def nist_sampling_format(output, metadata, columns_list, col_maps):
"""
Output layer format for generator data plus performing random sampling
from the output softmax and bernoulli distributions.
"""
with tf.name_scope('nist_sampling_format'):
output_list = []
cur_idx = 0
for k in columns_list:
v = col_maps[k]
if isinstance(v, dict):
if len(v) == 2:
output_list.append(
tf.cast(
tf.expand_dims(
Bernoulli(logits=output[:, cur_idx]).sample(), axis=1), tf.float32)
)
cur_idx += 1
else:
output_list.append(
tf.cast(tf.expand_dims(
Categorical(logits=output[:, cur_idx: cur_idx+len(v)]).sample(), axis=1), tf.float32))
cur_idx += len(v)
elif v == 'int':
output_list.append(
tf.nn.relu(output[:, cur_idx:cur_idx+1]))
cur_idx += 1
elif v == 'int_v':
output_list.append(tf.nn.sigmoid(output[:, cur_idx:cur_idx+1]))
output_list.append(tf.nn.relu(output[:, cur_idx+1:cur_idx+2]))
cur_idx += 2
elif v == 'void':
pass
return tf.concat(output_list, axis=1)
def _init_ibp_variational_parameters(self, q_tau, q_nu, alpha, num_features, num_objects, rescale=100.):
# init tau
if q_tau is None:
q_tau = np.ones((2, num_features))
alpha_K = alpha / num_features
q_tau[0,:] = alpha_K * np.ones(shape=(1, num_features))
q_tau = q_tau + 1.5 * min(1, alpha_K)* (np.random.rand(2, num_features) - 1.5)
self._q_tau = Param(q_tau, name="q_tau", transform=transforms.positive)
else:
self._q_tau = Param(q_tau, name="q_tau", transform=transforms.positive)
# init nu
if q_nu is None:
q_nu = np.random.uniform(0., 1., (num_objects, num_features))
in_range = transforms.Chain(transforms.Logistic(a=0., b=1.), transforms.Rescale(rescale))
self._q_nu = Param(q_nu, transform=in_range, trainable=True, name="q_nu")
self.q_nu_un = tf.unstack(self.q_nu)
self.q_nu_un = [tf.reshape(nu_n, shape=(1, self.num_ibp_features)) for nu_n in self.q_nu_un]
self.sampler = [Bernoulli(probs=nu_n, validate_args=True) for nu_n in self.q_nu_un]
def __init__(self, n_input, n_list, n_y=None,y_weight=100):
'''
n_input - number of input neurons
n_list - list of numbers of neurons in the hidden layers
n_y: optional - number of features that will be given as input y during training
y_weight - relative weight of losses (VAE vs regression for y features). Trial-and-error
'''
# input data
self.X = tf.placeholder(tf.float32, shape=(None, n_input))
# input y features
if (n_y is not None):
self.y = tf.placeholder(tf.float32, shape=(None, n_y))
# encoder
self.encoder_layers = []
# input layer
previous = n_input
# current is the output of each layer (skip last because there is nothing after it)
for current in n_list[:-1]:
h = DenseLayer(previous,current)
self.encoder_layers.append(h)
previous = current
# latent features number
latent = n_list[-1]
encoder_output = DenseLayer(current,latent*2,activation='none')
self.encoder_layers.append(encoder_output)
# feed forward through encoder
c_X = self.X
for layer in self.encoder_layers:
c_X = layer.feed_forward(c_X)
# c_X now holds the output of the encoder
# first half are the means
self.means = c_X[:,:latent]
# second half are the std; must be positive; +1e-6 for smoothing
self.std = tf.nn.softplus(c_X[:,latent:]) + 1e-6
# optional loss for steered latent features
if (n_y is not None):
self.yhat = self.means[:,:n_y]
self.error = tf.losses.mean_squared_error(labels=self.y,predictions=self.yhat)
# reparameterization trick
normal = Normal(loc=self.means,scale=self.std)
self.Z = normal.sample()
# decoder
self.decoder_layers = []
previous = latent
for current in reversed(n_list[:-1]):
h = DenseLayer(previous,current)
self.decoder_layers.append(h)
previous = current
# output is the reconstruction
decoder_output = DenseLayer(previous,n_input,activation=lambda x:x)
self.decoder_layers.append(decoder_output)
#feed forward through decoder, using the sampled 'data'
c_X = self.Z
for layer in self.decoder_layers:
c_X = layer.feed_forward(c_X)
logits = c_X
# use logits for cost function below
neg_cross_entropy = -tf.nn.sigmoid_cross_entropy_with_logits(labels=self.X,
logits=logits)
neg_cross_entropy = tf.reduce_sum(neg_cross_entropy, 1)
# output
self.y_prob = Bernoulli(logits=logits)
# sample from output
self.post_pred = self.y_prob.sample()
self.post_pred_probs = tf.nn.sigmoid(logits)
# generate 'de-novo' output
self.gen = tf.Variable(0)
Z_std = Normal(0.0,1.0).sample([self.gen,latent])
c_X = Z_std
for layer in self.decoder_layers:
c_X = layer.feed_forward(c_X)
logits = c_X
prior_pred_dist = Bernoulli(logits=logits)
self.prior_pred = prior_pred_dist.sample()
self.prior_pred_probs = tf.nn.sigmoid(logits)
# manually input Z
self.Z_input = tf.placeholder(np.float32, shape=(None, latent))
c_X = self.Z_input
for layer in self.decoder_layers:
c_X = layer.feed_forward(c_X)
logits = c_X
self.manual_prior_prob = tf.nn.sigmoid(logits)
# cost function
# Kullback–Leibler divergence
kl = -tf.log(self.std) + 0.5*(self.std**2 + self.means**2) - 0.5
kl = tf.reduce_sum(kl, axis=1)
# ELBO
self.elbo = tf.reduce_sum(neg_cross_entropy - kl)
if (n_y is None):
# only ELBO
self.optimizer = tf.train.RMSPropOptimizer(learning_rate=0.001).minimize(-self.elbo)
else:
# weighted regression loss and ELBO
self.optimizer = tf.train.RMSPropOptimizer(learning_rate=0.001).minimize(
tf.reduce_sum(y_weight*self.error-self.elbo))
self.init = tf.global_variables_initializer()
self.session = tf.Session()
self.session.run(self.init)
def __init__(self, n_input, n_list, ent_weight=1, lr=0.001):
'''
number of input neurons and a list of the number of neurons in the encoder hidden layers
The last number provided should be the number of latent features desired
The decoder will have an inverted architecture
Note: the actual number of neurons in the last layer of the encoder will be x2, for mean and std
'''
# input data
self.X = tf.placeholder(tf.float32, shape=(None, n_input))
# encoder
self.encoder_layers = []
# input layer
previous = n_input
# in case there is only one hidden layer (for loop will be skipped)
current = n_input
# current is the output of each layer (skip last because there is nothing after it)
for current in n_list[:-1]:
h = DenseLayer(previous, current)
self.encoder_layers.append(h)
previous = current
# latent features number
latent = n_list[-1]
encoder_output = DenseLayer(current, latent * 2, activation='none')
self.encoder_layers.append(encoder_output)
# feed forward through encoder
c_X = self.X
for layer in self.encoder_layers:
c_X = layer.feed_forward(c_X)
# c_X now holds the output of the encoder
# first half are the means
self.means = c_X[:, :latent]
# second half are the std; must be positive; +1e-6 for smoothing
self.std = tf.nn.softplus(c_X[:, latent:]) + 1e-6
# reparameterization trick
normal = Normal(loc=self.means, scale=self.std)
self.Z = normal.sample()
# decoder
self.decoder_layers = []
previous = latent
for current in reversed(n_list[:-1]):
h = DenseLayer(previous, current)
self.decoder_layers.append(h)
previous = current
# output is the reconstruction
decoder_output = DenseLayer(previous, n_input, activation=lambda x: x)
self.decoder_layers.append(decoder_output)
#feed forward through decoder, using the sampled 'data'
c_X = self.Z
for layer in self.decoder_layers:
c_X = layer.feed_forward(c_X)
logits = c_X
# use logits for cost function below
neg_cross_entropy = -tf.nn.sigmoid_cross_entropy_with_logits(
labels=self.X, logits=logits)
neg_cross_entropy = tf.reduce_sum(neg_cross_entropy, 1)
# output
self.y_prob = Bernoulli(logits=logits)
# sample from output
self.post_pred = self.y_prob.sample()
self.post_pred_probs = tf.nn.sigmoid(logits)
# generate 'de-novo' output
self.gen = tf.Variable(0)
Z_std = Normal(0.0, 1.0).sample([self.gen, latent])
c_X = Z_std
for layer in self.decoder_layers:
c_X = layer.feed_forward(c_X)
logits = c_X
prior_pred_dist = Bernoulli(logits=logits)
self.prior_pred = prior_pred_dist.sample()
self.prior_pred_probs = tf.nn.sigmoid(logits)
# manually input Z
self.Z_input = tf.placeholder(np.float32, shape=(None, latent))
c_X = self.Z_input
for layer in self.decoder_layers:
c_X = layer.feed_forward(c_X)
logits = c_X
self.manual_prior_prob = tf.nn.sigmoid(logits)
# cost function
# Kullback–Leibler divergence
kl = -tf.log(self.std) + 0.5 * (self.std**2 + self.means**2) - 0.5
kl = tf.reduce_sum(kl, axis=1)
# ELBO
self.elbo = tf.reduce_sum(ent_weight * neg_cross_entropy - kl)
self.optimizer = tf.train.RMSPropOptimizer(
learning_rate=lr).minimize(-self.elbo)
self.init = tf.global_variables_initializer()
self.session = tf.Session()
self.session.run(self.init)
def __init__(self,
image_shape=(128, 128, 3),
conv_param=(3, 16, True),
n_list=[256, 32]):
# input data
self.X = tf.placeholder(tf.float32, shape=(None, *image_shape))
# encoder
self.encoder_layers = []
# convolution layer
h = Conv2DLayer(image_shape[2], conv_param[0], conv_param[1],
conv_param[2])
self.encoder_layers.append(h)
# flatten layer
self.encoder_layers.append(FlattenLayer())
# calculate number of input neurons to the FC layer
previous = image_shape[0] * image_shape[1] * conv_param[1]
if conv_param[2]:
previous = previous // 4
# save for later
flat = previous
# current is the output of each layer (skip last because there is nothing after it)
for current in n_list[:-1]:
h = DenseLayer(previous, current)
self.encoder_layers.append(h)
previous = current
# latent features number
latent = n_list[-1]
encoder_output = DenseLayer(current, latent * 2, activation='none')
self.encoder_layers.append(encoder_output)
# feed forward through encoder
c_X = self.X
for layer in self.encoder_layers:
c_X = layer.feed_forward(c_X)
# c_X now holds the output of the encoder
# first half are the means
self.means = c_X[:, :latent]
# second half are the std; must be positive; +1e-6 for smoothing
self.std = tf.nn.softplus(c_X[:, latent:]) + 1e-6
# reparameterization trick
normal = Normal(loc=self.means, scale=self.std)
self.Z = normal.sample()
# decoder
self.decoder_layers = []
previous = latent
for current in reversed(n_list[:-1]):
h = DenseLayer(previous, current)
self.decoder_layers.append(h)
previous = current
decoder_output = DenseLayer(previous, flat, activation=lambda x: x)
self.decoder_layers.append(decoder_output)
#feed forward through decoder, using the sampled 'data'
c_X = self.Z
for layer in self.decoder_layers:
c_X = layer.feed_forward(c_X)
# reshape
if (conv_param[2]):
shape = [
-1, image_shape[0] // 2, image_shape[0] // 2, conv_param[1]
]
else:
shape = [-1, image_shape[0], image_shape[0], conv_param[1]]
c_X = tf.reshape(c_X, shape)
# convolution transpose
self.trans_k = tf.Variable(
tf.truncated_normal(
[conv_param[0], conv_param[0], image_shape[2], conv_param[1]],
stddev=0.1))
if (conv_param[2]):
strides = (1, 2, 2, 1)
else:
strides = (1, 1, 1, 1)
c_X = tf.nn.conv2d_transpose(c_X,
self.trans_k,
strides=strides,
padding='SAME',
output_shape=[50, *image_shape])
# output logit
logits = c_X
# use logits for cost function below
neg_cross_entropy = -tf.nn.sigmoid_cross_entropy_with_logits(
labels=self.X, logits=logits)
neg_cross_entropy = tf.reduce_sum(neg_cross_entropy, 1)
# output
self.y_prob = Bernoulli(logits=logits)
# sample from output
self.post_pred = self.y_prob.sample()
self.post_pred_probs = tf.nn.sigmoid(logits)
# generate 'de-novo' output
self.gen = tf.Variable(0)
Z_std = Normal(0.0, 1.0).sample([self.gen, latent])
c_X = Z_std
for layer in self.decoder_layers:
c_X = layer.feed_forward(c_X)
c_X = tf.reshape(c_X, shape)
c_X = tf.nn.conv2d_transpose(c_X,
self.trans_k,
strides=strides,
padding='SAME',
output_shape=[50, *image_shape])
logits = c_X
prior_pred_dist = Bernoulli(logits=logits)
self.prior_pred = prior_pred_dist.sample()
self.prior_pred_probs = tf.nn.sigmoid(logits)
# manually input Z
self.Z_input = tf.placeholder(np.float32, shape=(None, latent))
c_X = self.Z_input
for layer in self.decoder_layers:
c_X = layer.feed_forward(c_X)
logits = c_X
self.manual_prior_prob = tf.nn.sigmoid(logits)
# cost function
# Kullback–Leibler divergence
kl = -tf.log(self.std) + 0.5 * (self.std**2 + self.means**2) - 0.5
kl = tf.reduce_sum(kl, axis=1)
# ELBO
self.elbo = tf.reduce_sum(neg_cross_entropy - kl)
self.optimizer = tf.train.RMSPropOptimizer(
learning_rate=0.001).minimize(-self.elbo)
self.init = tf.global_variables_initializer()
self.session = tf.Session()
self.session.run(self.init)