def build_model1(input_shape, num_output=2, input_var=None, depth=1, num_units=79, num_rbf=0, nonlin=rect):
""" each layer has rbf and relu activation. all layers have the same distribution except the output.
if dropout is True, it is set at 50% probability.
"""
assert num_rbf <= num_units
l = layers.InputLayer(input_shape,input_var=input_var)
for d in range(depth):
if num_rbf == 0:
l = layers.DenseLayer(l,num_units=num_units, nonlinearity=nonlin())
else :
l1 = layers.DenseLayer(l,num_units=(num_units-num_rbf),nonlinearity=nonlin())
l2 = layers.DenseLayer(l, num_units=num_rbf,nonlinearity=sigm)
l = layers.ConcatLayer([l1,l2])
# if dropout:
# l = layers.DropoutLayer(l, p=0.5)
if True :
assert num_output== 2
l2 = layers.DenseLayer(l, num_units=1, nonlinearity=nonlin())
l1 = layers.DenseLayer(l, num_units=1, nonlinearity=sigm)
l = layers.ConcatLayer([l1, l2])
else :
l = layers.DenseLayer(l,num_units=num_output)
return l
def concat_tcn(_top, _cond, _seed, start=0, num_slices=1):
_cond1 = create_conditon_slices_from(_cond, _top.output_shape)
if num_slices>0:
_seed1, n = create_slices_from(_seed, _top.output_shape, start=start, num_slices=num_slices)
return L.ConcatLayer([_top, _cond1, _seed1], axis=1), start+n
else:
return L.ConcatLayer([_top, _cond1], axis=1), start
def __init__(self, incomings, num_units_hidden_common, dim_z, beta):
'''
params:
incomings: input layers, [image, label]
num_units_hidden_common: num_units_hidden for all BasicLayers.
dim_z: the dimension of z and num_units_output for encoders BaiscLayer
'''
super(SemiVAE, self).__init__(incomings)
self.mrg_srng = MRG_RandomStreams()
# random generator
self.incomings = incomings
self.num_classes = incomings[1].output_shape[1]
self.num_units_hidden_common = num_units_hidden_common
self.dim_z = dim_z
self.beta = beta
self.concat_xy = layers.ConcatLayer(self.incomings, axis=1)
self.encoder = BasicLayer(
self.concat_xy,
num_units_hidden=self.num_units_hidden_common,
)
self.encoder_mu = layers.DenseLayer(
self.encoder, self.dim_z, nonlinearity=nonlinearities.identity)
self.encoder_log_var = layers.DenseLayer(
self.encoder, self.dim_z, nonlinearity=nonlinearities.identity)
[image_input, label_input] = self.incomings
self.dim_image = image_input.output_shape[1]
print('dim_image: ', self.dim_image)
# merge encoder_mu and encoder_log_var to get z.
self.sampler = SamplerLayer([self.encoder_mu, self.encoder_log_var])
self.concat_yz = layers.ConcatLayer([label_input, self.sampler],
axis=1)
self.decoder = BasicLayer(
self.concat_yz, num_units_hidden=self.num_units_hidden_common)
self.decoder_x = layers.DenseLayer(self.decoder,
num_units=self.dim_image,
nonlinearity=nonlinearities.sigmoid)
self.classifier_helper = BasicLayer(
self.incomings[0], num_units_hidden=self.num_units_hidden_common)
self.classifier = layers.DenseLayer(
self.classifier_helper,
num_units=self.num_classes,
nonlinearity=nonlinearities.softmax,
)
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 init_cnn(model_file, hidden_units, num_filters, filter_hs, dropout_rate,
n_words, n_dim):
"""
initializes CNN by loading weights of a previously trained model. note that the model
trained and this model need to have same parameters. see trainCNN.py for explanation of
neural network architecture
:param model_file:
:param hidden_units:
:param num_filters:
:param filter_hs:
:param dropout_rate:
:param n_words:
:param n_dim:
:return:
"""
assert len(num_filters) == len(filter_hs)
filter_shapes = []
pool_sizes = []
for filter_h in filter_hs:
filter_shapes.append((filter_h, n_dim))
pool_sizes.append((n_words - filter_h + 1, 1))
l_in = LL.InputLayer(shape=(None, 1, n_words, n_dim))
layer_list = []
for i in range(len(filter_hs)):
l_conv = LL.Conv2DLayer(l_in,
num_filters=num_filters[i],
filter_size=filter_shapes[i],
nonlinearity=L.nonlinearities.rectify,
W=L.init.HeNormal(gain='relu'))
l_pool = LL.MaxPool2DLayer(l_conv, pool_size=pool_sizes[i])
layer_list.append(l_pool)
mergedLayer = LL.ConcatLayer(layer_list)
l_hidden1 = LL.DenseLayer(mergedLayer,
num_units=hidden_units[0],
nonlinearity=L.nonlinearities.tanh,
W=L.init.HeNormal(gain='relu'))
l_hidden1_dropout = LL.DropoutLayer(l_hidden1, p=dropout_rate[0])
l_hidden2 = LL.DenseLayer(l_hidden1_dropout,
num_units=hidden_units[1],
nonlinearity=L.nonlinearities.tanh,
W=L.init.HeNormal(gain='relu'))
l_hidden2_dropout = LL.DropoutLayer(l_hidden2, p=dropout_rate[1])
l_output = LL.DenseLayer(l_hidden2_dropout,
num_units=hidden_units[2],
nonlinearity=L.nonlinearities.tanh)
net_output = theano.function([l_in.input_var],
LL.get_output(l_output, deterministic=True))
with np.load(model_file) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
LL.set_all_param_values(l_output, param_values)
return net_output
def pons_cnn(params):
""""""
layers = L.InputLayer((None, 1, params['dur'], 128))
print layers.output_shape
sclr = joblib.load(params['scaler'])
layers = L.standardize(layers,
sclr.mean_.astype(np.float32),
sclr.scale_.astype(np.float32),
shared_axes=(0, 1, 2))
print layers.output_shape
layers_timbre = L.GlobalPoolLayer(
L.batch_norm(L.Conv2DLayer(layers, 64, (1, 96))))
layers_rhythm = L.GlobalPoolLayer(
L.batch_norm(L.Conv2DLayer(layers, 64, (params['dur'] - 10, 1))))
layers = L.ConcatLayer([layers_rhythm, layers_timbre], axis=-1)
layers = L.DenseLayer(layers, 64, nonlinearity=nl.rectify)
print layers.output_shape
layers = L.DenseLayer(layers, 16, nonlinearity=nl.softmax)
print layers.output_shape
return layers
def build_network():
l_in = L.InputLayer((None, SEQUENCE_LEN, 256))
l_forward = L.RecurrentLayer(l_in, num_units=16)
l_backward = L.RecurrentLayer(l_in, num_units=16, backwards=True)
l_concat = L.ConcatLayer([l_forward, l_backward])
l_out = L.DenseLayer(l_concat, num_units=2, nonlinearity=T.nnet.softmax)
return l_out
def __build_48_net__(self):
model24 = self.subnet
network = layers.InputLayer((None, 3, 48, 48),
input_var=self.__input_var__)
network = layers.Conv2DLayer(network,
num_filters=64,
filter_size=(5, 5),
stride=1,
nonlinearity=relu)
network = layers.batch_norm(
layers.MaxPool2DLayer(network, pool_size=(3, 3), stride=2))
network = layers.Conv2DLayer(network,
num_filters=64,
filter_size=(5, 5),
stride=1,
nonlinearity=relu)
network = layers.BatchNormLayer(network)
network = layers.MaxPool2DLayer(network, pool_size=(3, 3), stride=2)
network = layers.DenseLayer(network, num_units=256, nonlinearity=relu)
#network = layers.Conv2DLayer(network,num_filters=256,filter_size=(1,1),stride=1,nonlinearity=relu)
denselayer24 = model24.net.input_layer
network = layers.ConcatLayer([network, denselayer24])
network = layers.DenseLayer(network, num_units=2, nonlinearity=softmax)
return network
def build_network(self, ra_input_var, mc_input_var):
print('Building raw dnn with parameters:')
pp = pprint.PrettyPrinter(indent=4)
pp.pprint(self.net_opts)
ra_network_1 = layers.InputLayer((None, 1, 3969), ra_input_var)
ra_network_1 = self.set_conv_layer(ra_network_1, 'ra_conv_1', dropout=False, pad='same')
ra_network_1 = self.set_pool_layer(ra_network_1, 'ra_pool_1')
ra_network_1 = self.set_conv_layer(ra_network_1, 'ra_conv_2', pad='same')
ra_network_1 = self.set_pool_layer(ra_network_1, 'ra_pool_2')
ra_network_1 = self.set_conv_layer(ra_network_1, 'ra_conv_3', pad='same')
ra_network_1 = self.set_pool_layer(ra_network_1, 'ra_pool_3')
ra_network_1 = self.set_conv_layer(ra_network_1, 'ra_conv_4', pad='same')
ra_network_1 = self.set_pool_layer(ra_network_1, 'ra_pool_4')
concat_list = [ra_network_1]
mc_input = layers.InputLayer((None, 2, MC_LENGTH), mc_input_var)
concat_list.append(mc_input)
network = layers.ConcatLayer(concat_list, axis=1, cropping=[None, None, 'center'])
network = layers.BatchNormLayer(network)
for n in self.net_opts['layer_list']:
network = layers.DenseLayer(layers.dropout(network, p=self.net_opts['dropout_p']),
n,
nonlinearity=lasagne.nonlinearities.rectify)
network = layers.DenseLayer(layers.dropout(network, p=self.net_opts['dropout_p']),
self.net_opts['num_class'],
nonlinearity=lasagne.nonlinearities.softmax)
# print(layers.get_output_shape(network))
self.network = network
return self.network
def load_check_point(train_id, path=None):
"""
"""
if path is None:
path = os.getcwd()
param_fn = os.path.join(path, str(train_id) + '.param.npz')
config_fn = os.path.join(path, str(train_id) + '.nnconfig.gz')
params = joblib.load(config_fn)
mdl, net = build(network(params), params)
layers = L.get_all_layers(L.ConcatLayer(net.values(), axis=1))
if os.path.exists(param_fn):
try:
print('Loadong pre-trained weight...')
with np.load(param_fn) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
L.set_all_param_values(layers, param_values)
except Exception as e:
print(e)
print('Cannot load parameters!')
else:
print('Cannot find parameters!')
return net, mdl, params
def concat_tn(_top, _seed, start=0, num_slices=1):
if _top==None:
return L.SliceLayer(_seed, indices=slice(start, start+num_slices), axis=1), start+num_slices
elif num_slices>0:
_seed1, n = create_slices_from(_seed, _top.output_shape, start=start, num_slices=num_slices)
return L.ConcatLayer([_top, _seed1], axis=1), start+n
else:
return _top, start
def output_block(net, config, non_lin, verbose=True):
"""
"""
# output setting
out_acts = []
for out_act in config.hyper_parameters.out_act:
exec('from lasagne.nonlinearities import {}'.format(out_act))
out_acts.append(eval(out_act))
n_outs = config.hyper_parameters.n_out
# Global Average Pooling
last_conv_block_name = next(reversed(net))
net['gap'] = L.GlobalPoolLayer(net[last_conv_block_name], name='gap')
net['gap.bn'] = L.BatchNormLayer(net['gap'], name='gap.bn')
n_features = net['gap.bn'].output_shape[-1]
# feature Layer
net['fc'] = L.dropout(L.batch_norm(
L.DenseLayer(net['gap.bn'],
num_units=n_features,
nonlinearity=non_lin,
name='fc')),
name='fc.bn.do')
# output (prediction)
# check whether the model if for MTL or STL
# target is passed as list, regardless whether
# it's MTL or STL (configuration checker checks it)
targets = config.target
out_layer_names = []
for target, n_out, out_act in zip(targets, n_outs, out_acts):
out_layer_names.append('out.{}'.format(target))
if target == 'self':
net[out_layer_names[-1]], inputs = build_siamese(net['fc'])
else:
net[out_layer_names[-1]] = L.DenseLayer(net['fc'],
num_units=n_out,
nonlinearity=out_act,
name=out_layer_names[-1])
inputs = [net['input'].input_var]
# make a concatation layer just for save/load purpose
net['IO'] = L.ConcatLayer([
L.FlattenLayer(net[target_layer_name])
if target == 'self' else net[target_layer_name]
for target_layer_name in out_layer_names
],
name='IO')
if verbose:
print(net['gap.bn'].output_shape)
print(net['fc'].output_shape)
for target in targets:
print(net['out.{}'.format(target)].output_shape)
return net, inputs
def __init__(self, output_size, meta_size, depth=2):
encoder_sizes = [64, 64, 64]
input_var = TT.matrix()
meta_var = TT.matrix()
target_var = TT.matrix()
mask_var = TT.matrix()
input_layer = layers.InputLayer((None, output_size), input_var=input_var)
meta_layer = layers.InputLayer((None, meta_size), input_var=meta_var)
concat_input_layer = layers.ConcatLayer([input_layer, meta_layer])
dense = concat_input_layer
for idx in xrange(depth):
dense = layers.DenseLayer(dense, encoder_sizes[idx])
dense = layers.batch_norm(dense)
mu_and_logvar = layers.DenseLayer(dense, 2 * output_size, nonlinearity=nonlinearities.linear)
mu = layers.SliceLayer(mu_and_logvar, slice(0, output_size), axis=1)
log_var = layers.SliceLayer(mu_and_logvar, slice(output_size, None), axis=1)
loss = neg_log_likelihood2(
target_var,
layers.get_output(mu),
layers.get_output(log_var),
mask_var
).mean()
test_loss = neg_log_likelihood2(
target_var,
layers.get_output(mu, deterministic=True),
layers.get_output(log_var, deterministic=True),
mask_var
).mean()
params = layers.get_all_params(mu_and_logvar, trainable=True)
param_updates = updates.adadelta(loss, params)
self._train_fn = theano.function(
[input_var, meta_var, target_var],
updates=param_updates,
outputs=loss
)
self._loss_fn = theano.function(
[input_var, meta_var, target_var],
outputs=test_loss
)
self._predict_fn = theano.function(
[input_var, meta_var],
outputs=[
layers.get_output(mu, deterministic=True),
layers.get_output(log_var, deterministic=True)
]
)
def get_generator(self, meanx, z0, y_1hot):
''' specify generator G0, gen_x = G0(z0, h1) '''
"""
#z0 = theano_rng.uniform(size=(self.args.batch_size, 16)) # uniform noise
gen0_layers = [LL.InputLayer(shape=(self.args.batch_size, 50), input_var=z0)] # Input layer for z0
gen0_layers.append(nn.batch_norm(LL.DenseLayer(nn.batch_norm(LL.DenseLayer(gen0_layers[0], num_units=128, W=Normal(0.02), nonlinearity=nn.relu)),
num_units=128, W=Normal(0.02), nonlinearity=nn.relu))) # embedding, 50 -> 128
gen0_layer_z_embed = gen0_layers[-1]
#gen0_layers.append(LL.InputLayer(shape=(self.args.batch_size, 256), input_var=real_fc3)) # Input layer for real_fc3 in independent training, gen_fc3 in joint training
gen0_layers.append(LL.InputLayer(shape=(self.args.batch_size, 10), input_var=y_1hot)) # Input layer for real_fc3 in independent training, gen_fc3 in joint training
gen0_layer_fc3 = gen0_layers[-1]
gen0_layers.append(LL.ConcatLayer([gen0_layer_fc3,gen0_layer_z_embed], axis=1)) # concatenate noise and fc3 features
gen0_layers.append(LL.ReshapeLayer(nn.batch_norm(LL.DenseLayer(gen0_layers[-1], num_units=256*5*5, W=Normal(0.02), nonlinearity=T.nnet.relu)),
(self.args.batch_size,256,5,5))) # fc
gen0_layers.append(nn.batch_norm(nn.Deconv2DLayer(gen0_layers[-1], (self.args.batch_size,256,10,10), (5,5), stride=(2, 2), padding = 'half',
W=Normal(0.02), nonlinearity=nn.relu))) # deconv
gen0_layers.append(nn.batch_norm(nn.Deconv2DLayer(gen0_layers[-1], (self.args.batch_size,128,14,14), (5,5), stride=(1, 1), padding = 'valid',
W=Normal(0.02), nonlinearity=nn.relu))) # deconv
gen0_layers.append(nn.batch_norm(nn.Deconv2DLayer(gen0_layers[-1], (self.args.batch_size,128,28,28), (5,5), stride=(2, 2), padding = 'half',
W=Normal(0.02), nonlinearity=nn.relu))) # deconv
gen0_layers.append(nn.Deconv2DLayer(gen0_layers[-1], (self.args.batch_size,3,32,32), (5,5), stride=(1, 1), padding = 'valid',
W=Normal(0.02), nonlinearity=T.nnet.sigmoid)) # deconv
gen_x_pre = LL.get_output(gen0_layers[-1], deterministic=False)
gen_x = gen_x_pre - meanx
# gen_x_joint = LL.get_output(gen0_layers[-1], {gen0_layer_fc3: gen_fc3}, deterministic=False) - meanx
return gen0_layers, gen_x
"""
gen_x_layer_z = LL.InputLayer(shape=(self.args.batch_size, self.args.z0dim), input_var=z0) # z, 20
# gen_x_layer_z_embed = nn.batch_norm(LL.DenseLayer(gen_x_layer_z, num_units=128), g=None) # 20 -> 64
gen_x_layer_y = LL.InputLayer(shape=(self.args.batch_size, 10), input_var=y_1hot) # conditioned on real fc3 activations
gen_x_layer_y_z = LL.ConcatLayer([gen_x_layer_y,gen_x_layer_z],axis=1) #512+256 = 768
gen_x_layer_pool2 = LL.ReshapeLayer(nn.batch_norm(LL.DenseLayer(gen_x_layer_y_z, num_units=256*5*5)), (self.args.batch_size,256,5,5))
gen_x_layer_dconv2_1 = nn.batch_norm(nn.Deconv2DLayer(gen_x_layer_pool2, (self.args.batch_size,256,10,10), (5,5), stride=(2, 2), padding = 'half',
W=Normal(0.02), nonlinearity=nn.relu))
gen_x_layer_dconv2_2 = nn.batch_norm(nn.Deconv2DLayer(gen_x_layer_dconv2_1, (self.args.batch_size,128,14,14), (5,5), stride=(1, 1), padding = 'valid',
W=Normal(0.02), nonlinearity=nn.relu))
gen_x_layer_dconv1_1 = nn.batch_norm(nn.Deconv2DLayer(gen_x_layer_dconv2_2, (self.args.batch_size,128,28,28), (5,5), stride=(2, 2), padding = 'half',
W=Normal(0.02), nonlinearity=nn.relu))
gen_x_layer_x = nn.Deconv2DLayer(gen_x_layer_dconv1_1, (self.args.batch_size,3,32,32), (5,5), stride=(1, 1), padding = 'valid',
W=Normal(0.02), nonlinearity=T.nnet.sigmoid)
# gen_x_layer_x = dnn.Conv2DDNNLayer(gen_x_layer_dconv1_2, 3, (1,1), pad=0, stride=1,
# W=Normal(0.02), nonlinearity=T.nnet.sigmoid)
gen_x_layers = [gen_x_layer_z, gen_x_layer_y, gen_x_layer_y_z, gen_x_layer_pool2, gen_x_layer_dconv2_1,
gen_x_layer_dconv2_2, gen_x_layer_dconv1_1, gen_x_layer_x]
gen_x_pre = LL.get_output(gen_x_layer_x, deterministic=False)
gen_x = gen_x_pre - meanx
return gen_x_layers, gen_x
def get_model(inp, patch_op):
icnn1 = batch_norm(utils_lasagne.GCNNLayer([icnn, patch_op], 16, nrings=5, nrays=16))
ffn1 = icnn1
ffn4 = LL.ConcatLayer([inp,ffn1],axis=1, cropping=None);
ffn = LL.DenseLayer(ffn4, nclasses, nonlinearity=utils_lasagne.log_softmax)
return ffn
def build_cnn():
data_size = (None, 10, 100) # Batch size x Img Channels x Height x Width
input_var = T.tensor3(name="input", dtype='int64')
values = np.array(np.random.randint(0, 1, (5, 10, 100)))
input_var.tag.test_value = values
input_layer = L.InputLayer(data_size, input_var=input_var)
W = create_char_embedding_matrix()
embed_layer = L.EmbeddingLayer(input_layer,
input_size=102,
output_size=101,
W=W)
reshape = L.reshape(embed_layer, (-1, 100, 101))
dim_shuffle = L.dimshuffle(reshape, (0, 2, 1))
#conv_layer_1 = L.Conv2DLayer(embed_layer, 4, (1), 1, 0)
#pool_layer_1 = L.MaxPool1DLayer(conv_layer_1, pool_size=1)
print L.get_output(dim_shuffle).tag.test_value.shape
conv_layer_1 = L.Conv1DLayer(dim_shuffle, 50, 2, 1)
print L.get_output(conv_layer_1).tag.test_value.shape
print "TEST"
pool_layer_1 = L.MaxPool1DLayer(conv_layer_1, pool_size=99)
print L.get_output(pool_layer_1).tag.test_value.shape
reshape_conv_1 = L.reshape(pool_layer_1, (-1, 50))
conv_layer_2 = L.Conv1DLayer(dim_shuffle, 50, 3, 1)
pool_layer_2 = L.MaxPool1DLayer(conv_layer_2, pool_size=98)
reshape_conv_2 = L.reshape(pool_layer_2, (-1, 50))
merge_layer = L.ConcatLayer([reshape_conv_1, reshape_conv_2], 1)
print L.get_output(merge_layer).tag.test_value.shape
reshape_output = L.reshape(merge_layer, (-1, 10, 100))
print L.get_output(reshape_output).tag.test_value.shape
x = T.tensor3(name="testname", dtype='int32')
#x = T.imatrix()
#output = L.get_output(conv_layer_1,x)
#f = theano.function([x],output)
word = unicode("Tat")
word_index = np.array([])
#print word_index
#x_test = np.array([word_index]).astype('int32')
#print f(x_test)
return reshape_output
def save_check_point(network, params, train_id, path=None):
""""""
layers = L.get_all_layers(L.ConcatLayer(network.values(), axis=1))
if path is None:
path = os.getcwd()
param_fn = os.path.join(path, str(train_id) + '.param')
config_fn = os.path.join(path, str(train_id) + '.nnconfig.gz')
np.savez(param_fn, *lasagne.layers.get_all_param_values(layers))
joblib.dump(params, config_fn)
def build_block(
incoming,
num_layers,
num_filters,
use_linear_skip=True,
filter_size=3,
p=0.1,
W_init=lasagne.init.GlorotUniform(),
b_init=None,
nonlinearity=lasagne.nonlinearities.rectify,
):
"""Builds a block in the DenseNet model."""
feature_maps = [incoming]
for i in xrange(num_layers):
if len(feature_maps) == 1:
network = incoming
else:
network = nn.ConcatLayer(feature_maps, axis=1)
network = nn.BatchNormLayer(network)
network = nn.NonlinearityLayer(network, nonlinearity)
network = nn.Conv2DLayer(network,
num_filters,
filter_size,
pad='same',
W=W_init,
b=b_init)
if p > 0:
network = nn.DropoutLayer(network, p=p)
feature_maps.append(network)
# Whether to return all connections (vanilla DenseNet), or to return only
# those feature maps created in the current block used in upscale path for
# semantic segmentation (100 layer tiramisu)
if use_linear_skip:
return nn.ConcatLayer(feature_maps, axis=1)
return nn.ConcatLayer(feature_maps[1:], axis=1)
def build_cnn(input):
#data_size = (None,103,130) # Batch size x Img Channels x Height x Width
#input_var = T.tensor3(name = "input",dtype='int64')
input_var = input
#values = np.array(np.random.randint(0,102,(1,9,50)))
#input_var.tag.test_value = values
#number sentences x words x characters
input_layer = L.InputLayer((None,9,50), input_var=input)
W = create_char_embedding_matrix()
embed_layer = L.EmbeddingLayer(input_layer, input_size=103,output_size=101, W=W)
#print "EMBED", L.get_output(embed_layer).tag.test_value.shape
reshape_embed = L.reshape(embed_layer,(-1,50,101))
#print "reshap embed", L.get_output(reshape_embed).tag.test_value.shape
conv_layer_1 = L.Conv1DLayer(reshape_embed, 55, 2)
conv_layer_2 = L.Conv1DLayer(reshape_embed, 55, 3)
#print "TEST"
#print "Convolution Layer 1", L.get_output(conv_layer_1).tag.test_value.shape
#print "Convolution Layer 2", L.get_output(conv_layer_2).tag.test_value.shape
#flatten_conv_1 = L.flatten(conv_layer_1,3)
#flatten_conv_2 = L.flatten(conv_layer_2,3)
#reshape_max_1 = L.reshape(flatten_conv_1,(-1,49))
#reshape_max_2 = L.reshape(flatten_conv_2, (-1,48))
#print "OUTPUT Flatten1", L.get_output(flatten_conv_1).tag.test_value.shape
#print "OUTPUT Flatten2", L.get_output(flatten_conv_2).tag.test_value.shape
#print "OUTPUT reshape_max_1", L.get_output(reshape_max_1).tag.test_value.shape
#print "OUTPUT reshape_max_2", L.get_output(reshape_max_2).tag.test_value.shape
pool_layer_1 = L.MaxPool1DLayer(conv_layer_1, pool_size=54)
pool_layer_2 = L.MaxPool1DLayer(conv_layer_2, pool_size=53)
#print "OUTPUT POOL1", L.get_output(pool_layer_1).tag.test_value.shape
#print "OUTPUT POOL2",L.get_output(pool_layer_2).tag.test_value.shape
merge_layer = L.ConcatLayer([pool_layer_1, pool_layer_2], 1)
flatten_merge = L.flatten(merge_layer, 2)
reshape_merge = L.reshape(flatten_merge, (1,9,110))
print L.get_output(reshape_embed).shape
#print L.get_output(reshape_merge).tag.test_value.shape
return reshape_merge, char_index_lookup
def build_network(self, ra_input_var, mc_input_var):
print('Building raw network with parameters:')
pp = pprint.PrettyPrinter(indent=4)
pp.pprint(self.net_opts)
ra_network_1 = layers.InputLayer((None, 1, None), ra_input_var)
ra_network_1 = self.set_conv_layer(ra_network_1,
'ra_conv_1',
dropout=False,
pad='same')
ra_network_1 = self.set_pool_layer(ra_network_1, 'ra_pool_1')
ra_network_1 = self.set_conv_layer(ra_network_1,
'ra_conv_2',
pad='same')
ra_network_1 = self.set_pool_layer(ra_network_1, 'ra_pool_2')
ra_network_1 = self.set_conv_layer(ra_network_1,
'ra_conv_3',
pad='same')
ra_network_1 = self.set_pool_layer(ra_network_1, 'ra_pool_3')
ra_network_1 = self.set_conv_layer(ra_network_1,
'ra_conv_4',
pad='same')
ra_network_1 = self.set_pool_layer(ra_network_1, 'ra_pool_4')
concat_list = [ra_network_1]
mc_input = layers.InputLayer((None, 2, None), mc_input_var)
concat_list.append(mc_input)
network = layers.ConcatLayer(concat_list,
axis=1,
cropping=[None, None, 'center'])
network = self.set_conv_layer(network, 'conv_1')
network = self.set_pool_layer(network, 'pool_1')
network = self.set_conv_layer(network, 'conv_2')
network = self.set_pool_layer(network, 'pool_2')
network = self.set_conv_layer(network, 'conv_3')
network = layers.GlobalPoolLayer(
network, getattr(T, self.net_opts['global_pool_func']))
# print(layers.get_output_shape(network))
# network = layers.DenseLayer(layers.dropout(network, p=self.net_opts['dropout_p']),
# self.net_opts['dens_1'],
# nonlinearity=lasagne.nonlinearities.rectify)
network = layers.DenseLayer(
layers.dropout(network, p=self.net_opts['dropout_p']),
self.net_opts['dens_2'],
nonlinearity=lasagne.nonlinearities.rectify)
network = layers.DenseLayer(
layers.dropout(network, p=self.net_opts['dropout_p']),
self.net_opts['num_class'],
nonlinearity=lasagne.nonlinearities.softmax)
# print(layers.get_output_shape(network))
self.network = network
return self.network
def __init__(self,
insize,
vocoder,
mlpg_wins=[],
hiddensize=256,
nonlinearity=lasagne.nonlinearities.very_leaky_rectify,
nblayers=3,
bn_axes=None,
dropout_p=-1.0,
grad_clipping=50):
if bn_axes is None:
bn_axes = [] # Recurrent nets don't like batch norm [ref needed]
model.Model.__init__(self, insize, vocoder, hiddensize)
if len(bn_axes) > 0:
warnings.warn(
'ModelBLSTM: You are using bn_axes={}, but batch normalisation is supposed to make Recurrent Neural Networks (RNNS) unstable [ref. needed]'
.format(bn_axes))
l_hid = ll.InputLayer(shape=(None, None, insize),
input_var=self._input_values,
name='input_conditional')
for layi in xrange(nblayers):
layerstr = 'l' + str(1 + layi) + '_BLSTM{}'.format(hiddensize)
fwd = layer_LSTM(l_hid,
hiddensize,
nonlinearity=nonlinearity,
backwards=False,
grad_clipping=grad_clipping,
name=layerstr + '.fwd')
bck = layer_LSTM(l_hid,
hiddensize,
nonlinearity=nonlinearity,
backwards=True,
grad_clipping=grad_clipping,
name=layerstr + '.bck')
l_hid = ll.ConcatLayer((fwd, bck), axis=2)
# Add batch normalisation
if len(bn_axes) > 0: l_hid = ll.batch_norm(l_hid, axes=bn_axes)
# Add dropout (after batchnorm)
if dropout_p > 0.0: l_hid = ll.dropout(l_hid, p=dropout_p)
l_out = layer_final(l_hid, vocoder, mlpg_wins)
self.init_finish(
l_out
) # Has to be called at the end of the __init__ to print out the architecture, get the trainable params, etc.
def conv_cond_concat(self, aLayer, aY, aYSize):
if True:
'''
aY: theano var.
'''
x_shape = aLayer.output_shape
oned = T.ones((x_shape[0], aYSize, x_shape[2], x_shape[3]))
y_oned = aY * oned # [Ymin, Ymax]
l_aY = ll.InputLayer(input_var=y_oned,
shape=(x_shape[0], aYSize, x_shape[2],
x_shape[3]))
layer = ll.ConcatLayer([aLayer, l_aY], axis=1)
return layer
else:
return aLayer
def build_dist_feat_fnc(net,
target,
conv_feat_locs=[5, 10, 12, 17, 19],
fc_feat_locs=[24, 28]):
""""""
layers = L.get_all_layers(net[target])
assert len(layers) == 30 # only works for standard deep conv2d
feat = [L.GlobalPoolLayer(layers[l]) for l in conv_feat_locs]
feat += [layers[l] for l in fc_feat_locs]
feat = L.ConcatLayer(feat, axis=1)
f = L.get_output(feat, deterministic=True)
f_feat = {target: {}}
f_feat[target]['transform'] = theano.function([layers[0].input_var],
f,
allow_input_downcast=True)
return f_feat
def __build_24_net__(self):
model12 = self.subnet
network = layers.InputLayer((None, 3, 24, 24),
input_var=self.__input_var__)
network = layers.Conv2DLayer(network,
num_filters=16,
filter_size=(5, 5),
stride=1,
nonlinearity=relu)
network = layers.MaxPool2DLayer(network, pool_size=(3, 3), stride=2)
network = layers.DropoutLayer(network)
network = layers.DenseLayer(network, num_units=128, nonlinearity=relu)
#network = layers.Conv2DLayer(network,num_filters=128,filter_size=(1,1),stride=1,nonlinearity=relu)
denselayer12 = model12.net.input_layer # i.e., one layer before the output layer of model12
network = layers.ConcatLayer(
[network,
denselayer12]) # concatenate with dense layer of this model
network = layers.DenseLayer(network, num_units=2, nonlinearity=softmax)
return network
def get_model(inp, patch_op):
icnn = LL.DenseLayer(inp, 16)
icnn1 = batch_norm(
utils_lasagne.GCNNLayer([icnn, patch_op], 16, nrings=5, nrays=16))
ffn1 = icnn1
icnn2 = batch_norm(
utils_lasagne.GCNNLayer([icnn1, patch_op], 32, nrings=5, nrays=16))
ffn2 = icnn2
icnn3 = batch_norm(
utils_lasagne.GCNNLayer([icnn2, patch_op], 64, nrings=5, nrays=16))
ffn3 = LL.DenseLayer(icnn3, 512)
ffn4 = LL.ConcatLayer([inp, ffn1, ffn2, ffn3], axis=1, cropping=None)
ffn = LL.DenseLayer(ffn4, nclasses, nonlinearity=utils_lasagne.log_softmax)
return ffn
def inceptionModule(input_layer, nfilters):
inception_net = []
inception_net.append(
dnn.MaxPool2DDNNLayer(input_layer, pool_size=3, stride=1, pad=1)) #0
inception_net.append(
dnn.Conv2DDNNLayer(inception_net[-1],
nfilters[0],
1,
flip_filters=False)) #1
inception_net.append(
dnn.Conv2DDNNLayer(input_layer, nfilters[1], 1,
flip_filters=False)) #2
inception_net.append(
dnn.Conv2DDNNLayer(input_layer, nfilters[2], 1,
flip_filters=False)) #3
inception_net.append(
dnn.Conv2DDNNLayer(inception_net[-1],
nfilters[3],
3,
pad=1,
flip_filters=False)) #4
inception_net.append(
dnn.Conv2DDNNLayer(input_layer, nfilters[4], 1,
flip_filters=False)) #5
inception_net.append(
dnn.Conv2DDNNLayer(inception_net[-1],
nfilters[5],
5,
pad=2,
flip_filters=False)) #6
inception_net.append(
ll.ConcatLayer([
inception_net[2],
inception_net[4],
inception_net[6],
inception_net[1],
])) #7
return inception_net
def build_transition_up(incoming,
incoming_skip,
layers_per_block,
growth_rate,
W_init=lasagne.init.GlorotUniform(),
b_init=None):
""""Builds a transition in the DenseNet model.
Transitions consist of the sequence: Batch Normalization, 1x1 Convolution,
2x2 Average Pooling. The channels can be compressed by specifying
0 < m <= 1, where num_channels = channels * m.
"""
network = nn.TransposedConv2DLayer(incoming,
growth_rate * layers_per_block,
filter_size=3,
stride=2,
crop='valid',
W=W_init,
b=b_init)
cropping = [None, None, 'center', 'center']
return nn.ConcatLayer([network, incoming_skip], cropping=cropping)
def rnn_encoder(x_sym, x_mask):
name = "Encoder"
n_layers = 1
n_units = 128
emb_size = 128
rnn = DropoutLSTMLayer
l_in = L.InputLayer((None, None), input_var=x_sym)
l_mask = L.InputLayer((None, None), input_var=x_mask)
l_emb = DropoutEmbeddingLayer(l_in,
dict_size,
emb_size,
name=name + '.Embedding',
dropout=0.25)
l_onehot = L.EmbeddingLayer(l_in,
dict_size,
dict_size,
W=np.eye(dict_size, dtype='float32'),
name=name + '.OneHot')
l_onehot.params[l_onehot.W].remove('trainable')
l_enc_forwards = rnn(l_emb,
num_units=n_units,
mask_input=l_mask,
name=name + '.0.Forward')
l_enc_backwards = rnn(l_emb,
num_units=n_units,
mask_input=l_mask,
backwards=True,
name=name + '.0.Backward')
l_enc = L.ConcatLayer([l_enc_forwards, l_enc_backwards], axis=2)
for i in range(n_layers - 1):
l_enc = rnn(l_enc,
num_units=n_units,
mask_input=l_mask,
name="%s.%d.Forward" % (name, i + 1),
dropout=0.25)
return l_onehot, l_enc
def build_network(W,
number_unique_tags,
longest_word,
longest_sentence,
input_var=None):
print("Building network ...")
input_layer = L.InputLayer((None, longest_sentence, longest_word),
input_var=input_var)
embed_layer = L.EmbeddingLayer(input_layer,
input_size=103,
output_size=101,
W=W)
reshape_embed = L.reshape(embed_layer, (-1, longest_word, 101))
conv_layer_1 = L.Conv1DLayer(reshape_embed, longest_word, 2)
conv_layer_2 = L.Conv1DLayer(reshape_embed, longest_word, 3)
pool_layer_1 = L.MaxPool1DLayer(conv_layer_1, pool_size=longest_word - 1)
pool_layer_2 = L.MaxPool1DLayer(conv_layer_2, pool_size=longest_word - 2)
merge_layer = L.ConcatLayer([pool_layer_1, pool_layer_2], 1)
flatten_merge = L.flatten(merge_layer, 2)
reshape_merge = L.reshape(flatten_merge,
(-1, longest_sentence, int(longest_word * 2)))
l_re = lasagne.layers.RecurrentLayer(
reshape_merge,
N_HIDDEN,
nonlinearity=lasagne.nonlinearities.sigmoid,
mask_input=None)
l_out = lasagne.layers.DenseLayer(
l_re, number_unique_tags, nonlinearity=lasagne.nonlinearities.softmax)
print "DONE BUILDING NETWORK"
return l_out
def build_expand_level(incoming,
incoming_skip,
num_filters,
nonlin,
W_init=lasagne.init.GlorotUniform(),
b_init=lasagne.init.Constant(0.01),
filter_size=3):
"""Builds a Conv-Conv-Deconv-Concat block of U-Net."""
network = nn.Conv2DLayer(incoming,
num_filters,
filter_size,
pad='same',
W=W_init,
b=b_init,
nonlinearity=nonlin)
network = nn.batch_norm(network)
network = nn.Conv2DLayer(network,
num_filters,
filter_size,
pad='same',
W=W_init,
b=b_init,
nonlinearity=nonlin)
network = nn.batch_norm(network)
network = nn.TransposedConv2DLayer(network,
num_filters // 2,
filter_size,
stride=2,
crop='valid',
W=W_init,
b=b_init,
nonlinearity=nonlin)
network = nn.batch_norm(network)
crop_mode = [None, None, 'center', 'center']
return nn.ConcatLayer([network, incoming_skip], cropping=crop_mode)