def _module_fn():
"""
Function building the module
"""
feature_layer = tf.placeholder(
tf.float32,
shape=[None, None, None, None, nchannels],
name='input')
obs_layer = tf.placeholder(tf.float32,
shape=[None, None, None, None, n_y],
name='observations')
# Builds the neural network
net = slim.conv3d(feature_layer,
16,
5,
activation_fn=tf.nn.leaky_relu,
padding='valid')
#net = wide_resnet(feature_layer, 8, activation_fn=tf.nn.leaky_relu, is_training=is_training)
net = wide_resnet(net,
16,
activation_fn=tf.nn.leaky_relu,
keep_prob=dropout,
is_training=is_training)
net = wide_resnet(net,
32,
activation_fn=tf.nn.leaky_relu,
keep_prob=dropout,
is_training=is_training)
net = wide_resnet(net,
32,
activation_fn=tf.nn.leaky_relu,
keep_prob=dropout,
is_training=is_training)
net = slim.conv3d(net, 32, 3, activation_fn=tf.nn.tanh)
# Define the probabilistic layer
#out_rate = slim.conv3d(net, 1, 1, activation_fn=tf.nn.relu)
#out_rate = tf.math.add(out_rate, 1e-6, name='rate')
net = slim.conv3d(net, n_mixture * n_y, 1, activation_fn=tf.nn.relu)
cube_size = tf.shape(obs_layer)[1]
out_rate = tf.reshape(net, [-1, cube_size, cube_size, cube_size, n_y])
out_rate = tf.math.add(out_rate, 1e-6, name='rate')
pdf = tfd.Poisson(rate=out_rate)
# Define a function for sampling, and a function for estimating the log likelihood
sample = tf.squeeze(pdf.sample())
loglik = pdf.log_prob(obs_layer)
hub.add_signature(inputs={
'features': feature_layer,
'labels': obs_layer
},
outputs={
'sample': sample,
'loglikelihood': loglik
})
def module_fn():
'''Define network here'''
x = tf.placeholder(
tf.float32,
shape=[None, cube_sizeft, cube_sizeft, cube_sizeft, nchannels],
name='input')
y = tf.placeholder(tf.float32,
shape=[None, cube_size, cube_size, cube_size, 1],
name='labels')
keepprob = tf.placeholder(tf.float32, name='keepprob')
print('Shape of training and testing data is : ',
x.shape,
y.shape,
file=fname)
#
wregwt, bregwt = 0.001, 0.001
if wregwt: wreg = slim.regularizers.l2_regularizer(wregwt)
else: wreg = None
if bregwt: breg = slim.regularizers.l2_regularizer(bregwt)
else: breg = None
print('Regularizing weights are : ', wregwt, bregwt, file=fname)
#
net = slim.conv3d(x,
16,
5,
activation_fn=tf.nn.leaky_relu,
padding='valid',
weights_regularizer=wreg,
biases_regularizer=breg)
net = wide_resnet(net,
32,
keep_prob=keepprob,
activation_fn=tf.nn.leaky_relu)
net = wide_resnet(net,
64,
keep_prob=keepprob,
activation_fn=tf.nn.leaky_relu)
net = wide_resnet(net,
32,
keep_prob=keepprob,
activation_fn=tf.nn.leaky_relu)
net = wide_resnet(net,
16,
keep_prob=keepprob,
activation_fn=tf.nn.leaky_relu)
net = slim.conv3d(net, 1, 3, activation_fn=None)
net = tf.identity(net, name='logits')
pred = tf.nn.sigmoid(net, name='prediction')
#
inputs = dict(input=x, label=y, keepprob=keepprob)
outputs = dict(default=net, prediction=pred)
hub.add_signature(inputs=inputs, outputs=outputs)
def _module_fn():
"""
Function building the module
"""
feature_layer = tf.placeholder(
tf.float32,
shape=[None, None, None, None, nchannels],
name='input')
obs_layer = tf.placeholder(tf.float32,
shape=[None, None, None, None, n_y],
name='observations')
# Builds the neural network
net = slim.conv3d(feature_layer,
16,
5,
activation_fn=tf.nn.leaky_relu,
padding='valid')
#net = wide_resnet(feature_layer, 8, activation_fn=tf.nn.leaky_relu, is_training=is_training)
net = wide_resnet(net,
16,
activation_fn=tf.nn.leaky_relu,
keep_prob=dropout,
is_training=is_training)
net = wide_resnet(net,
32,
activation_fn=tf.nn.leaky_relu,
keep_prob=dropout,
is_training=is_training)
net = wide_resnet(net,
32,
activation_fn=tf.nn.leaky_relu,
keep_prob=dropout,
is_training=is_training)
net = slim.conv3d(net, 32, 3, activation_fn=tf.nn.tanh)
# Define the probabilistic layer
net = slim.conv3d(net, n_mixture * 3 * n_y, 1, activation_fn=None)
cube_size = tf.shape(obs_layer)[1]
net = tf.reshape(
net, [-1, cube_size, cube_size, cube_size, n_y, n_mixture * 3])
# net = tf.reshape(net, [None, None, None, None, n_y, n_mixture*3])
loc, unconstrained_scale, logits = tf.split(net,
num_or_size_splits=3,
axis=-1)
scale = tf.nn.softplus(unconstrained_scale)
# Form mixture of discretized logistic distributions. Note we shift the
# logistic distribution by -0.5. This lets the quantization capture "rounding"
# intervals, `(x-0.5, x+0.5]`, and not "ceiling" intervals, `(x-1, x]`.
discretized_logistic_dist = tfd.QuantizedDistribution(
distribution=tfd.TransformedDistribution(
distribution=tfd.Logistic(loc=loc, scale=scale),
bijector=tfb.AffineScalar(shift=-0.5)),
low=0.,
high=2.**4 - 1)
mixture_dist = tfd.MixtureSameFamily(
mixture_distribution=tfd.Categorical(logits=logits),
components_distribution=discretized_logistic_dist)
# Define a function for sampling, and a function for estimating the log likelihood
sample = tf.squeeze(mixture_dist.sample())
loglik = mixture_dist.log_prob(obs_layer)
hub.add_signature(inputs={
'features': feature_layer,
'labels': obs_layer
},
outputs={
'sample': sample,
'loglikelihood': loglik
})
def _module_fn():
"""
Function building the module
"""
feature_layer = tf.placeholder(
tf.float32,
shape=[None, None, None, None, nchannels],
name='input')
obs_layer = tf.placeholder(tf.float32,
shape=[None, None, None, None, n_y],
name='observations')
conditional_im = wide_resnet(feature_layer,
16,
activation_fn=tf.nn.leaky_relu,
keep_prob=dropout,
is_training=is_training)
conditional_im = wide_resnet(conditional_im,
16,
activation_fn=tf.nn.leaky_relu,
keep_prob=dropout,
is_training=is_training)
conditional_im = wide_resnet(conditional_im,
1,
activation_fn=tf.nn.leaky_relu,
keep_prob=dropout,
is_training=is_training)
conditional_im = tf.concat((feature_layer, conditional_im), -1)
# Builds the neural network
ul = [[obs_layer]]
for i in range(10):
ul.append(
PixelCNN3Dlayer(i,
ul[i],
f_map=f_map,
full_horizontal=True,
h=None,
conditional_im=conditional_im,
cfilter_size=cfilter_size,
gatedact='sigmoid'))
h_stack_in = ul[-1][-1]
with tf.variable_scope("fc_1"):
fc1 = GatedCNN([1, 1, 1, 1],
h_stack_in,
orientation=None,
gated=False,
mask='b').output()
with tf.variable_scope("fc_2"):
fc2 = GatedCNN([1, 1, 1, n_mixture * 3 * n_y],
fc1,
orientation=None,
gated=False,
mask='b',
activation=False).output()
cube_size = tf.shape(obs_layer)[1]
net = tf.reshape(
fc2, [-1, cube_size, cube_size, cube_size, n_y, n_mixture * 3])
loc, unconstrained_scale, logits = tf.split(net,
num_or_size_splits=3,
axis=-1)
scale = tf.nn.softplus(unconstrained_scale) + 1e-3
# Form mixture of discretized logistic distributions. Note we shift the
# logistic distribution by -0.5. This lets the quantization capture "rounding"
# intervals, `(x-0.5, x+0.5]`, and not "ceiling" intervals, `(x-1, x]`.
# discretized_logistic_dist = tfd.QuantizedDistribution(
# distribution=tfd.TransformedDistribution(
# distribution=tfd.Logistic(loc=loc, scale=scale),
# bijector=tfb.AffineScalar(shift=-0.5)),
# low=0.,
# high=2.**3-1)
mixture_dist = tfd.MixtureSameFamily(
mixture_distribution=tfd.Categorical(logits=logits),
components_distribution=tfd.Normal(loc, scale))
# Define a function for sampling, and a function for estimating the log likelihood
#sample = tf.squeeze(mixture_dist.sample())
sample = mixture_dist.sample()
loglik = mixture_dist.log_prob(obs_layer)
hub.add_signature(inputs={
'features': feature_layer,
'labels': obs_layer
},
outputs={
'sample': sample,
'loglikelihood': loglik,
'loc': loc,
'scale': scale,
'logits': logits
})
activation_fn=tf.nn.leaky_relu,
padding='valid',
weights_regularizer=wreg,
biases_regularizer=breg)
#net = tf.nn.dropout(net, keep_prob=keepprob)
#net = slim.conv3d(net, 32, 5, activation_fn=tf.nn.leaky_relu, padding='valid', weights_regularizer=wreg, biases_regularizer=breg)
#net = tf.nn.dropout(net, keep_prob=keepprob)
#net = slim.conv3d(net, 128, 5, activation_fn=tf.nn.leaky_relu, weights_regularizer=wreg, biases_regularizer=breg)
#net = tf.nn.netout(net, keep_prob=keepprob)
#net = slim.conv3d(net, 64, 3, activation_fn=tf.nn.leaky_relu, weights_regularizer=wreg, biases_regularizer=breg)
#net = tf.nn.netout(net, keep_prob=keepprob)
#net = slim.conv3d(net, 16, 3, activation_fn=tf.nn.leaky_relu, weights_regularizer=wreg, biases_regularizer=breg)
#net = tf.nn.netout(net, keep_prob=keepprob)
#net = slim.conv3d(net, 1, 3, activation_fn=None, weights_regularizer=wreg, biases_regularizer=breg)
#pred = tf.nn.sigmoid(net, name='prediction')
net = wide_resnet(net, 32, keep_prob=keepprob, activation_fn=tf.nn.leaky_relu)
net = wide_resnet(net, 64, keep_prob=keepprob, activation_fn=tf.nn.leaky_relu)
net = wide_resnet(net, 32, keep_prob=keepprob, activation_fn=tf.nn.leaky_relu)
net = wide_resnet(net, 16, keep_prob=keepprob, activation_fn=tf.nn.leaky_relu)
net = slim.conv3d(net, 1, 3, activation_fn=None)
pred = tf.nn.sigmoid(net, name='prediction')
#####
loss = tf.losses.sigmoid_cross_entropy(y, net)
optimizer = tf.train.AdamOptimizer(learning_rate=lr, name='optimizer')
opt_op = optimizer.minimize(loss, name='minimize')
#############################
###Train
sess = tf.Session()
tf.reset_default_graph()
x = tf.placeholder(tf.float32, shape=[None, cube_sizeft, cube_sizeft, cube_sizeft, nchannels], name='input')
ycen = tf.placeholder(tf.float32, shape=[None, cube_size, cube_size, cube_size, 1], name='centrals')
ysat = tf.placeholder(tf.float32, shape=[None, cube_size, cube_size, cube_size, 1], name='satellites')
ywt = tf.placeholder(tf.float32, shape=[None, cube_size, cube_size, cube_size], name='weights')
##x = tf.placeholder(tf.float32, shape=[None, cube_sizeft, cube_sizeft, cube_sizeft, nchannels], name='input')
##y = tf.placeholder(tf.float32, shape=[None, cube_size, cube_size, cube_size, 1], name='labels')
##m = tf.placeholder(tf.float32, shape=[None, cube_size, cube_size, cube_size], name='mask')
keepprob = tf.placeholder(tf.float32, name='keepprob')
lr = tf.placeholder(tf.float32, name='learningrate')
net = slim.conv3d(x, 16, 5, activation_fn=tf.nn.leaky_relu, padding='valid')
# net = slim.conv3d(net, 32, 5, activation_fn=None, padding='valid')
net = wide_resnet(net, 32, activation_fn=tf.nn.leaky_relu, keep_prob=keepprob)
net = wide_resnet(net, 32, activation_fn=tf.nn.leaky_relu, keep_prob=keepprob)
net = wide_resnet(net, 32, activation_fn=tf.nn.leaky_relu, keep_prob=keepprob)
net = slim.conv3d(net, 64, 1, activation_fn=tf.nn.relu6)
net = tf.nn.dropout(net, keep_prob=keepprob)
# Create mixture components from network output
#out_rate = slim.conv3d(net, 1, 3, activation_fn=tf.nn.relu)
out_rate = slim.conv3d(net, 1, 1, activation_fn=tf.nn.relu,
#weights_initializer=tf.initializers.random_normal(mean=1, stddev=0.25))
weights_initializer=tf.initializers.random_uniform(minval=0.01, maxval=1))
out_rate = tf.math.add(out_rate, 1e-8, name='rate')
# Predicted mask
out_mask = slim.conv3d(net, 1, 1, activation_fn=None)
pred_mask = tf.nn.sigmoid(out_mask, name='prediction')
def _module_fn():
"""
Function building the module
"""
feature_layer = tf.placeholder(
tf.float32,
shape=[None, None, None, None, nchannels],
name='input')
obs_layer = tf.placeholder(tf.float32,
shape=[None, None, None, None, n_y],
name='observations')
# Builds the neural network
if pad == 0:
d00 = slim.conv3d(feature_layer,
fsize,
5,
activation_fn=tf.nn.leaky_relu,
padding='same')
elif pad == 2:
d00 = slim.conv3d(feature_layer,
fsize,
5,
activation_fn=tf.nn.leaky_relu,
padding='valid')
if pad == 4:
d00 = slim.conv3d(feature_layer,
fsize,
5,
activation_fn=tf.nn.leaky_relu,
padding='valid')
d00 = slim.conv3d(d00,
fsize * 2,
5,
activation_fn=tf.nn.leaky_relu,
padding='valid')
## #downsample
## dd = [[d00]]
## cfsize = fsize
## for i in range(nsub):
## d0 = dd[-1][-1]
## d1 = wide_resnet(d0, cfsize, activation_fn=tf.nn.leaky_relu)
## d2 = wide_resnet(d1, cfsize, activation_fn=tf.nn.leaky_relu)
## dsub = slim.max_pool3d(d2, kernel_size=3, stride=2, padding='SAME')
## dd.append([d1, d2, dsub])
## cfsize *= 2
##
## #lower layer
## d0 = dd[-1][-1]
## d1 = wide_resnet(d0, cfsize, activation_fn=tf.nn.leaky_relu)
## d2 = wide_resnet(d1, cfsize, activation_fn=tf.nn.leaky_relu)
##
## up = [[d1, d2]]
## #upsample
## for i in range(nsub):
## cfsize = cfsize // 2
## usub = up[-1][-1]
## dup = dd.pop()
## u0 = dynamic_deconv3d('up%d'%i, usub, shape=[3,3,3,cfsize], activation=tf.nn.leaky_relu)
## #u0 = slim.conv3d_transpose(usub, fsize, kernel_size=3, stride=2)
## uc = tf.concat([u0, dup[1]], axis=-1)
## u1 = wide_resnet(uc, cfsize, activation_fn=tf.nn.leaky_relu)
## u2 = wide_resnet(u1, cfsize, activation_fn=tf.nn.leaky_relu)
## up.append([u0, u1, u1c, u2])
##
## u0 = up[-1][-1]
## net = slim.conv3d(u0, 1, 3, activation_fn=tf.nn.tanh)
##
#downsample #restructure code while doubling filter size
cfsize = fsize
d1 = wide_resnet(d00, cfsize, activation_fn=tf.nn.leaky_relu)
d2 = wide_resnet(d1, cfsize, activation_fn=tf.nn.leaky_relu)
dd = [d2]
for i in range(nsub):
cfsize *= 2
print(i, cfsize)
dsub = slim.max_pool3d(dd[-1],
kernel_size=3,
stride=2,
padding='SAME')
d1 = wide_resnet(dsub, cfsize, activation_fn=tf.nn.leaky_relu)
d2 = wide_resnet(d1, cfsize, activation_fn=tf.nn.leaky_relu)
dd.append(d2)
print(len(dd))
#upsample
usub = dd.pop()
for i in range(nsub):
u0 = dynamic_deconv3d('up%d' % i,
usub,
shape=[3, 3, 3, cfsize],
activation=tf.identity)
cfsize = cfsize // 2
print(i, cfsize)
u0 = slim.conv3d(u0,
cfsize,
1,
activation_fn=tf.identity,
padding='same')
#u0 = slim.conv3d_transpose(usub, fsize, kernel_size=3, stride=2)
uc = tf.concat([u0, dd.pop()], axis=-1)
u1 = wide_resnet(uc, cfsize, activation_fn=tf.nn.leaky_relu)
u2 = wide_resnet(u1, cfsize, activation_fn=tf.nn.leaky_relu)
usub = u2
print(len(dd))
net = slim.conv3d(usub, 1, 3, activation_fn=tf.nn.tanh)
# Define the probabilistic layer
net = slim.conv3d(net, n_mixture * 3 * n_y, 1, activation_fn=None)
cube_size = tf.shape(obs_layer)[1]
net = tf.reshape(
net, [-1, cube_size, cube_size, cube_size, n_y, n_mixture * 3])
# net = tf.reshape(net, [None, None, None, None, n_y, n_mixture*3])
loc, unconstrained_scale, logits = tf.split(net,
num_or_size_splits=3,
axis=-1)
scale = tf.nn.softplus(unconstrained_scale) + 1e-3
# Form mixture of discretized logistic distributions. Note we shift the
# logistic distribution by -0.5. This lets the quantization capture "rounding"
# intervals, `(x-0.5, x+0.5]`, and not "ceiling" intervals, `(x-1, x]`.
if distribution == 'logistic':
discretized_logistic_dist = tfd.QuantizedDistribution(
distribution=tfd.TransformedDistribution(
distribution=tfd.Logistic(loc=loc, scale=scale),
bijector=tfb.AffineScalar(shift=-0.5)),
low=0.,
high=2.**3 - 1)
mixture_dist = tfd.MixtureSameFamily(
mixture_distribution=tfd.Categorical(logits=logits),
components_distribution=discretized_logistic_dist)
elif distribution == 'normal':
mixture_dist = tfd.MixtureSameFamily(
mixture_distribution=tfd.Categorical(logits=logits),
components_distribution=tfd.Normal(loc=loc, scale=scale))
# Define a function for sampling, and a function for estimating the log likelihood
#sample = tf.squeeze(mixture_dist.sample())
sample = mixture_dist.sample()
loglik = mixture_dist.log_prob(obs_layer)
hub.add_signature(inputs={
'features': feature_layer,
'labels': obs_layer
},
outputs={
'sample': sample,
'loglikelihood': loglik,
'loc': loc,
'scale': scale,
'logits': logits
})
def _module_fn():
"""
Function building the module
"""
feature_layer = tf.placeholder(
tf.float32,
shape=[None, None, None, None, nchannels],
name='input')
obs_layer = tf.placeholder(tf.float32,
shape=[None, None, None, None, n_y],
name='observations')
# Builds the neural network
net = slim.conv3d(feature_layer,
16,
5,
activation_fn=tf.nn.leaky_relu,
padding='same')
#net = wide_resnet(feature_layer, 8, activation_fn=tf.nn.leaky_relu, is_training=is_training)
net = wide_resnet(net,
16,
activation_fn=tf.nn.leaky_relu,
keep_prob=dropout,
is_training=is_training)
net = wide_resnet(net,
32,
activation_fn=tf.nn.leaky_relu,
keep_prob=dropout,
is_training=is_training)
net = wide_resnet(net,
32,
activation_fn=tf.nn.leaky_relu,
keep_prob=dropout,
is_training=is_training)
net = slim.conv3d(net, 32, 3, activation_fn=tf.nn.leaky_relu)
# Define the probabilistic layer
net = slim.conv3d(net,
3 * n_mixture * nchannels,
1,
activation_fn=None)
cube_size = tf.shape(obs_layer)[1]
net = tf.reshape(
net,
[-1, cube_size, cube_size, cube_size, nchannels, n_mixture * 3])
logits, loc, unconstrained_scale = tf.split(net,
num_or_size_splits=3,
axis=-1)
print('\nloc :\n', loc)
scale = tf.nn.softplus(unconstrained_scale[...]) + 1e-3
distribution = tfd.MixtureSameFamily(
mixture_distribution=tfd.Categorical(logits=logits[...]),
#components_distribution=tfd.MultivariateNormalDiag(loc=loc[...,0], scale_diag=scale))
components_distribution=tfd.Normal(loc=loc[...], scale=scale))
print('\ngmm\n', distribution)
# Define a function for sampling, and a function for estimating the log likelihood
if log:
print('Logged it')
sample = tf.exp(distribution.sample()) - logoffset
print('\ninf dist sample :\n', distribution.sample())
logfeature = tf.log(tf.add(logoffset, obs_layer), 'logfeature')
print('\nlogfeature :\n', logfeature)
prob = distribution.prob(logfeature[...])
loglik = distribution.log_prob(logfeature[...])
else:
print('UnLogged it')
sample = distribution.sample()
print('\ninf dist sample :\n', distribution.sample())
loglik = distribution.log_prob(obs_layer[...])
hub.add_signature(inputs={
'features': feature_layer,
'labels': obs_layer
},
outputs={
'sample': sample,
'loglikelihood': loglik,
'sigma': scale,
'mean': loc,
'logits': logits
})
def _module_fn():
"""
Function building the module
"""
feature_layer = tf.placeholder(
tf.float32,
shape=[None, None, None, None, nchannels],
name='input')
obs_layer = tf.placeholder(tf.float32,
shape=[None, None, None, None, n_y],
name='observations')
mask_layer = tf.clip_by_value(obs_layer, 0, 0.001) * 1000
#
# Builds the neural network
if pad == 0:
net = slim.conv3d(feature_layer,
16,
5,
activation_fn=tf.nn.leaky_relu,
padding='same')
elif pad == 2:
net = slim.conv3d(feature_layer,
16,
5,
activation_fn=tf.nn.leaky_relu,
padding='valid')
#net = wide_resnet(feature_layer, 8, activation_fn=tf.nn.leaky_relu, is_training=is_training)
net = wide_resnet(net,
16,
activation_fn=tf.nn.leaky_relu,
keep_prob=dropout,
is_training=is_training)
net = wide_resnet(net,
32,
activation_fn=tf.nn.leaky_relu,
keep_prob=dropout,
is_training=is_training)
net = wide_resnet(net,
32,
activation_fn=tf.nn.leaky_relu,
keep_prob=dropout,
is_training=is_training)
if distribution == 'logistic':
net = slim.conv3d(net, 32, 3, activation_fn=tf.nn.tanh)
else:
net = slim.conv3d(net, 32, 3, activation_fn=tf.nn.leaky_relu)
#Predicted mask
masknet = slim.conv3d(net, 8, 1, activation_fn=tf.nn.leaky_relu)
out_mask = slim.conv3d(masknet, 1, 1, activation_fn=None)
pred_mask = tf.nn.sigmoid(out_mask)
# Define the probabilistic layer
likenet = slim.conv3d(net, 64, 1, activation_fn=tf.nn.leaky_relu)
net = slim.conv3d(likenet, n_mixture * 3 * n_y, 1, activation_fn=None)
cube_size = tf.shape(obs_layer)[1]
net = tf.reshape(
net, [-1, cube_size, cube_size, cube_size, n_y, n_mixture * 3])
# net = tf.reshape(net, [None, None, None, None, n_y, n_mixture*3])
loc, unconstrained_scale, logits = tf.split(net,
num_or_size_splits=3,
axis=-1)
scale = tf.nn.softplus(unconstrained_scale) + 1e-3
# Form mixture of discretized logistic distributions. Note we shift the
# logistic distribution by -0.5. This lets the quantization capture "rounding"
# intervals, `(x-0.5, x+0.5]`, and not "ceiling" intervals, `(x-1, x]`.
if distribution == 'logistic':
discretized_logistic_dist = tfd.QuantizedDistribution(
distribution=tfd.TransformedDistribution(
distribution=tfd.Logistic(loc=loc, scale=scale),
bijector=tfb.AffineScalar(shift=-0.5)),
low=0.,
high=2.**3 - 1)
mixture_dist = tfd.MixtureSameFamily(
mixture_distribution=tfd.Categorical(logits=logits),
components_distribution=discretized_logistic_dist)
elif distribution == 'normal':
mixture_dist = tfd.MixtureSameFamily(
mixture_distribution=tfd.Categorical(logits=logits),
components_distribution=tfd.Normal(loc=loc, scale=scale))
# Define a function for sampling, and a function for estimating the log likelihood
#sample = tf.squeeze(mixture_dist.sample())
rawsample = mixture_dist.sample()
sample = rawsample * pred_mask
rawloglik = mixture_dist.log_prob(obs_layer)
print(rawloglik)
print(out_mask)
print(mask_layer)
#loss1 = - rawloglik* out_mask #This can be constant mask as well if we use mask_layer instead
if masktype == 'constant': loss1 = -rawloglik * mask_layer
elif masktype == 'vary': loss1 = -rawloglik * pred_mask
loss2 = tf.nn.sigmoid_cross_entropy_with_logits(logits=out_mask,
labels=mask_layer)
loglik = -(loss1 + loss2)
hub.add_signature(inputs={
'features': feature_layer,
'labels': obs_layer
},
outputs={
'sample': sample,
'loglikelihood': loglik,
'loc': loc,
'scale': scale,
'logits': logits,
'rawsample': rawsample,
'pred_mask': pred_mask,
'out_mask': out_mask,
'rawloglik': rawloglik,
'loss1': loss1,
'loss2': loss2
})
