def __init__(self, input_dim, output_dim, hidden_size, init_ranges,
**kwargs):
linear1 = LinearMaxout(input_dim=input_dim,
output_dim=hidden_size,
num_pieces=2,
name='linear1')
linear2 = LinearMaxout(input_dim=hidden_size,
output_dim=hidden_size,
num_pieces=2,
name='linear2')
linear3 = Linear(input_dim=hidden_size, output_dim=output_dim)
logistic = Logistic()
bricks = [
linear1,
BatchNormalization(input_dim=hidden_size, name='bn2'), linear2,
BatchNormalization(input_dim=hidden_size, name='bnl'), linear3,
logistic
]
for init_range, b in zip(init_ranges, (linear1, linear2, linear3)):
b.biases_init = initialization.Constant(0)
b.weights_init = initialization.Uniform(width=init_range)
kwargs.setdefault('use_bias', False)
super(ConcatenateClassifier, self).__init__([b.apply for b in bricks],
**kwargs)
def create_model_brick():
decoder = MLP(
dims=[NLAT, GEN_HIDDEN, GEN_HIDDEN, GEN_HIDDEN, GEN_HIDDEN, INPUT_DIM],
activations=[Sequence([BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply],
name='decoder_h1'),
Sequence([BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply],
name='decoder_h2'),
Sequence([BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply],
name='decoder_h3'),
Sequence([BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply],
name='decoder_h4'),
Identity(name='decoder_out')],
use_bias=False,
name='decoder')
discriminator = Sequence(
application_methods=[
LinearMaxout(
input_dim=INPUT_DIM,
output_dim=DISC_HIDDEN,
num_pieces=MAXOUT_PIECES,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='discriminator_h1').apply,
LinearMaxout(
input_dim=DISC_HIDDEN,
output_dim=DISC_HIDDEN,
num_pieces=MAXOUT_PIECES,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='discriminator_h2').apply,
LinearMaxout(
input_dim=DISC_HIDDEN,
output_dim=DISC_HIDDEN,
num_pieces=MAXOUT_PIECES,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='discriminator_h3').apply,
Linear(
input_dim=DISC_HIDDEN,
output_dim=1,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='discriminator_out').apply],
name='discriminator')
gan = GAN(decoder=decoder, discriminator=discriminator,
weights_init=GAUSSIAN_INIT, biases_init=ZERO_INIT, name='gan')
gan.push_allocation_config()
decoder.linear_transformations[-1].use_bias = True
gan.initialize()
return gan
def test_linear_maxout():
x = tensor.matrix()
linear_maxout = LinearMaxout(input_dim=16, output_dim=8, num_pieces=3,
weights_init=Constant(2),
biases_init=Constant(1))
y = linear_maxout.apply(x)
linear_maxout.initialize()
x_val = numpy.ones((4, 16), dtype=theano.config.floatX)
assert_allclose(
y.eval({x: x_val}),
(x_val.dot(2 * numpy.ones((16, 24))) +
numpy.ones((4, 24))).reshape(4, 8, 3).max(2))
def test_linear_maxout():
x = tensor.matrix()
linear_maxout = LinearMaxout(input_dim=16, output_dim=8, num_pieces=3,
weights_init=Constant(2),
biases_init=Constant(1))
y = linear_maxout.apply(x)
linear_maxout.initialize()
x_val = numpy.ones((4, 16), dtype=theano.config.floatX)
assert_allclose(
y.eval({x: x_val}),
(x_val.dot(2 * numpy.ones((16, 24))) +
numpy.ones((4, 24))).reshape(4, 8, 3).max(2))
def __init__(self, visual_dim, textual_dim, output_dim, hidden_size,
init_ranges, **kwargs):
(visual_range, textual_range, linear_range_1, linear_range_2,
linear_range_3) = init_ranges
visual_layer = FeedforwardSequence([
BatchNormalization(input_dim=visual_dim).apply,
LinearMaxout(
input_dim=visual_dim,
output_dim=hidden_size,
weights_init=initialization.Uniform(width=visual_range),
use_bias=False,
biases_init=initialization.Constant(0),
num_pieces=2).apply
],
name='visual_layer')
textual_layer = FeedforwardSequence([
BatchNormalization(input_dim=textual_dim).apply,
LinearMaxout(
input_dim=textual_dim,
output_dim=hidden_size,
weights_init=initialization.Uniform(width=textual_range),
biases_init=initialization.Constant(0),
use_bias=False,
num_pieces=2).apply
],
name='textual_layer')
logistic_mlp = MLPGenreClassifier(
hidden_size, output_dim, hidden_size,
[linear_range_1, linear_range_2, linear_range_3])
# logistic_mlp = Sequence([
# BatchNormalization(input_dim=hidden_size, name='bn1').apply,
# Linear(hidden_size, output_dim, name='linear_output', use_bias=False,
# weights_init=initialization.Uniform(width=linear_range_1)).apply,
# Logistic().apply
#], name='logistic_mlp')
children = [visual_layer, textual_layer, logistic_mlp]
kwargs.setdefault('use_bias', False)
kwargs.setdefault('children', children)
super(LinearSumClassifier, self).__init__(**kwargs)
def create_model_brick():
encoder_mapping = MLP(
dims=[2 * INPUT_DIM, GEN_HIDDEN, GEN_HIDDEN, NLAT],
activations=[
Sequence([
BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply
],
name='encoder_h1'),
Sequence([
BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply
],
name='encoder_h2'),
Identity(name='encoder_out')
],
use_bias=False,
name='encoder_mapping')
encoder = COVConditional(encoder_mapping, (INPUT_DIM, ),
name='encoder')
decoder_mapping = MLP(dims=[
NLAT, GEN_HIDDEN, GEN_HIDDEN, GEN_HIDDEN, GEN_HIDDEN, INPUT_DIM
],
activations=[
Sequence([
BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply
],
name='decoder_h1'),
Sequence([
BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply
],
name='decoder_h2'),
Sequence([
BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply
],
name='decoder_h3'),
Sequence([
BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply
],
name='decoder_h4'),
Identity(name='decoder_out')
],
use_bias=False,
name='decoder_mapping')
decoder = DeterministicConditional(decoder_mapping, name='decoder')
x_discriminator = Identity(name='x_discriminator')
z_discriminator = Identity(name='z_discriminator')
joint_discriminator = Sequence(application_methods=[
LinearMaxout(input_dim=INPUT_DIM + NLAT,
output_dim=DISC_HIDDEN,
num_pieces=MAXOUT_PIECES,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='discriminator_h1').apply,
LinearMaxout(input_dim=DISC_HIDDEN,
output_dim=DISC_HIDDEN,
num_pieces=MAXOUT_PIECES,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='discriminator_h2').apply,
LinearMaxout(input_dim=DISC_HIDDEN,
output_dim=DISC_HIDDEN,
num_pieces=MAXOUT_PIECES,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='discriminator_h3').apply,
Linear(input_dim=DISC_HIDDEN,
output_dim=1,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='discriminator_out').apply
],
name='joint_discriminator')
discriminator = XZJointDiscriminator(x_discriminator,
z_discriminator,
joint_discriminator,
name='discriminator')
ali = ALI(encoder=encoder,
decoder=decoder,
discriminator=discriminator,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='ali')
ali.push_allocation_config()
encoder_mapping.linear_transformations[-1].use_bias = True
decoder_mapping.linear_transformations[-1].use_bias = True
ali.initialize()
print("Number of parameters in discriminator: {}".format(
numpy.sum([
numpy.prod(v.shape.eval()) for v in Selector(
ali.discriminator).get_parameters().values()
])))
print("Number of parameters in encoder: {}".format(
numpy.sum([
numpy.prod(v.shape.eval())
for v in Selector(ali.encoder).get_parameters().values()
])))
print("Number of parameters in decoder: {}".format(
numpy.sum([
numpy.prod(v.shape.eval())
for v in Selector(ali.decoder).get_parameters().values()
])))
return ali
def __init__(self, config_dict, init_type="xavier", **kwargs):
super(CharRNNModel, self).__init__(**kwargs)
self.batch_size = config_dict["batch_size"]
self.num_subwords = config_dict["num_subwords"]
self.num_words = config_dict["num_words"]
self.subword_embedding_size = config_dict["subword_embedding_size"]
self.input_vocab_size = config_dict["input_vocab_size"]
self.output_vocab_size = config_dict["output_vocab_size"]
self.subword_RNN_hidden_state_size = config_dict["subword_RNN_hidden_state_size"]
self.table_width = config_dict["table_width"]
self.max_out_dim = config_dict["max_out_dim"]
self.max_out_K = config_dict["max_out_K"]
self.lookup = LookupTable(length=self.input_vocab_size, dim=self.subword_embedding_size, name="input_lookup")
self.lookup.weights_init = Uniform(width=self.table_width)
self.lookup.biases_init = Constant(0)
if init_type == "xavier":
linear_init = XavierInitializationOriginal(self.subword_embedding_size, self.subword_RNN_hidden_state_size)
lstm_init = XavierInitializationOriginal(self.subword_embedding_size, self.subword_RNN_hidden_state_size)
else: # default is gaussian
linear_init = IsotropicGaussian()
lstm_init = IsotropicGaussian()
# The `inputs` are then split in this order: Input gates, forget gates, cells and output gates
self.linear_forward = Linear(
input_dim=self.subword_embedding_size,
output_dim=self.subword_RNN_hidden_state_size * 4,
name="linear_forward",
weights_init=linear_init,
biases_init=Constant(0.0),
)
self.language_model = LSTM(
dim=self.subword_RNN_hidden_state_size,
activation=Tanh(),
name="language_model_RNN",
weights_init=lstm_init,
biases_init=Constant(0.0),
)
self.max_out = LinearMaxout(
self.subword_RNN_hidden_state_size,
self.max_out_dim,
self.max_out_K,
name="max_out",
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0),
)
self.softmax_linear = Linear(
self.max_out_dim,
self.output_vocab_size,
name="soft_max_linear",
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0),
)
self.softmax = NDimensionalSoftmax()
self.children = [
self.lookup,
self.linear_forward,
self.language_model,
self.max_out,
self.softmax_linear,
self.softmax,
]
class CharRNNModel(Initializable):
"""
A model for testing that the components of my more complex models work.
This is just a model that predicts one character at a time using a LSTM layer
"""
def __init__(self, config_dict, init_type="xavier", **kwargs):
super(CharRNNModel, self).__init__(**kwargs)
self.batch_size = config_dict["batch_size"]
self.num_subwords = config_dict["num_subwords"]
self.num_words = config_dict["num_words"]
self.subword_embedding_size = config_dict["subword_embedding_size"]
self.input_vocab_size = config_dict["input_vocab_size"]
self.output_vocab_size = config_dict["output_vocab_size"]
self.subword_RNN_hidden_state_size = config_dict["subword_RNN_hidden_state_size"]
self.table_width = config_dict["table_width"]
self.max_out_dim = config_dict["max_out_dim"]
self.max_out_K = config_dict["max_out_K"]
self.lookup = LookupTable(length=self.input_vocab_size, dim=self.subword_embedding_size, name="input_lookup")
self.lookup.weights_init = Uniform(width=self.table_width)
self.lookup.biases_init = Constant(0)
if init_type == "xavier":
linear_init = XavierInitializationOriginal(self.subword_embedding_size, self.subword_RNN_hidden_state_size)
lstm_init = XavierInitializationOriginal(self.subword_embedding_size, self.subword_RNN_hidden_state_size)
else: # default is gaussian
linear_init = IsotropicGaussian()
lstm_init = IsotropicGaussian()
# The `inputs` are then split in this order: Input gates, forget gates, cells and output gates
self.linear_forward = Linear(
input_dim=self.subword_embedding_size,
output_dim=self.subword_RNN_hidden_state_size * 4,
name="linear_forward",
weights_init=linear_init,
biases_init=Constant(0.0),
)
self.language_model = LSTM(
dim=self.subword_RNN_hidden_state_size,
activation=Tanh(),
name="language_model_RNN",
weights_init=lstm_init,
biases_init=Constant(0.0),
)
self.max_out = LinearMaxout(
self.subword_RNN_hidden_state_size,
self.max_out_dim,
self.max_out_K,
name="max_out",
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0),
)
self.softmax_linear = Linear(
self.max_out_dim,
self.output_vocab_size,
name="soft_max_linear",
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0),
)
self.softmax = NDimensionalSoftmax()
self.children = [
self.lookup,
self.linear_forward,
self.language_model,
self.max_out,
self.softmax_linear,
self.softmax,
]
@application(inputs=["features", "features_mask", "targets", "targets_mask"], outputs=["cost"])
def apply(self, features, features_mask, targets, targets_mask):
subword_embeddings = self.lookup.apply(features)
sentence_embeddings = self.language_model.apply(
self.linear_forward.apply(subword_embeddings), mask=features_mask
)[
0
] # [0] = hidden states, [1] = cells
linear_output = self.softmax_linear.apply(self.max_out.apply(sentence_embeddings))
cost = self.softmax.categorical_cross_entropy(targets, linear_output, extra_ndim=1).mean()
cost.name = "cost"
return ((cost * targets_mask).sum()) / targets_mask.sum()
def create_model_brick(self):
decoder = MLP(
dims=[
self._config["num_zdim"], self._config["gen_hidden_size"],
self._config["gen_hidden_size"],
self._config["gen_hidden_size"],
self._config["gen_hidden_size"], self._config["num_xdim"]
],
activations=[
Sequence([
BatchNormalization(self._config["gen_hidden_size"]).apply,
self._config["gen_activation"]().apply
],
name='decoder_h1'),
Sequence([
BatchNormalization(self._config["gen_hidden_size"]).apply,
self._config["gen_activation"]().apply
],
name='decoder_h2'),
Sequence([
BatchNormalization(self._config["gen_hidden_size"]).apply,
self._config["gen_activation"]().apply
],
name='decoder_h3'),
Sequence([
BatchNormalization(self._config["gen_hidden_size"]).apply,
self._config["gen_activation"]().apply
],
name='decoder_h4'),
Identity(name='decoder_out')
],
use_bias=False,
name='decoder')
discriminator = Sequence(application_methods=[
LinearMaxout(input_dim=self._config["num_xdim"] *
self._config["num_packing"],
output_dim=self._config["disc_hidden_size"],
num_pieces=self._config["disc_maxout_pieces"],
weights_init=IsotropicGaussian(
self._config["weights_init_std"]),
biases_init=self._config["biases_init"],
name='discriminator_h1').apply,
LinearMaxout(input_dim=self._config["disc_hidden_size"],
output_dim=self._config["disc_hidden_size"],
num_pieces=self._config["disc_maxout_pieces"],
weights_init=IsotropicGaussian(
self._config["weights_init_std"]),
biases_init=self._config["biases_init"],
name='discriminator_h2').apply,
LinearMaxout(input_dim=self._config["disc_hidden_size"],
output_dim=self._config["disc_hidden_size"],
num_pieces=self._config["disc_maxout_pieces"],
weights_init=IsotropicGaussian(
self._config["weights_init_std"]),
biases_init=self._config["biases_init"],
name='discriminator_h3').apply,
Linear(input_dim=self._config["disc_hidden_size"],
output_dim=1,
weights_init=IsotropicGaussian(
self._config["weights_init_std"]),
biases_init=self._config["biases_init"],
name='discriminator_out').apply
],
name='discriminator')
gan = PacGAN(decoder=decoder,
discriminator=discriminator,
weights_init=IsotropicGaussian(
self._config["weights_init_std"]),
biases_init=self._config["biases_init"],
name='gan')
gan.push_allocation_config()
decoder.linear_transformations[-1].use_bias = True
gan.initialize()
print("Number of parameters in discriminator: {}".format(
numpy.sum([
numpy.prod(v.shape.eval())
for v in Selector(gan.discriminator).get_parameters().values()
])))
print("Number of parameters in decoder: {}".format(
numpy.sum([
numpy.prod(v.shape.eval())
for v in Selector(gan.decoder).get_parameters().values()
])))
return gan
