_size,
output_size=self.embed_dim,
W=l_docembed.W)
l_qembed = L.ReshapeLayer(
l_qembed, (qry_shp[0], qry_shp[1], self.embed_dim)) # B x N x DE
l_fembed = L.EmbeddingLayer(l_featin, input_size=2,
output_size=2) # B x N x 2
if self.train_emb == 0:
l_docembed.params[l_docembed.W].remove('trainable')
# char embeddings
if self.use_chars:
l_lookup = L.EmbeddingLayer(l_tokin, self.num_chars,
2 * self.char_dim) # T x L x D
l_fgru = L.GRULayer(l_lookup,
self.char_dim,
grad_clipping=GRAD_CLIP,
mask_input=l_tokmask,
gradient_steps=GRAD_STEPS,
precompute_input=True,
only_return_final=True)
l_bgru = L.GRULayer(l_lookup,
2 * self.char_dim,
grad_clipping=GRAD_CLIP,
mask_input=l_tokmask,
gradient_steps=GRAD_STEPS,
precompute_input=True,
backwards=True,
only_return_final=True) # T x 2D
l_fwdembed = L.DenseLayer(l_fgru,
self.embed_dim / 2,
nonlinearity=None) # T x DE/2
l_bckembed = L.DenseLayer(l_bgru,
self.embed_dim / 2,
nonlinearity=None) # T x DE/2
l_embed = L.ElemwiseSumLayer([l_fwdembed, l_bckembed], coeffs=1)
l_docchar_embed = IndexLayer([l_doctokin, l_embed]) # B x N x DE/2
l_qchar_embed = IndexLayer([l_qtokin, l_embed]) # B x Q x DE/2
l_doce = L.ConcatLayer([l_doce, l_docchar_embed], axis=2)
l_qembed = L.ConcatLayer([l_qembed, l_qchar_embed], axis=2)
l_fwd_q = L.GRULayer(l_qembed,
self.nhidden,
grad_clipping=GRAD_CLIP,
mask_input=l_qmask,
gradient_steps=GRAD_STEPS,
precompute_input=True,
only_return_final=False)
l_bkd_q = L.GRULayer(l_qembed,
self.nhidden,
grad_clipping=GRAD_CLIP,
mask_input=l_qmask,
gradient_steps=GRAD_STEPS,
precompute_input=True,
backwards=True,
only_return_final=False)
l_q = L.ConcatLayer([l_fwd_q, l_bkd_q]) # B x Q x 2D
q = L.get_output(l_q) # B x Q x 2D
q = q[T.arange(q.shape[0]), self.inps[12], :] # B x 2D
l_qs = [l_q]
for i in range(K - 1):
l_fwd_doc_1 = L.GRULayer(l_doce,
self.nhidden,
grad_clipping=GRAD_CLIP,
mask_input=l_docmask,
gradient_steps=GRAD_STEPS,
precompute_input=True)
l_bkd_doc_1 = L.GRULayer(l_doce, self.nhidden, grad_clipping=GRAD_CLIP,
mask_input=l_docmask, gradient_steps=GRAD_STEPS, precompute_input=True, \
backwards=True)
l_doc_1 = L.concat([l_fwd_doc_1, l_bkd_doc_1],
axis=2) # B x N x DE
l_fwd_q_1 = L.GRULayer(l_qembed,
self.nhidden,
grad_clipping=GRAD_CLIP,
mask_input=l_qmask,
gradient_steps=GRAD_STEPS,
precompute_input=True)
l_bkd_q_1 = L.GRULayer(l_qembed,
self.nhidden,
grad_clipping=GRAD_CLIP,
mask_input=l_qmask,
gradient_steps=GRAD_STEPS,
precompute_input=True,
backwards=True)
l_q_c_1 = L.ConcatLayer([l_fwd_q_1, l_bkd_q_1],
axis=2) # B x Q x DE
l_qs.append(l_q_c_1)
qd = L.get_output(l_q_c_1) # B x Q x DE
dd = L.get_output(l_doc_1) # B x N x DE
M = T.batched_dot(dd, qd.dimshuffle((0, 2, 1))) # B x N x Q
alphas = T.nnet.softmax(
T.reshape(M, (M.shape[0] * M.shape[1], M.shape[2])))
alphas_r = T.reshape(alphas, (M.shape[0],M.shape[1],M.shape[2]))* \
self.inps[7][:,np.newaxis,:] # B x N x Q
alphas_r = alphas_r / alphas_r.sum(axis=2)[:, :,
np.newaxis] # B x N x Q
q_rep = T.batched_dot(alphas_r, qd) # B x N x DE
l_q_rep_in = L.InputLayer(shape=(None, None, 2 * self.nhidden),
input_var=q_rep)
l_doc_2_in = L.ElemwiseMergeLayer([l_doc_1, l_q_rep_in], T.mul)
l_doce = L.dropout(l_doc_2_in, p=self.dropout) # B x N x DE
if self.use_feat:
l_doce = L.ConcatLayer([l_doce, l_fembed], axis=2) # B x N x DE+2
l_fwd_doc = L.GRULayer(l_doce,
self.nhidden,
grad_clipping=GRAD_CLIP,
mask_input=l_docmask,
gradient_steps=GRAD_STEPS,
precompute_input=True)
l_bkd_doc = L.GRULayer(l_doce, self.nhidden, grad_clipping=GRAD_CLIP,
mask_input=l_docmask, gradient_steps=GRAD_STEPS, precompute_input=True, \
backwards=True)
l_doc = L.concat([l_fwd_doc, l_bkd_doc], axis=2)
d = L.get_output(l_doc) # B x N x 2D
p = T.batched_dot(d, q) # B x N
pm = T.nnet.softmax(p) * self.inps[10]
pm = pm / pm.sum(axis=1)[:, np.newaxis]
final = T.batched_dot(pm, self.inps[4])
dv = L.get_output(l_doc, deterministic=True) # B x N x 2D
p = T.batched_dot(dv, q) # B x N
pm = T.nnet.softmax(p) * self.inps[10]
pm = pm / pm.sum(axis=1)[:, np.newaxis]
final_v = T.batched_dot(pm, self.inps[4])
return final, final_v, l_doc, l_qs, l_docembed.W
def build_model(hyparams,
vmap,
log,
nclasses=2,
batchsize=None,
invar=None,
maskvar=None,
maxlen=MAXLEN):
embedding_dim = hyparams.embedding_dim
nhidden = hyparams.nhidden
bidirectional = hyparams.bidirectional
pool = hyparams.pool
grad_clip = hyparams.grad_clip
init = hyparams.init
net = OrderedDict()
V = len(vmap)
W = lasagne.init.Normal()
gate_params = layer.recurrent.Gate(W_in=lasagne.init.Orthogonal(),
W_hid=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.))
cell_params = layer.recurrent.Gate(
W_in=lasagne.init.Orthogonal(),
W_hid=lasagne.init.Orthogonal(),
W_cell=None,
b=lasagne.init.Constant(0.),
nonlinearity=lasagne.nonlinearities.tanh)
net['input'] = layer.InputLayer((batchsize, maxlen), input_var=invar)
net['mask'] = layer.InputLayer((batchsize, maxlen), input_var=maskvar)
ASSUME = {net['input']: (200, 140), net['mask']: (200, 140)}
net['emb'] = layer.EmbeddingLayer(net['input'],
input_size=V,
output_size=embedding_dim,
W=W)
net['fwd1'] = layer.LSTMLayer(net['emb'],
num_units=nhidden,
grad_clipping=grad_clip,
nonlinearity=lasagne.nonlinearities.tanh,
mask_input=net['mask'],
ingate=gate_params,
forgetgate=gate_params,
cell=cell_params,
outgate=gate_params,
learn_init=True)
if bidirectional:
net['bwd1'] = layer.LSTMLayer(net['emb'],
num_units=nhidden,
grad_clipping=grad_clip,
nonlinearity=lasagne.nonlinearities.tanh,
mask_input=net['mask'],
ingate=gate_params,
forgetgate=gate_params,
cell=cell_params,
outgate=gate_params,
learn_init=True,
backwards=True)
if pool == 'mean':
def tmean(a, b):
agg = theano.tensor.add(a, b)
agg /= 2.
return agg
net['pool'] = layer.ElemwiseMergeLayer([net['fwd1'], net['bwd1']],
tmean)
elif pool == 'sum':
net['pool'] = layer.ElemwiseSumLayer([net['fwd1'], net['bwd1']])
else:
net['pool'] = layer.ConcatLayer([net['fwd1'], net['bwd1']])
else:
net['pool'] = layer.ConcatLayer([net['fwd1']])
net['dropout1'] = layer.DropoutLayer(net['pool'], p=0.5)
if init == 'identity':
gate_params2 = layer.recurrent.Gate(W_in=np.eye(nhidden,
dtype=np.float32),
W_hid=np.eye(nhidden,
dtype=np.float32),
b=lasagne.init.Constant(0.))
cell_params2 = layer.recurrent.Gate(
W_in=np.eye(nhidden, dtype=np.float32),
W_hid=np.eye(nhidden, dtype=np.float32),
W_cell=None,
b=lasagne.init.Constant(0.),
nonlinearity=lasagne.nonlinearities.rectify)
net['fwd2'] = layer.LSTMLayer(net['dropout1'],
num_units=nhidden,
grad_clipping=grad_clip,
nonlinearity=lasagne.nonlinearities.tanh,
mask_input=net['mask'],
ingate=gate_params2,
forgetgate=gate_params2,
cell=cell_params2,
outgate=gate_params2,
learn_init=True,
only_return_final=True)
else:
net['fwd2'] = layer.LSTMLayer(net['dropout1'],
num_units=nhidden,
grad_clipping=grad_clip,
nonlinearity=lasagne.nonlinearities.tanh,
mask_input=net['mask'],
ingate=gate_params,
forgetgate=gate_params,
cell=cell_params,
outgate=gate_params,
learn_init=True,
only_return_final=True)
net['dropout2'] = layer.DropoutLayer(net['fwd2'], p=0.6)
net['softmax'] = layer.DenseLayer(
net['dropout2'],
num_units=nclasses,
nonlinearity=lasagne.nonlinearities.softmax)
logstr = '========== MODEL ========== \n'
logstr += 'vocab size: %d\n' % V
logstr += 'embedding dim: %d\n' % embedding_dim
logstr += 'nhidden: %d\n' % nhidden
logstr += 'pooling: %s\n' % pool
for lname, lyr in net.items():
logstr += '%s %s\n' % (lname, str(get_output_shape(lyr, ASSUME)))
logstr += '=========================== \n'
print logstr
log.write(logstr)
log.flush()
return net
def build_rnn_net(input_var=None,
input_width=None,
input_dim=None,
nin_units=80,
h_num_units=[64, 64],
h_grad_clip=1.0,
output_width=1):
"""
A stacked bidirectional RNN network for regression, alternating
with dense layers and merging of the two directions, followed by
a feature mean pooling in the time direction, with a linear
dim-reduction layer at the start
add dropout for generalizations
Args:
input_var (theano 3-tensor): minibatch of input sequence vectors
input_width (int): length of input sequences
nin_units (list): number of NIN features
h_num_units (int list): no. of units in hidden layer in each stack
from bottom to top
h_grad_clip (float): gradient clipping maximum value
output_width (int): size of output layer (e.g. =1 for 1D regression)
Returns:
output layer (Lasagne layer object)
"""
# Non-linearity hyperparameter
leaky_ratio = 0.3
nonlin = lasagne.nonlinearities.LeakyRectify(leakiness=leaky_ratio)
# Input layer
l_in = LL.InputLayer(shape=(None, input_width, input_dim),
input_var=input_var)
batchsize = l_in.input_var.shape[0]
# NIN-layer
#l_in_1 = LL.NINLayer(l_in, num_units=nin_units,
#nonlinearity=lasagne.nonlinearities.linear)
l_in_1 = l_in
#l_in_d = LL.DropoutLayer(l_in, p = 0.8) Do not use drop out now, for the first rnn layer is 256
# currently, we do not drop features
# RNN layers
# dropout in the first two (total three) or three (total five) layers
counter = -1
drop_ends = 2
for h in h_num_units:
counter += 1
# Forward layers
l_forward_0 = LL.RecurrentLayer(
l_in_1,
nonlinearity=nonlin,
num_units=h,
W_in_to_hid=lasagne.init.Normal(0.01, 0),
#W_in_to_hid=lasagne.init.He(initializer, math.sqrt(2/(1+0.15**2))),
W_hid_to_hid=lasagne.init.Orthogonal(
math.sqrt(2 / (1 + leaky_ratio**2))),
backwards=False,
learn_init=True,
grad_clipping=h_grad_clip,
#gradient_steps = 20,
unroll_scan=True,
precompute_input=True)
l_forward_0a = LL.ReshapeLayer(l_forward_0, (-1, h))
if (counter < drop_ends and counter % 2 != 0):
l_forward_0a = LL.DropoutLayer(l_forward_0a, p=0.2)
else:
l_forward_0a = l_forward_0a
l_forward_0b = LL.DenseLayer(l_forward_0a,
num_units=h,
nonlinearity=nonlin)
l_forward_0c = LL.ReshapeLayer(l_forward_0b,
(batchsize, input_width, h))
l_forward_out = l_forward_0c
# Backward layers
l_backward_0 = LL.RecurrentLayer(
l_in_1,
nonlinearity=nonlin,
num_units=h,
W_in_to_hid=lasagne.init.Normal(0.01, 0),
#W_in_to_hid=lasagne.init.He(initializer, math.sqrt(2/(1+0.15**2))),
W_hid_to_hid=lasagne.init.Orthogonal(
math.sqrt(2 / (1 + leaky_ratio**2))),
backwards=True,
learn_init=True,
grad_clipping=h_grad_clip,
#gradient_steps = 20,
unroll_scan=True,
precompute_input=True)
l_backward_0a = LL.ReshapeLayer(l_backward_0, (-1, h))
if (counter < drop_ends and counter % 2 == 0):
l_backward_0a = LL.DropoutLayer(l_backward_0a, p=0.2)
else:
l_backward_0a = l_backward_0a
l_backward_0b = LL.DenseLayer(l_backward_0a,
num_units=h,
nonlinearity=nonlin)
l_backward_0c = LL.ReshapeLayer(l_backward_0b,
(batchsize, input_width, h))
l_backward_out = l_backward_0c
l_in_1 = LL.ElemwiseSumLayer([l_forward_out, l_backward_out])
# Output layers
network_0a = LL.DenseLayer(l_in_1,
num_units=1,
num_leading_axes=2,
nonlinearity=nonlin)
output_net = LL.FlattenLayer(network_0a, outdim=2)
return output_net
def build_RNN(self,
n_hidden_list=(100, ),
bidirectional=False,
addDenseLayers=False,
seed=int(time.time()),
debug=False,
logger=logger_RNNtools):
# some inspiration from http://colinraffel.com/talks/hammer2015recurrent.pdf
# if debug:
# logger_RNNtools.debug('\nInputs:');
# logger_RNNtools.debug(' X.shape: %s', self.X[0].shape)
# logger_RNNtools.debug(' X[0].shape: %s %s %s \n%s', self.X[0][0].shape, type(self.X[0][0]),
# type(self.X[0][0][0]), self.X[0][0][:5])
#
# logger_RNNtools.debug('Targets: ');
# logger_RNNtools.debug(' Y.shape: %s', self.Y.shape)
# logger_RNNtools.debug(' Y[0].shape: %s %s %s \n%s', self.Y[0].shape, type(self.Y[0]), type(self.Y[0][0]),
# self.Y[0][:5])
# logger_RNNtools.debug('Layers: ')
# fix these at initialization because it allows for compiler opimizations
num_output_units = self.num_output_units
num_features = self.num_features
batch_size = self.batch_size
audio_inputs = self.audio_inputs_var
audio_masks = self.audio_masks_var #set MATRIX, not iMatrix!! Otherwise all mask calculations are done by CPU, and everything will be ~2x slowed down!! Also in general_tools.generate_masks()
valid_indices = self.audio_valid_indices_var
net = {}
# net['l1_in_valid'] = L.InputLayer(shape=(batch_size, None), input_var=valid_indices)
# shape = (batch_size, batch_max_seq_length, num_features)
net['l1_in'] = L.InputLayer(shape=(batch_size, None, num_features),
input_var=audio_inputs)
# We could do this and set all input_vars to None, but that is slower -> fix batch_size and num_features at initialization
# batch_size, n_time_steps, n_features = net['l1_in'].input_var.shape
# This input will be used to provide the network with masks.
# Masks are matrices of shape (batch_size, n_time_steps);
net['l1_mask'] = L.InputLayer(shape=(batch_size, None),
input_var=audio_masks)
if debug:
get_l_in = L.get_output(net['l1_in'])
l_in_val = get_l_in.eval({net['l1_in'].input_var: self.X})
# logger_RNNtools.debug(l_in_val)
logger_RNNtools.debug(' l_in size: %s', l_in_val.shape)
get_l_mask = L.get_output(net['l1_mask'])
l_mask_val = get_l_mask.eval(
{net['l1_mask'].input_var: self.masks})
# logger_RNNtools.debug(l_in_val)
logger_RNNtools.debug(' l_mask size: %s', l_mask_val.shape)
n_batch, n_time_steps, n_features = net['l1_in'].input_var.shape
logger_RNNtools.debug(
" n_batch: %s | n_time_steps: %s | n_features: %s", n_batch,
n_time_steps, n_features)
## LSTM parameters
# All gates have initializers for the input-to-gate and hidden state-to-gate
# weight matrices, the cell-to-gate weight vector, the bias vector, and the nonlinearity.
# The convention is that gates use the standard sigmoid nonlinearity,
# which is the default for the Gate class.
gate_parameters = L.recurrent.Gate(W_in=lasagne.init.Orthogonal(),
W_hid=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.))
cell_parameters = L.recurrent.Gate(
W_in=lasagne.init.Orthogonal(),
W_hid=lasagne.init.Orthogonal(),
# Setting W_cell to None denotes that no cell connection will be used.
W_cell=None,
b=lasagne.init.Constant(0.),
# By convention, the cell nonlinearity is tanh in an LSTM.
nonlinearity=lasagne.nonlinearities.tanh)
# generate layers of stacked LSTMs, possibly bidirectional
net['l2_lstm'] = []
for i in range(len(n_hidden_list)):
n_hidden = n_hidden_list[i]
if i == 0: input = net['l1_in']
else: input = net['l2_lstm'][i - 1]
nextForwardLSTMLayer = L.recurrent.LSTMLayer(
input,
n_hidden,
# We need to specify a separate input for masks
mask_input=net['l1_mask'],
# Here, we supply the gate parameters for each gate
ingate=gate_parameters,
forgetgate=gate_parameters,
cell=cell_parameters,
outgate=gate_parameters,
# We'll learn the initialization and use gradient clipping
learn_init=True,
grad_clipping=100.)
net['l2_lstm'].append(nextForwardLSTMLayer)
if bidirectional:
input = net['l2_lstm'][-1]
# Use backward LSTM
# The "backwards" layer is the same as the first,
# except that the backwards argument is set to True.
nextBackwardLSTMLayer = L.recurrent.LSTMLayer(
input,
n_hidden,
ingate=gate_parameters,
mask_input=net['l1_mask'],
forgetgate=gate_parameters,
cell=cell_parameters,
outgate=gate_parameters,
learn_init=True,
grad_clipping=100.,
backwards=True)
net['l2_lstm'].append(nextBackwardLSTMLayer)
# if debug:
# # Backwards LSTM
# get_l_lstm_back = theano.function([net['l1_in'].input_var, net['l1_mask'].input_var],
# L.get_output(net['l2_lstm'][-1]))
# l_lstmBack_val = get_l_lstm_back(self.X, self.masks)
# logger_RNNtools.debug(' l_lstm_back size: %s', l_lstmBack_val.shape)
# We'll combine the forward and backward layer output by summing.
# Merge layers take in lists of layers to merge as input.
# The output of l_sum will be of shape (n_batch, max_n_time_steps, n_features)
net['l2_lstm'].append(
L.ElemwiseSumLayer(
[net['l2_lstm'][-2], net['l2_lstm'][-1]]))
# we need to convert (batch_size, seq_length, num_features) to (batch_size * seq_length, num_features) because Dense networks can't deal with 2 unknown sizes
net['l3_reshape'] = L.ReshapeLayer(net['l2_lstm'][-1],
(-1, n_hidden_list[-1]))
# if debug:
# get_l_reshape = theano.function([net['l1_in'].input_var, net['l1_mask'].input_var],
# L.get_output(net['l3_reshape']))
# l_reshape_val = get_l_reshape(self.X, self.masks)
# logger.debug(' l_reshape size: %s', l_reshape_val.shape)
#
# if debug:
# # Forwards LSTM
# get_l_lstm = theano.function([net['l1_in'].input_var, net['l1_mask'].input_var],
# L.get_output(net['l2_lstm'][-1]))
# l_lstm_val = get_l_lstm(self.X, self.masks)
# logger_RNNtools.debug(' l2_lstm size: %s', l_lstm_val.shape);
if addDenseLayers:
net['l4_dense'] = L.DenseLayer(
net['l3_reshape'],
nonlinearity=lasagne.nonlinearities.rectify,
num_units=256)
dropoutLayer = L.DropoutLayer(net['l4_dense'], p=0.3)
net['l5_dense'] = L.DenseLayer(
dropoutLayer,
nonlinearity=lasagne.nonlinearities.rectify,
num_units=64)
# Now we can apply feed-forward layers as usual for classification
net['l6_dense'] = L.DenseLayer(
net['l5_dense'],
num_units=num_output_units,
nonlinearity=lasagne.nonlinearities.softmax)
else:
# Now we can apply feed-forward layers as usual for classification
net['l6_dense'] = L.DenseLayer(
net['l3_reshape'],
num_units=num_output_units,
nonlinearity=lasagne.nonlinearities.softmax)
# # Now, the shape will be (n_batch * n_timesteps, num_output_units). We can then reshape to
# # n_batch to get num_output_units values for each timestep from each sequence
net['l7_out_flattened'] = L.ReshapeLayer(net['l6_dense'],
(-1, num_output_units))
net['l7_out'] = L.ReshapeLayer(net['l6_dense'],
(batch_size, -1, num_output_units))
net['l7_out_valid_basic'] = L.SliceLayer(net['l7_out'],
indices=valid_indices,
axis=1)
net['l7_out_valid'] = L.ReshapeLayer(
net['l7_out_valid_basic'], (batch_size, -1, num_output_units))
net['l7_out_valid_flattened'] = L.ReshapeLayer(
net['l7_out_valid_basic'], (-1, num_output_units))
if debug:
get_l_out = theano.function(
[net['l1_in'].input_var, net['l1_mask'].input_var],
L.get_output(net['l7_out']))
l_out = get_l_out(self.X, self.masks)
# this only works for batch_size == 1
get_l_out_valid = theano.function(
[audio_inputs, audio_masks, valid_indices],
L.get_output(net['l7_out_valid']))
try:
l_out_valid = get_l_out_valid(self.X, self.masks,
self.valid_frames)
logger_RNNtools.debug('\n\n\n l_out: %s | l_out_valid: %s',
l_out.shape, l_out_valid.shape)
except:
logger_RNNtools.warning(
"batchsize not 1, get_valid not working")
if debug: self.print_network_structure(net)
self.network_lout = net['l7_out_flattened']
self.network_lout_batch = net['l7_out']
self.network_lout_valid = net['l7_out_valid']
self.network_lout_valid_flattened = net['l7_out_valid_flattened']
self.network = net
def resblock(net_in,
filters,
kernel_size,
stride=1,
num_groups=1,
preactivated=True):
# Preactivation
net_pre = batch_norm(net_in)
net_pre = l.NonlinearityLayer(net_pre,
nonlinearity=nonlinearity(cfg.NONLINEARITY))
# Preactivated shortcut?
if preactivated:
net_sc = net_pre
else:
net_sc = net_in
# Stride size
if cfg.MAX_POOLING:
s = 1
else:
s = stride
# First Convolution (alwys has preactivated input)
net = batch_norm(
l.Conv2DLayer(net_pre,
num_filters=filters,
filter_size=kernel_size,
pad='same',
stride=s,
num_groups=num_groups,
W=initialization(cfg.NONLINEARITY),
nonlinearity=nonlinearity(cfg.NONLINEARITY)))
# Optional pooling layer
if cfg.MAX_POOLING and stride > 1:
net = l.MaxPool2DLayer(net, pool_size=stride)
# Dropout Layer (we support different types of dropout)
if cfg.DROPOUT_TYPE == 'channels' and cfg.DROPOUT > 0.0:
net = l.dropout_channels(net, p=cfg.DROPOUT)
elif cfg.DROPOUT_TYPE == 'location' and cfg.DROPOUT > 0.0:
net = l.dropout_location(net, p=cfg.DROPOUT)
elif cfg.DROPOUT > 0.0:
net = l.DropoutLayer(net, p=cfg.DROPOUT)
# Second Convolution
net = l.Conv2DLayer(net,
num_filters=filters,
filter_size=kernel_size,
pad='same',
stride=1,
num_groups=num_groups,
W=initialization(cfg.NONLINEARITY),
nonlinearity=None)
# Shortcut Layer
if not l.get_output_shape(net) == l.get_output_shape(net_sc):
shortcut = l.Conv2DLayer(net_sc,
num_filters=filters,
filter_size=1,
pad='same',
stride=s,
W=initialization(cfg.NONLINEARITY),
nonlinearity=None,
b=None)
# Optional pooling layer
if cfg.MAX_POOLING and stride > 1:
shortcut = l.MaxPool2DLayer(shortcut, pool_size=stride)
else:
shortcut = net_sc
# Merge Layer
out = l.ElemwiseSumLayer([net, shortcut])
return out
def make_model():
image = ll.InputLayer((BS, CH, IH, IW), name='step1.image')
h_read_init = ll.InputLayer(
(HS, ),
lasagne.utils.create_param(li.Uniform(), (HS, ),
name='step1.tensor.h_read_init'),
name='step1.h_read_init')
h_read_init.add_param(h_read_init.input_var, (HS, ))
h_write_init = ll.InputLayer(
(HS, ),
lasagne.utils.create_param(li.Uniform(), (HS, ),
name='step1.tensor.h_write_init'),
name='step1.h_write_init')
h_write_init.add_param(h_write_init.input_var, (HS, ))
h_read = ll.ExpressionLayer(h_read_init,
lambda t: T.tile(T.reshape(t, (1, HS)),
(BS, 1)), (BS, HS),
name='step1.h_read')
h_write = ll.ExpressionLayer(h_write_init,
lambda t: T.tile(T.reshape(t, (1, HS)),
(BS, 1)), (BS, HS),
name='step1.h_write')
canvas = ll.InputLayer(
(BS, CH, IH, IW),
lasagne.utils.create_param(li.Constant(0.0), (BS, CH, IH, IW),
name='step1.tensor.canvas'),
name='step1.canvas')
image_prev = ll.NonlinearityLayer(canvas,
ln.sigmoid,
name='step1.image_prev')
image_error = ll.ElemwiseSumLayer([image, image_prev],
coeffs=[1, -1],
name='step1.image_error')
image_stack = ll.ConcatLayer([image, image_error],
name='step1.image_stack')
read_params = ll.DenseLayer(h_write,
6,
nonlinearity=None,
name='step1.read_params')
read_window = advanced_layers.AttentionLayer([read_params, image_stack],
(WH, WW),
name='step1.read_window')
read_flat = ll.FlattenLayer(read_window, name='step1.read_flat')
read_code = ll.ConcatLayer([read_flat, h_write], name='step1.read_code')
read_code_sequence = ll.ReshapeLayer(read_code,
(BS, 1, read_code.output_shape[-1]),
name='step1.read_code_sequence')
read_rnn = ll.GRULayer(
read_code_sequence,
HS,
only_return_final=True,
hid_init=h_read,
name='step1.read_rnn',
)
sample_mean = ll.DenseLayer(read_rnn,
ENC_NDIM,
nonlinearity=None,
name='step1.sample_mean')
sample_logvar2 = ll.DenseLayer(read_rnn,
ENC_NDIM,
nonlinearity=None,
name='step1.sample_logvar2')
sample = advanced_layers.SamplingLayer([sample_mean, sample_logvar2],
ENC_VAR,
name='step1.sample')
write_code = ll.DenseLayer(sample, HS, name='step1.write_code')
write_code_sequence = ll.ReshapeLayer(write_code,
(BS, 1, write_code.output_shape[-1]),
name='step1.write_code_sequence')
write_rnn = ll.GRULayer(
write_code_sequence,
HS,
only_return_final=True,
hid_init=h_write,
name='step1.write_rnn',
)
write_window_flat = ll.DenseLayer(write_rnn,
CH * WH * WW,
name='step1.write_window_flat')
write_window = ll.ReshapeLayer(write_window_flat, (BS, CH, WH, WW),
name='step1.write_window')
write_params = ll.DenseLayer(h_write,
6,
nonlinearity=None,
name='step1.write_params')
write_image = advanced_layers.AttentionLayer([write_params, write_window],
(IH, IW),
name='step1.write_image')
canvas_next = ll.ElemwiseSumLayer([canvas, write_image],
name='step1.canvas_next')
def rename(name):
if name is None:
return None
step, real_name = name.split('.', 1)
step = int(step[4:])
return 'step%d.%s' % (step + 1, real_name)
for step in xrange(1, TIME_ROUNDS):
sample_random_variable_next = sample.random_stream.normal(
sample.input_shapes[0],
std=sample.variation_coeff,
)
sample_random_variable_next.name = 'step%d.sample.random_variable' % \
(step + 1)
canvas, canvas_next = (canvas_next,
utils.modified_copy(
canvas_next,
modify={
h_read:
read_rnn,
h_write:
write_rnn,
canvas:
canvas_next,
sample.random_stream:
sample.random_stream,
sample.random_variable:
sample_random_variable_next,
},
rename=rename,
))
h_read = read_rnn
h_write = write_rnn
read_rnn = utils.layer_by_name(canvas_next,
'step%d.read_rnn' % (step + 1))
write_rnn = utils.layer_by_name(canvas_next,
'step%d.write_rnn' % (step + 1))
sample = utils.layer_by_name(canvas_next, 'step%d.sample' % (step + 1))
output = ll.NonlinearityLayer(canvas_next, ln.sigmoid, name='output')
return output
def resblock(net_in, filters, kernel_size, stride=1, preactivated=True, block_id=1, name=''):
# Show input shape
#log.p(("\t\t" + name + " IN SHAPE:", l.get_output_shape(net_in)), new_line=False)
# Pre-activation
if block_id > 1:
net_pre = l.NonlinearityLayer(net_in, nonlinearity=nl.rectify)
else:
net_pre = net_in
# Pre-activated shortcut?
if preactivated:
net_in = net_pre
# Bottleneck Convolution
if stride > 1:
net_pre = l.batch_norm(l.Conv2DLayer(net_pre,
num_filters=l.get_output_shape(net_pre)[1],
filter_size=1,
pad='same',
stride=1,
nonlinearity=nl.rectify))
# First Convolution
net = l.batch_norm(l.Conv2DLayer(net_pre,
num_filters=l.get_output_shape(net_pre)[1],
filter_size=kernel_size,
pad='same',
stride=1,
nonlinearity=nl.rectify))
# Pooling layer
if stride > 1:
net = l.MaxPool2DLayer(net, pool_size=(stride, stride))
# Dropout Layer
net = l.DropoutLayer(net)
# Second Convolution
net = l.batch_norm(l.Conv2DLayer(net,
num_filters=filters,
filter_size=kernel_size,
pad='same',
stride=1,
nonlinearity=None))
# Shortcut Layer
if not l.get_output_shape(net) == l.get_output_shape(net_in):
# Average pooling
shortcut = l.Pool2DLayer(net_in, pool_size=(stride, stride), stride=stride, mode='average_exc_pad')
# Shortcut convolution
shortcut = l.batch_norm(l.Conv2DLayer(shortcut,
num_filters=filters,
filter_size=1,
pad='same',
stride=1,
nonlinearity=None))
else:
# Shortcut = input
shortcut = net_in
# Merge Layer
out = l.ElemwiseSumLayer([net, shortcut])
# Show output shape
#log.p(("OUT SHAPE:", l.get_output_shape(out), "LAYER:", len(l.get_all_layers(out)) - 1))
return out
def get_actor(self, avg=False):
suf = '_avg' if avg else ''
iw = L.InputLayer(shape=(None, self.args.sw)) # (100, 24)
ew = L.EmbeddingLayer(
iw,
self.args.vw,
self.args.nw,
name='ew' + suf,
W=HeNormal() if not avg else Constant()) # (100, 24, 256)
ew.params[ew.W].remove('regularizable')
if 'w' in self.args.freeze:
ew.params[ew.W].remove('trainable')
# for access from outside
if not avg:
self.Ew = ew.W
# char embedding with CNN/LSTM
ic = L.InputLayer(shape=(None, self.args.sw,
self.args.max_len)) # (100, 24, 32)
ec = self.get_char2word(ic, avg) # (100, 24, 256)
it = L.InputLayer(shape=(None, self.args.st))
et = L.EmbeddingLayer(it,
self.args.vt,
self.args.nt,
name='et' + suf,
W=HeNormal() if not avg else Constant())
et.params[et.W].remove('regularizable')
il = L.InputLayer(shape=(None, self.args.sl))
el = L.EmbeddingLayer(il,
self.args.vl,
self.args.nl,
name='el' + suf,
W=HeNormal() if not avg else Constant())
el.params[el.W].remove('regularizable')
to_concat = []
if self.args.type == 'word':
to_concat.append(ew)
elif self.args.type == 'char':
to_concat.append(ec)
elif self.args.type == 'both':
to_concat += [ew, ec]
elif self.args.type == 'mix':
to_concat.append(L.ElemwiseSumLayer([ew, ec]))
if not self.args.untagged:
to_concat.append(et)
if not self.args.unlabeled:
to_concat.append(el)
x = L.concat(to_concat, axis=2) # (100, 24, 64+16+16)
# additional:
# get the more compact representation of each token by its word, tag and label,
# before putting into the hidden layer
if self.args.squeeze:
x = L.DenseLayer(
x,
num_units=self.args.squeeze,
name='h0' + suf,
num_leading_axes=2,
W=HeNormal('relu') if not avg else Constant()) # (100, 24, 64)
h1 = L.DenseLayer(
x,
num_units=self.args.nh1,
name='h1' + suf,
W=HeNormal('relu') if not avg else Constant()) # (100, 512)
h1 = L.dropout(h1, self.args.p1)
h2 = L.DenseLayer(
h1,
num_units=self.args.nh2,
name='h2' + suf,
W=HeNormal('relu') if not avg else Constant()) # (100, 256)
h2 = L.dropout(h2, self.args.p2)
h3 = L.DenseLayer(h2,
num_units=self.args.nh3,
name='h3' + suf,
W=HeNormal() if not avg else Constant(),
nonlinearity=softmax) # (100, 125) num of actions
return iw, ic, it, il, h3
def build_segmenter_jet_2():
# downsample down to a small region, then upsample all the way back up, using jet architecture
# recreate basic FCN-8s structure (though more aptly 1s here since we upsample back to the original input size)
# this jet will have another conv layer in the final upsample
inp = ll.InputLayer(shape=(None, 1, None, None), name='input')
conv1 = ll.Conv2DLayer(inp,
num_filters=32,
filter_size=(3, 3),
pad='same',
W=Orthogonal(),
nonlinearity=rectify,
name='conv1_1')
bn1 = ll.BatchNormLayer(conv1, name='bn1')
conv2 = ll.Conv2DLayer(bn1,
num_filters=64,
filter_size=(3, 3),
pad='same',
W=Orthogonal(),
nonlinearity=rectify,
name='conv1_2')
bn2 = ll.BatchNormLayer(conv2, name='bn2')
mp1 = ll.MaxPool2DLayer(bn2, 2, stride=2, name='mp1') # 2x downsample
conv3 = ll.Conv2DLayer(mp1,
num_filters=128,
filter_size=(3, 3),
pad='same',
W=Orthogonal(),
nonlinearity=rectify,
name='conv2_1')
bn3 = ll.BatchNormLayer(conv3, name='bn3')
conv4 = ll.Conv2DLayer(bn3,
num_filters=128,
filter_size=(3, 3),
pad='same',
W=Orthogonal(),
nonlinearity=rectify,
name='conv2_2')
bn4 = ll.BatchNormLayer(conv4, name='bn4')
mp2 = ll.MaxPool2DLayer(bn4, 2, stride=2, name='mp2') # 4x downsample
conv5 = ll.Conv2DLayer(mp2,
num_filters=128,
filter_size=(3, 3),
pad='same',
W=Orthogonal(),
nonlinearity=rectify,
name='conv3_1')
bn5 = ll.BatchNormLayer(conv5, name='bn5')
conv6 = ll.Conv2DLayer(bn5,
num_filters=128,
filter_size=(3, 3),
pad='same',
W=Orthogonal(),
nonlinearity=rectify,
name='conv3_2')
bn6 = ll.BatchNormLayer(conv6, name='bn6')
mp3 = ll.MaxPool2DLayer(bn6, 2, stride=2, name='mp3') # 8x downsample
conv7 = ll.Conv2DLayer(mp3,
num_filters=128,
filter_size=(3, 3),
pad='same',
W=Orthogonal(),
nonlinearity=rectify,
name='conv4_1')
bn7 = ll.BatchNormLayer(conv7, name='bn7')
conv8 = ll.Conv2DLayer(bn7,
num_filters=128,
filter_size=(3, 3),
pad='same',
W=Orthogonal(),
nonlinearity=rectify,
name='conv4_2')
bn8 = ll.BatchNormLayer(conv8, name='bn8')
# f 68 s 8
# now start the upsample
## FIRST UPSAMPLE PREDICTION (akin to FCN-32s)
conv_f8 = ll.Conv2DLayer(bn8,
num_filters=2,
filter_size=(3, 3),
pad='same',
W=Orthogonal(),
nonlinearity=linear,
name='conv_8xpred')
softmax_8 = Softmax4D(conv_f8, name='4dsoftmax_8x')
up8 = ll.Upscale2DLayer(
softmax_8, 8,
name='upsample_8x') # take loss here, 8x upsample from 8x downsample
## COMBINE BY UPSAMPLING SOFTMAX 8 AND PRED ON CONV 6
softmax_4up = ll.Upscale2DLayer(softmax_8, 2,
name='upsample_4x_pre') # 4x downsample
conv_f6 = ll.Conv2DLayer(bn6,
num_filters=2,
filter_size=(3, 3),
pad='same',
W=Orthogonal(),
nonlinearity=linear,
name='conv_4xpred')
softmax_4 = Softmax4D(conv_f6, name='4dsoftmax_4x') # 4x downsample
softmax_4_merge = ll.ElemwiseSumLayer([softmax_4, softmax_4up],
coeffs=0.5,
name='softmax_4_merge')
up4 = ll.Upscale2DLayer(
softmax_4_merge, 4,
name='upsample_4x') # take loss here, 4x upsample from 4x downsample
## COMBINE BY UPSAMPLING SOFTMAX_4_MERGE AND CONV 4
softmax_2up = ll.Upscale2DLayer(softmax_4_merge, 2,
name='upsample_2x_pre') # 2x downsample
conv_f4 = ll.Conv2DLayer(bn4,
num_filters=2,
filter_size=(3, 3),
pad='same',
W=Orthogonal(),
nonlinearity=linear,
name='conv_2xpred')
softmax_2 = Softmax4D(conv_f4, name='4dsoftmax_2x')
softmax_2_merge = ll.ElemwiseSumLayer([softmax_2, softmax_2up],
coeffs=0.5,
name='softmax_2_merge')
up2 = ll.Upscale2DLayer(
softmax_2_merge, 2, name='upsample_2x'
) # final loss here, 2x upsample from a 2x downsample
## COMBINE BY UPSAMPLING SOFTMAX_2_MERGE AND CONV 2
softmax_1up = ll.Upscale2DLayer(
softmax_2_merge, 2,
name='upsample_1x_pre') # 1x downsample (i.e. no downsample)
conv_f2 = ll.Conv2DLayer(bn2,
num_filters=2,
filter_size=(3, 3),
pad='same',
W=Orthogonal(),
nonlinearity=linear,
name='conv_1xpred')
softmax_1 = Softmax4D(conv_f2, name='4dsoftmax_1x')
softmax_1_merge = ll.ElemwiseSumLayer([softmax_1, softmax_1up],
coeffs=0.5,
name='softmax_1_merge')
# this is where up1 would go but that doesn't make any sense
return [up8, up4, up2, softmax_1_merge]
def run_experiment(args):
import os
# set environment variables for theano
os.environ['THEANO_FLAGS'] = "lib.cnmem=" + str(
args.mem) + ",device=gpu" + str(args.gpu)
import inspect
import shutil
import time
import logging
import six
import collections
import numpy as np
import scipy
import theano
import theano.tensor as T
import lasagne
import lasagne.layers as ll
import lasagne.nonlinearities as ln
import parmesan
import layers
import utils
import cfdataset
#----------------------------------------------------------------
# Arguments and Settings
floatX = theano.config.floatX
logger = logging.getLogger()
np.random.seed(args.seed)
# copy file for reproducibility
dirname = utils.setup_logging(args.message, args.loglv)
script_src = os.path.abspath(inspect.getfile(inspect.currentframe()))
script_dst = os.path.join(dirname, os.path.split(script_src)[1])
shutil.copyfile(script_src, script_dst)
# print arguments
args_dict = collections.OrderedDict(sorted(vars(args).items()))
for k, v in six.iteritems(args_dict):
logger.info(" %20s: %s" % (k, v))
# get arguments
D_u, D_v = args.D_u, args.D_v
J_u, J_v = args.J_u, args.J_v
lr = args.lr
alpha = args.alpha
weight_decay = args.weight_decay
n_step = args.n_step
lookahead = args.lookahead
max_epoch = args.max_epoch
batch_size_u, batch_size_v = args.batch_size_u, args.batch_size_v
share_params = not args.no_share_params
nonlin_enc = layers.get_nonlin(args.nonlin_enc)
nonlin_dec = layers.get_nonlin(args.nonlin_dec)
#----------------------------------------------------------------
# Dataset
dataset = cfdataset.CFdata(name=args.dataset, split=args.split)
N_stars = dataset.N_stars
N_u, N_v = dataset.N_users, dataset.N_items
R_train = dataset.R_train # int (3 * N_train_rating)
R_test = dataset.R_test # int (3 * N_test_rating)
n_valid_split = np.int(dataset.N_train_rating / 20)
train_valid_perm = np.random.permutation(dataset.N_train_rating)
R_valid = R_train[:, train_valid_perm[:n_valid_split]]
R_train = R_train[:, train_valid_perm[n_valid_split:]]
R_matrix = dict()
R_matrix['train'] = scipy.sparse.coo_matrix(
(R_train[2], (R_train[0], R_train[1])),
shape=(N_u, N_v)).toarray().astype('int32')
R_matrix['valid'] = scipy.sparse.coo_matrix(
(R_valid[2], (R_valid[0], R_valid[1])),
shape=(N_u, N_v)).toarray().astype('int32')
R_matrix['test'] = scipy.sparse.coo_matrix(
(R_test[2], (R_test[0], R_test[1])),
shape=(N_u, N_v)).toarray().astype('int32')
N_rating = dict()
N_rating['train'] = dataset.N_train_rating - n_valid_split
N_rating['valid'] = n_valid_split
N_rating['test'] = dataset.N_test_rating
logger.info("%d users, %d items" % (N_u, N_v))
logger.info("%d training ratings, %d validation ratings, %d test ratings" %
(N_rating['train'], N_rating['valid'], N_rating['test']))
logger.info("%d-star scale" % N_stars)
#----------------------------------------------------------------
# numpy variables
# encoded vectors
np_enc_u_h = np.zeros((N_u, D_u), dtype=floatX)
np_enc_v_h = np.zeros((N_v, D_v), dtype=floatX)
#----------------------------------------------------------------
# Symbolic variables
sym_lr = T.fscalar('lr')
sym_Ru = T.imatrix('Ru')
sym_Rv = T.imatrix('Rv')
sym_dr_Ru = T.fscalar('dr_Ru')
sym_dr_Rv = T.fscalar('dr_Rv')
sym_uid_origin = T.ivector('uid_origin')
sym_uid_minibatch = T.ivector('uid_minibatch')
sym_vid_origin = T.ivector('vid_origin')
sym_vid_minibatch = T.ivector('vid_minibatch')
sym_R_minibatch = T.ivector('R_minibatch')
#----------------------------------------------------------------
# Model setup (training model)
logger.info("Setting up model ...")
# Input layers
l_in_Ru = ll.InputLayer((None, N_v), input_var=sym_Ru, name='l_in_Ru')
l_in_Rv = ll.InputLayer((None, N_u), input_var=sym_Rv, name='l_in_Rv')
l_in_uid_origin = ll.InputLayer((None, ),
input_var=sym_uid_origin,
name='l_in_uid_origin')
l_in_vid_origin = ll.InputLayer((None, ),
input_var=sym_vid_origin,
name='l_in_vid_origin')
l_in_uid_minibatch = ll.InputLayer((None, ),
input_var=sym_uid_minibatch,
name='l_in_uid_minibatch')
l_in_vid_minibatch = ll.InputLayer((None, ),
input_var=sym_vid_minibatch,
name='l_in_vid_minibatch')
# Dropout layers
l_in_Ru = ll.DropoutLayer(l_in_Ru,
p=sym_dr_Ru,
rescale=False,
name='Dropout-l_in_Ru')
l_in_Rv = ll.DropoutLayer(l_in_Rv,
p=sym_dr_Rv,
rescale=False,
name='Dropout-l_in_Rv')
# User encoder model h(Ru)
l_enc_u_h = layers.OneHotEncodeLayer(l_in_Ru,
num_units=D_u,
rank=J_u,
num_hots=N_stars,
share_params=share_params,
nonlinearity=None,
name='Dense-l_enc_u_h')
l_enc_u_h = ll.NonlinearityLayer(l_enc_u_h,
nonlinearity=nonlin_enc,
name='Nonlin-l_enc_u_h')
# Item encoder model h(Rv)
l_enc_v_h = layers.OneHotEncodeLayer(l_in_Rv,
num_units=D_v,
rank=J_v,
num_hots=N_stars,
share_params=share_params,
nonlinearity=None,
name='Dense-l_enc_v_h')
l_enc_v_h = ll.NonlinearityLayer(l_enc_v_h,
nonlinearity=nonlin_enc,
name='Nonlin-l_enc_v_h')
# User decoder model s(h(Ru))
l_dec_u_s = layers.OneHotDecodeLayer(
[l_enc_u_h, l_in_vid_origin, l_in_uid_minibatch],
num_units=N_v,
rank=J_u,
num_hots=N_stars,
share_params=share_params,
nonlinearity=None,
name='Dense-l_dec_u_s')
# Item decoder model s(h(Rv))
l_dec_v_s = layers.OneHotDecodeLayer(
[l_enc_v_h, l_in_uid_origin, l_in_vid_minibatch],
num_units=N_u,
rank=J_v,
num_hots=N_stars,
share_params=share_params,
nonlinearity=None,
name='Dense-l_dec_v_s')
# Likelihood model p(R)
l_uv_s = ll.ElemwiseSumLayer([l_dec_u_s, l_dec_v_s], name='l_uv_s')
l_r = ll.NonlinearityLayer(l_uv_s, nonlinearity=ln.softmax, name='l_r')
l_r_ordinal = ll.NonlinearityLayer(l_uv_s,
nonlinearity=layers.log_ordinal_softmax,
name='l_r_ordinal')
#----------------------------------------------------------------
# Likelihood and RMSE
# training
p_r_train, log_p_r_ordinal_train = ll.get_output([l_r, l_r_ordinal],
deterministic=False)
log_p_r = T.mean(
parmesan.distributions.log_multinomial(sym_R_minibatch - 1, p_r_train))
R_minibatch_one_hot = lasagne.utils.one_hot(sym_R_minibatch,
m=N_stars + 1)[:, 1:]
log_p_r_ordinal = T.mean(
T.sum(log_p_r_ordinal_train * R_minibatch_one_hot, axis=1))
regularization = lasagne.regularization.regularize_network_params(
[l_r], lasagne.regularization.l2)
cost_function = -(
1.0 - alpha
) * log_p_r - alpha * log_p_r_ordinal + weight_decay * regularization
predicts_train = T.sum(p_r_train *
T.shape_padleft(T.arange(1, 1 + N_stars)),
axis=1)
SE_train = T.sum(T.sqr(T.cast(sym_R_minibatch, floatX) - predicts_train))
# test
sym_enc_u_h = T.fmatrix('enc_u_h')
sym_enc_v_h = T.fmatrix('enc_v_h')
enc_u_h_out, enc_v_h_out = ll.get_output([l_enc_u_h, l_enc_v_h],
deterministic=True)
p_r_test, = ll.get_output([l_r],
inputs={
l_enc_u_h: sym_enc_u_h,
l_enc_v_h: sym_enc_v_h
},
deterministic=True)
predicts_test = T.sum(p_r_test * T.shape_padleft(T.arange(1, 1 + N_stars)),
axis=1)
SE_test = T.sum(T.sqr(T.cast(sym_R_minibatch, floatX) - predicts_test))
#----------------------------------------------------------------
# Gradients
clip_grad = 1
max_norm = 5
params = ll.get_all_params([
l_r,
], trainable=True)
for p in params:
logger.debug("%s: %s" % (p, p.get_value().shape))
grads = T.grad(cost_function, params)
mgrads = lasagne.updates.total_norm_constraint(grads, max_norm=max_norm)
cgrads = [T.clip(g, -clip_grad, clip_grad) for g in mgrads]
updates = lasagne.updates.adam(cgrads,
params,
beta1=0.9,
beta2=0.999,
epsilon=1e-4,
learning_rate=sym_lr)
#----------------------------------------------------------------
# Compile
# training function
logger.info("Compiling train_model ...")
train_model = theano.function(
inputs=[
sym_lr, sym_uid_origin, sym_uid_minibatch, sym_vid_origin,
sym_vid_minibatch, sym_R_minibatch, sym_Ru, sym_Rv, sym_dr_Ru,
sym_dr_Rv
],
outputs=[log_p_r, SE_train],
updates=updates,
)
# encoders
logger.info("Compiling encode_model ...")
u_encode_model = theano.function(inputs=[sym_Ru], outputs=enc_u_h_out)
v_encode_model = theano.function(inputs=[sym_Rv], outputs=enc_v_h_out)
# test function
logger.info("Compiling test_model ...")
test_model = theano.function(
inputs=[
sym_uid_origin, sym_uid_minibatch, sym_vid_origin,
sym_vid_minibatch, sym_R_minibatch, sym_enc_u_h, sym_enc_v_h
],
outputs=[SE_test],
)
#----------------------------------------------------------------
# Predict function
def predict(which_set='test'):
assert which_set in ['valid', 'test']
if which_set == 'valid':
R_matrix_cond = R_matrix['train']
else:
R_matrix_cond = R_matrix['train'] + R_matrix['valid']
# test statistics
SE_epoch = 0
n_pred_epoch = 0
# preconpute hidden representation
u_end = 0
while u_end < N_u:
u_start, u_end = u_end, min(u_end + batch_size_u, N_u)
# create user mini-batch
u_batch_ids = np.arange(u_start, u_end).astype('int32')
# create conditionals
Ru_minibatch = R_matrix_cond[u_batch_ids, :]
# encode
np_enc_u_h[u_batch_ids] = u_encode_model(Ru_minibatch)
v_end = 0
while v_end < N_v:
v_start, v_end = v_end, min(v_end + batch_size_v, N_v)
# create item mini-batch
v_batch_ids = np.arange(v_start, v_end).astype('int32')
# create conditionals
Rv_minibatch = R_matrix_cond[:, v_batch_ids].T
# encode
np_enc_v_h[v_batch_ids] = v_encode_model(Rv_minibatch)
# loop mini-batches
u_end = 0
while u_end < N_u:
u_start, u_end = u_end, min(u_end + batch_size_u, N_u)
v_end = 0
while v_end < N_v:
v_start, v_end = v_end, min(v_end + batch_size_v, N_v)
# create user mini-batch and item mini-batch
u_batch_ids = np.arange(u_start, u_end).astype('int32')
v_batch_ids = np.arange(v_start, v_end).astype('int32')
# get encoded vectors
Ru_encoded = np_enc_u_h[u_batch_ids, :]
Rv_encoded = np_enc_v_h[v_batch_ids, :]
# create test samples mini-batch
R_matrix_minibatch = R_matrix[which_set][np.ix_(
u_batch_ids, v_batch_ids)]
R_matrix_minibatch_sparse = scipy.sparse.coo_matrix(
R_matrix_minibatch)
# prepare user and item IDs needed
uid_minibatch = R_matrix_minibatch_sparse.row
vid_minibatch = R_matrix_minibatch_sparse.col
R_minibatch = R_matrix_minibatch_sparse.data
n_pred_step = R_minibatch.shape[0]
if n_pred_step == 0:
continue
uid_origin = u_batch_ids[uid_minibatch]
vid_origin = v_batch_ids[vid_minibatch]
SE_step, = test_model(uid_origin, uid_minibatch, vid_origin,
vid_minibatch, R_minibatch, Ru_encoded,
Rv_encoded)
SE_epoch += SE_step
n_pred_epoch += n_pred_step
# print info after test finished
assert n_pred_epoch == N_rating[which_set]
RMSE_epoch = np.sqrt(SE_epoch / n_pred_epoch) / (N_stars / 5.0)
logger.critical("Estimated %s RMSE = %f (%d %s ratings)" %
(which_set, RMSE_epoch, n_pred_epoch, which_set))
return RMSE_epoch
#----------------------------------------------------------------
# Training
best_valid_result = np.inf
best_model = None
n_epocs_without_improvement = 0
logger.warning("Training started.")
# loop epoch
for epoch in range(1, 1 + max_epoch):
epoch_start_time = time.time()
# training statistics
LL_epoch_train, SE_epoch_train = 0, 0
n_pred_epoch_train = 0
# loop mini-batches
for step in range(n_step):
# sample i and j
#i = np.random.randint(N_u)
#j = np.random.randint(N_v)
threshold_u = np.int(0.2 * N_u)
threshold_v = np.int(0.2 * N_v)
i = np.random.randint(low=threshold_u,
high=N_u - min(threshold_u, batch_size_u))
j = np.random.randint(low=threshold_v,
high=N_v - min(threshold_v, batch_size_v))
# calculate mini-batch size
Bi = min(batch_size_u, N_u - i)
Bj = min(batch_size_v, N_v - j)
# sample user mini-batch and item mini-batch
u_batch_ids_train = np.random.choice(N_u, Bi,
replace=False).astype('int32')
v_batch_ids_train = np.random.choice(N_v, Bj,
replace=False).astype('int32')
# create conditionals
Ru_minibatch_train = R_matrix['train'][u_batch_ids_train, :]
Rv_minibatch_train = R_matrix['train'][:, v_batch_ids_train].T
Ru_minibatch_train[:, v_batch_ids_train] = 0
Rv_minibatch_train[:, u_batch_ids_train] = 0
# calculate dropout rate
dr_Ru = 1.0 - 1.0 * j / (N_v - Bj)
dr_Rv = 1.0 - 1.0 * i / (N_u - Bi)
# create training samples mini-batch
R_matrix_minibatch_train = R_matrix['train'][np.ix_(
u_batch_ids_train, v_batch_ids_train)]
R_matrix_minibatch_sparse_train = scipy.sparse.coo_matrix(
R_matrix_minibatch_train)
# prepare user and item IDs needed
uid_minibatch_train = R_matrix_minibatch_sparse_train.row
vid_minibatch_train = R_matrix_minibatch_sparse_train.col
R_minibatch_train = R_matrix_minibatch_sparse_train.data
n_pred_step_train = R_minibatch_train.shape[0]
if n_pred_step_train == 0:
logger.warning(
'no training samples in current mini-batch.(i=%d, j=%d)' %
(i, j))
continue
uid_origin_train = u_batch_ids_train[uid_minibatch_train]
vid_origin_train = v_batch_ids_train[vid_minibatch_train]
# update parameters and calculate likelihood and RMSE
LL_step_train, SE_step_train = train_model(
lr, uid_origin_train, uid_minibatch_train, vid_origin_train,
vid_minibatch_train, R_minibatch_train, Ru_minibatch_train,
Rv_minibatch_train, dr_Ru, dr_Rv)
LL_epoch_train += LL_step_train * n_pred_step_train
SE_epoch_train += SE_step_train
n_pred_epoch_train += n_pred_step_train
# print info after epoch finished
LL_epoch_train /= n_pred_epoch_train
RMSE_epoch_train = np.sqrt(
SE_epoch_train / n_pred_epoch_train) / (N_stars / 5.0)
epoch_end_time = time.time()
logger.info(
"Epoch %d, Estimated training RMSE = %f, LL = %f (%d training ratings). Elapsed time %fs."
% (epoch, RMSE_epoch_train, LL_epoch_train, n_pred_epoch_train,
epoch_end_time - epoch_start_time))
# validation
RMSE_valid = predict('valid')
# termination
if RMSE_valid < best_valid_result:
n_epocs_without_improvement = 0
best_valid_result = RMSE_valid
best_model = ll.get_all_param_values([
l_r,
], trainable=True)
logger.debug("New best model found!")
else:
n_epocs_without_improvement += 1
if n_epocs_without_improvement >= lookahead:
ll.set_all_param_values([
l_r,
], best_model, trainable=True)
if lr > 1e-5:
n_epocs_without_improvement = 0
lr /= 4
logger.warning("Learning rate = %f now." % lr)
else:
logger.warning("Training finished.")
break
#----------------------------------------------------------------
# Test
RMSE_test = predict('test')
#----------------------------------------------------------------
# Summarization
for k, v in six.iteritems(args_dict):
logger.info(" %20s: %s" % (k, v))
def build_network_from_ae(classn):
input_var = T.tensor4('input_var')
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var)
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
120,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = layers.Pool2DLayer(layer,
pool_size=(2, 2),
stride=2,
mode='average_inc_pad')
layer = batch_norm(
layers.Conv2DLayer(layer,
240,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = layers.Pool2DLayer(layer,
pool_size=(2, 2),
stride=2,
mode='average_inc_pad')
layer = batch_norm(
layers.Conv2DLayer(layer,
640,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
prely = batch_norm(
layers.Conv2DLayer(layer,
1024,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
featm = batch_norm(
layers.Conv2DLayer(prely,
640,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
feat_map = batch_norm(
layers.Conv2DLayer(featm,
100,
filter_size=(1, 1),
nonlinearity=rectify,
name="feat_map"))
maskm = batch_norm(
layers.Conv2DLayer(prely,
100,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
mask_rep = batch_norm(layers.Conv2DLayer(maskm,
1,
filter_size=(1, 1),
nonlinearity=None),
beta=None,
gamma=None)
mask_map = SoftThresPerc(mask_rep,
perc=97.0,
alpha=0.1,
beta=init.Constant(0.5),
tight=100.0,
name="mask_map")
enlyr = ChInnerProdMerge(feat_map, mask_map, name="encoder")
layer = batch_norm(
layers.Deconv2DLayer(enlyr,
1024,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
640,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
640,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
240,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
120,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = layers.Deconv2DLayer(layer,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
glblf = batch_norm(
layers.Conv2DLayer(prely,
128,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
glblf = layers.Pool2DLayer(glblf,
pool_size=(5, 5),
stride=5,
mode='average_inc_pad')
glblf = batch_norm(
layers.Conv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
gllyr = batch_norm(layers.Conv2DLayer(glblf,
5,
filter_size=(1, 1),
nonlinearity=rectify),
name="global_feature")
glblf = batch_norm(
layers.Deconv2DLayer(gllyr,
256,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(9, 9),
stride=5,
crop=(2, 2),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = layers.Deconv2DLayer(glblf,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
layer = layers.ElemwiseSumLayer([layer, glblf])
network = ReshapeLayer(layer, ([0], -1))
mask_map.beta.set_value(np.float32(0.9 * mask_map.beta.get_value()))
old_params = layers.get_all_params(network, trainable=True)
# Adding more layers
aug_var = T.matrix('aug_var')
target_var = T.imatrix('targets')
add_a = batch_norm(
layers.Conv2DLayer(enlyr,
320,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
add_b = batch_norm(
layers.Conv2DLayer(add_a,
320,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
add_c = batch_norm(
layers.Conv2DLayer(add_b,
320,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
add_d = batch_norm(
layers.Conv2DLayer(add_c,
320,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
add_0 = layers.Pool2DLayer(add_d,
pool_size=(25, 25),
stride=25,
mode='average_inc_pad')
add_1 = batch_norm(
layers.DenseLayer(add_0, 100, nonlinearity=leaky_rectify))
add_2 = batch_norm(
layers.DenseLayer(gllyr, 320, nonlinearity=leaky_rectify))
add_3 = batch_norm(
layers.DenseLayer(add_2, 320, nonlinearity=leaky_rectify))
add_4 = batch_norm(
layers.DenseLayer(add_3, 100, nonlinearity=leaky_rectify))
aug_layer = layers.InputLayer(shape=(None, aug_fea_n), input_var=aug_var)
cat_layer = lasagne.layers.ConcatLayer([add_1, add_4, aug_layer], axis=1)
hidden_layer = layers.DenseLayer(cat_layer, 80, nonlinearity=leaky_rectify)
network = layers.DenseLayer(hidden_layer, classn, nonlinearity=sigmoid)
all_params = layers.get_all_params(network, trainable=True)
new_params = [x for x in all_params if x not in old_params]
return network, new_params, input_var, aug_var, target_var
def build_network(self, K, vocab_size, W_init):
l_docin = L.InputLayer(shape=(None,None,1), input_var=self.inps[0])
l_doctokin = L.InputLayer(shape=(None,None), input_var=self.inps[1])
l_qin = L.InputLayer(shape=(None,None,1), input_var=self.inps[2])
l_qtokin = L.InputLayer(shape=(None,None), input_var=self.inps[3])
l_docmask = L.InputLayer(shape=(None,None), input_var=self.inps[6])
l_qmask = L.InputLayer(shape=(None,None), input_var=self.inps[7])
l_tokin = L.InputLayer(shape=(None,MAX_WORD_LEN), input_var=self.inps[8])
l_tokmask = L.InputLayer(shape=(None,MAX_WORD_LEN), input_var=self.inps[9])
l_featin = L.InputLayer(shape=(None,None), input_var=self.inps[11])
doc_shp = self.inps[1].shape
qry_shp = self.inps[3].shape
l_docembed = L.EmbeddingLayer(l_docin, input_size=vocab_size,
output_size=self.embed_dim, W=W_init) # B x N x 1 x DE
l_doce = L.ReshapeLayer(l_docembed,
(doc_shp[0],doc_shp[1],self.embed_dim)) # B x N x DE
l_qemb = L.EmbeddingLayer(l_qin, input_size=vocab_size,
output_size=self.embed_dim, W=l_docembed.W)
l_qembed = L.ReshapeLayer(l_qemb,
(qry_shp[0],qry_shp[1],self.embed_dim)) # B x N x DE
l_fembed = L.EmbeddingLayer(l_featin, input_size=2, output_size=2) # B x N x 2
if self.train_emb==0:
l_docembed.params[l_docembed.W].remove('trainable')
l_qemb.params[l_qemb.W].remove('trainable')
# char embeddings
if self.use_chars:
l_lookup = L.EmbeddingLayer(l_tokin, self.num_chars, self.char_dim) # T x L x D
l_fgru = L.GRULayer(l_lookup, self.char_dim, grad_clipping=GRAD_CLIP,
mask_input=l_tokmask, gradient_steps=GRAD_STEPS, precompute_input=True,
only_return_final=True)
l_bgru = L.GRULayer(l_lookup, self.char_dim, grad_clipping=GRAD_CLIP,
mask_input=l_tokmask, gradient_steps=GRAD_STEPS, precompute_input=True,
backwards=True, only_return_final=True) # T x 2D
l_fwdembed = L.DenseLayer(l_fgru, self.embed_dim/2, nonlinearity=None) # T x DE/2
l_bckembed = L.DenseLayer(l_bgru, self.embed_dim/2, nonlinearity=None) # T x DE/2
l_embed = L.ElemwiseSumLayer([l_fwdembed, l_bckembed], coeffs=1)
l_docchar_embed = IndexLayer([l_doctokin, l_embed]) # B x N x DE/2
l_qchar_embed = IndexLayer([l_qtokin, l_embed]) # B x Q x DE/2
l_doce = L.ConcatLayer([l_doce, l_docchar_embed], axis=2)
l_qembed = L.ConcatLayer([l_qembed, l_qchar_embed], axis=2)
attentions = []
if self.save_attn:
l_m = PairwiseInteractionLayer([l_doce,l_qembed])
attentions.append(L.get_output(l_m, deterministic=True))
for i in range(K-1):
l_fwd_doc_1 = L.GRULayer(l_doce, self.nhidden, grad_clipping=GRAD_CLIP,
mask_input=l_docmask, gradient_steps=GRAD_STEPS, precompute_input=True)
l_bkd_doc_1 = L.GRULayer(l_doce, self.nhidden, grad_clipping=GRAD_CLIP,
mask_input=l_docmask, gradient_steps=GRAD_STEPS, precompute_input=True, \
backwards=True)
l_doc_1 = L.concat([l_fwd_doc_1, l_bkd_doc_1], axis=2) # B x N x DE
l_fwd_q_1 = L.GRULayer(l_qembed, self.nhidden, grad_clipping=GRAD_CLIP,
mask_input=l_qmask,
gradient_steps=GRAD_STEPS, precompute_input=True)
l_bkd_q_1 = L.GRULayer(l_qembed, self.nhidden, grad_clipping=GRAD_CLIP,
mask_input=l_qmask,
gradient_steps=GRAD_STEPS, precompute_input=True, backwards=True)
l_q_c_1 = L.ConcatLayer([l_fwd_q_1, l_bkd_q_1], axis=2) # B x Q x DE
l_m = PairwiseInteractionLayer([l_doc_1, l_q_c_1])
l_doc_2_in = GatedAttentionLayer([l_doc_1, l_q_c_1, l_m],
gating_fn=self.gating_fn,
mask_input=self.inps[7])
l_doce = L.dropout(l_doc_2_in, p=self.dropout) # B x N x DE
if self.save_attn:
attentions.append(L.get_output(l_m, deterministic=True))
if self.use_feat: l_doce = L.ConcatLayer([l_doce, l_fembed], axis=2) # B x N x DE+2
# final layer
l_fwd_doc = L.GRULayer(l_doce, self.nhidden, grad_clipping=GRAD_CLIP,
mask_input=l_docmask, gradient_steps=GRAD_STEPS, precompute_input=True)
l_bkd_doc = L.GRULayer(l_doce, self.nhidden, grad_clipping=GRAD_CLIP,
mask_input=l_docmask, gradient_steps=GRAD_STEPS, precompute_input=True, \
backwards=True)
l_doc = L.concat([l_fwd_doc, l_bkd_doc], axis=2)
l_fwd_q = L.GRULayer(l_qembed, self.nhidden, grad_clipping=GRAD_CLIP, mask_input=l_qmask,
gradient_steps=GRAD_STEPS, precompute_input=True, only_return_final=False)
l_bkd_q = L.GRULayer(l_qembed, self.nhidden, grad_clipping=GRAD_CLIP, mask_input=l_qmask,
gradient_steps=GRAD_STEPS, precompute_input=True, backwards=True,
only_return_final=False)
l_q = L.ConcatLayer([l_fwd_q, l_bkd_q], axis=2) # B x Q x 2D
if self.save_attn:
l_m = PairwiseInteractionLayer([l_doc, l_q])
attentions.append(L.get_output(l_m, deterministic=True))
l_prob = AttentionSumLayer([l_doc,l_q], self.inps[4], self.inps[12],
mask_input=self.inps[10])
final = L.get_output(l_prob)
final_v = L.get_output(l_prob, deterministic=True)
return final, final_v, l_prob, l_docembed.W, attentions
