def _create_weights(self):
all_weights = collections.OrderedDict()
with tf.variable_scope("L2_VICI_ENC"):
# Encoder
all_weights['L2_VICI_encoder'] = collections.OrderedDict()
hidden_number_encoder = self.n_weights
all_weights['L2_VICI_encoder']['W3_to_hidden'] = tf.Variable(vae_utils.xavier_init(self.n_input, hidden_number_encoder), dtype=tf.float32)
tf.summary.histogram("W3_to_hidden", all_weights['L2_VICI_encoder']['W3_to_hidden'])
# all_weights['encoder']['W3_hth'] = tf.Variable(vae_utils.xavier_init(hidden_number_encoder, hidden_number_encoder), dtype=tf.float32)
# tf.summary.histogram("W3_hth", all_weights['encoder']['W3_hth'])
#
# all_weights['IVA_encoder']['W3b_hth'] = tf.Variable(vae_utils.xavier_init(hidden_number_encoder, hidden_number_encoder), dtype=tf.float32)
# tf.summary.histogram("W3b_hth", all_weights['IVA_encoder']['W3b_hth'])
## #
# all_weights['IVA_encoder']['W3c_hth'] = tf.Variable(vae_utils.xavier_init(hidden_number_encoder, hidden_number_encoder), dtype=tf.float32)
# tf.summary.histogram("W3c_hth", all_weights['IVA_encoder']['W3c_hth'])
#
# all_weights['encoder']['W3d_hth'] = tf.Variable(vae_utils.xavier_init(hidden_number_encoder, hidden_number_encoder), dtype=tf.float32)
# tf.summary.histogram("W3d_hth", all_weights['encoder']['W3d_hth'])
all_weights['L2_VICI_encoder']['W4_to_mu'] = tf.Variable(vae_utils.xavier_init(hidden_number_encoder, self.n_hidden),dtype=tf.float32)
tf.summary.histogram("W4_to_mu", all_weights['L2_VICI_encoder']['W4_to_mu'])
all_weights['L2_VICI_encoder']['W5_to_log_sigma'] = tf.Variable(vae_utils.xavier_init(hidden_number_encoder, self.n_hidden), dtype=tf.float32)
tf.summary.histogram("W5_to_log_sigma", all_weights['L2_VICI_encoder']['W5_to_log_sigma'])
all_weights['L2_VICI_encoder']['b3_to_hidden'] = tf.Variable(tf.zeros([hidden_number_encoder], dtype=tf.float32) * self.bias_start)
all_weights['L2_VICI_encoder']['b3_hth'] = tf.Variable(tf.zeros([hidden_number_encoder], dtype=tf.float32) * self.bias_start)
all_weights['L2_VICI_encoder']['b3b_hth'] = tf.Variable(tf.zeros([hidden_number_encoder], dtype=tf.float32) * self.bias_start)
all_weights['L2_VICI_encoder']['b3c_hth'] = tf.Variable(tf.zeros([hidden_number_encoder], dtype=tf.float32) * self.bias_start)
all_weights['L2_VICI_encoder']['b3d_hth'] = tf.Variable(tf.zeros([hidden_number_encoder], dtype=tf.float32) * self.bias_start)
all_weights['L2_VICI_encoder']['b4_to_mu'] = tf.Variable(tf.zeros([self.n_hidden], dtype=tf.float32) * self.bias_start, dtype=tf.float32)
all_weights['L2_VICI_encoder']['b5_to_log_sigma'] = tf.Variable(tf.zeros([self.n_hidden], dtype=tf.float32) * self.bias_start, dtype=tf.float32)
all_weights['prior_param'] = collections.OrderedDict()
return all_weights
def _create_weights(self):
all_weights = collections.OrderedDict()
# Decoder
with tf.variable_scope("L2_VICI_DEC"):
all_weights['L2_VICI_decoder'] = collections.OrderedDict()
if self.middle == "gaussian":
hidden_number_decoder = self.n_weights
all_weights['L2_VICI_decoder']['W3_to_hiddenG'] = tf.Variable(vae_utils.xavier_init(self.n_hidden, hidden_number_decoder), dtype=tf.float32)
all_weights['L2_VICI_decoder']['b3_to_hiddenG'] = tf.Variable(tf.zeros([hidden_number_decoder], dtype=tf.float32) * self.bias_start)
# all_weights['decoder']['W3_to_hiddenGS'] = tf.Variable(vae_utils.xavier_init(self.n_hidden, hidden_number_decoder), dtype=tf.float32)
# all_weights['decoder']['b3_to_hiddenGS'] = tf.Variable(tf.zeros([hidden_number_decoder], dtype=tf.float32) * self.bias_start)
# all_weights['decoder']['W3b_to_hiddenG'] = tf.Variable(vae_utils.xavier_init(hidden_number_decoder, hidden_number_decoder), dtype=tf.float32)
# all_weights['decoder']['b3b_to_hiddenG'] = tf.Variable(tf.zeros([hidden_number_decoder], dtype=tf.float32) * self.bias_start)
#
# all_weights['decoder']['W3c_to_hiddenG'] = tf.Variable(vae_utils.xavier_init(hidden_number_decoder, hidden_number_decoder), dtype=tf.float32)
# all_weights['decoder']['b3c_to_hiddenG'] = tf.Variable(tf.zeros([hidden_number_decoder], dtype=tf.float32) * self.bias_start)
#####
# all_weights['decoder']['W3d_to_hiddenG'] = tf.Variable(vae_utils.xavier_init(hidden_number_decoder, hidden_number_decoder), dtype=tf.float32)
# all_weights['decoder']['b3d_to_hiddenG'] = tf.Variable(tf.zeros([hidden_number_decoder], dtype=tf.float32) * self.bias_start)
#
# all_weights['decoder']['W3e_to_hiddenG'] = tf.Variable(vae_utils.xavier_init(hidden_number_decoder, hidden_number_decoder), dtype=tf.float32)
# all_weights['decoder']['b3e_to_hiddenG'] = tf.Variable(tf.zeros([hidden_number_decoder], dtype=tf.float32) * self.bias_start)
all_weights['L2_VICI_decoder']['W4_to_muG'] = tf.Variable(vae_utils.xavier_init(hidden_number_decoder, self.n_input), dtype=tf.float32)
all_weights['L2_VICI_decoder']['b4_to_muG'] = tf.Variable(tf.zeros([self.n_input]) * self.bias_start, dtype=tf.float32)
all_weights['L2_VICI_decoder']['W5_to_log_sigmaG'] = tf.Variable(vae_utils.xavier_init(hidden_number_decoder, self.n_input), dtype=tf.float32)
all_weights['L2_VICI_decoder']['b5_to_log_sigmaG'] = tf.Variable(tf.zeros([self.n_input]) * self.bias_start, dtype=tf.float32)
elif self.middle == "bernoulli":
hidden_number_decoder = 200
all_weights['decoder']['W1_to_hidden'] = tf.Variable(vae_utils.xavier_init(self.n_hidden, hidden_number_decoder))
tf.summary.histogram("W1_to_hidden", all_weights['decoder']['W1_to_hidden'])
all_weights['decoder']['W1b_htoh'] = tf.Variable(vae_utils.xavier_init(hidden_number_decoder, hidden_number_decoder))
tf.summary.histogram("W1b_htoh", all_weights['decoder']['W1_to_hidden'])
all_weights['decoder']['W1c_htoh'] = tf.Variable(vae_utils.xavier_init(hidden_number_decoder, hidden_number_decoder))
tf.summary.histogram("W1c_htoh", all_weights['decoder']['W1_to_hidden'])
all_weights['decoder']['W2_to_y_pre'] = tf.Variable(vae_utils.xavier_init(hidden_number_decoder, self.n_input))
tf.summary.histogram("W2_to_y_pre", all_weights['decoder']['W1_to_hidden'])
all_weights['decoder']['b1_to_hidden'] = tf.Variable(tf.ones([hidden_number_decoder], dtype=tf.float32) * self.bias_start)
tf.summary.histogram("b1_to_hidden", all_weights['decoder']['b1_to_hidden'])
all_weights['decoder']['b1b_htoh'] = tf.Variable(tf.ones([hidden_number_decoder], dtype=tf.float32) * self.bias_start)
tf.summary.histogram("b1b_htoh", all_weights['decoder']['b1b_htoh'])
all_weights['decoder']['b1c_htoh'] = tf.Variable(tf.ones([hidden_number_decoder], dtype=tf.float32) * self.bias_start)
tf.summary.histogram("b1c_htoh", all_weights['decoder']['b1c_htoh'])
all_weights['decoder']['b2_to_y_pre'] = tf.Variable(tf.ones([self.n_input], dtype=tf.float32) * self.bias_start)
tf.summary.histogram("b2_to_y_pre", all_weights['decoder']['b2_to_y_pre'])
else:
raise RuntimeError
all_weights['prior_param'] = collections.OrderedDict()
return all_weights
def _create_weights(self):
all_weights = collections.OrderedDict()
# Decoder
with tf.variable_scope("VICI_DEC"):
all_weights['VICI_decoder'] = collections.OrderedDict()
if self.n_conv is not None:
dummy = 1
for i in range(self.n_conv):
weight_name = 'w_conv_' + str(i)
bias_name = 'b_conv_' + str(i)
# orthogonal init
if self.weight_init == 'Orthogonal':
shape_init = (self.filter_size[i],dummy*self.n_filters[i])
initializer = tf.keras.initializers.Orthogonal()
all_weights['VICI_decoder'][weight_name] = tf.Variable(tf.reshape(initializer(shape=shape_init),[self.filter_size[i], 1, dummy, self.n_filters[i]]), dtype=tf.float32)
# Variance scaling
if self.weight_init == 'VarianceScaling':
shape_init = (self.filter_size[i],dummy*self.n_filters[i])
initializer = tf.keras.initializers.VarianceScaling()
all_weights['VICI_decoder'][weight_name] = tf.Variable(tf.reshape(initializer(shape=shape_init),[self.filter_size[i], 1, dummy, self.n_filters[i]]), dtype=tf.float32)
# xavier initilization
if self.weight_init == 'xavier':
all_weights['VICI_decoder'][weight_name] = tf.Variable(tf.reshape(vae_utils.xavier_init(self.filter_size[i], dummy*self.n_filters[i]),[self.filter_size[i], 1, dummy, self.n_filters[i]]), dtype=tf.float32)
all_weights['VICI_decoder'][bias_name] = tf.Variable(tf.zeros([self.n_filters[i]], dtype=tf.float32))
tf.summary.histogram(weight_name, all_weights['VICI_decoder'][weight_name])
tf.summary.histogram(bias_name, all_weights['VICI_decoder'][bias_name])
dummy = self.n_filters[i]
total_pool_stride_sum = 0
for j in range(len(self.maxpool)):
if self.maxpool[j] != 1 and self.pool_strides[j] != 1:
total_pool_stride_sum += 1
else:
if self.maxpool[j] != 1:
total_pool_stride_sum += 1
if self.pool_strides[j] != 1:
total_pool_stride_sum += 1
if self.conv_strides[j] != 1:
total_pool_stride_sum += 1
if self.by_channel == True:
fc_input_size = self.n_input1 + int(self.n_input2*self.n_filters[i]/(2**total_pool_stride_sum))
else:
fc_input_size = self.n_input1 + int(self.n_input2*self.n_filters[i]/(2**total_pool_stride_sum)*2)
else:
fc_input_size = self.n_input1 + self.n_input2
for i in range(self.n_hlayers):
weight_name = 'w_hidden_' + str(i)
bias_name = 'b_hidden' + str(i)
all_weights['VICI_decoder'][weight_name] = tf.Variable(vae_utils.xavier_init(fc_input_size, self.n_weights[i]), dtype=tf.float32)
all_weights['VICI_decoder'][bias_name] = tf.Variable(tf.zeros([self.n_weights[i]], dtype=tf.float32))
tf.summary.histogram(weight_name, all_weights['VICI_decoder'][weight_name])
tf.summary.histogram(bias_name, all_weights['VICI_decoder'][bias_name])
fc_input_size = self.n_weights[i]
all_weights['VICI_decoder']['w_loc'] = tf.Variable(vae_utils.xavier_init(self.n_weights[-1], self.n_output),dtype=tf.float32)
all_weights['VICI_decoder']['b_loc'] = tf.Variable(tf.zeros([self.n_output], dtype=tf.float32), dtype=tf.float32)
tf.summary.histogram('w_loc', all_weights['VICI_decoder']['w_loc'])
tf.summary.histogram('b_loc', all_weights['VICI_decoder']['b_loc'])
all_weights['VICI_decoder']['w_scale'] = tf.Variable(vae_utils.xavier_init(self.n_weights[-1], self.n_output),dtype=tf.float32)
all_weights['VICI_decoder']['b_scale'] = tf.Variable(tf.zeros([self.n_output], dtype=tf.float32), dtype=tf.float32)
tf.summary.histogram('w_scale', all_weights['VICI_decoder']['w_scale'])
tf.summary.histogram('b_scale', all_weights['VICI_decoder']['b_scale'])
all_weights['prior_param'] = collections.OrderedDict()
return all_weights
def _create_weights(self):
all_weights = collections.OrderedDict()
with tf.variable_scope("VICI_ENC"):
all_weights['VICI_encoder'] = collections.OrderedDict()
if self.n_conv is not None:
dummy = self.n_channels
for i in range(self.n_conv):
weight_name = 'w_conv_' + str(i)
bias_name = 'b_conv_' + str(i)
all_weights['VICI_encoder'][
weight_name + '1'] = tf.Variable(tf.reshape(
vae_utils.xavier_init(self.filter_size[i],
dummy * self.n_filters[i]),
[self.filter_size[i], 1, dummy, self.n_filters[i]
]),
dtype=tf.float32)
all_weights['VICI_encoder'][bias_name + '1'] = tf.Variable(
tf.zeros([self.n_filters[i]], dtype=tf.float32))
#all_weights['VICI_encoder'][weight_name + '2'] = tf.Variable(tf.reshape(vae_utils.xavier_init(self.filter_size*2, dummy*self.n_filters),[self.filter_size*2, dummy, self.n_filters]), dtype=tf.float32)
#all_weights['VICI_encoder'][bias_name + '2'] = tf.Variable(tf.zeros([self.n_filters], dtype=tf.float32))
#all_weights['VICI_encoder'][weight_name + '3'] = tf.Variable(tf.reshape(vae_utils.xavier_init(self.filter_size*4, dummy*self.n_filters),[self.filter_size*4, dummy, self.n_filters]), dtype=tf.float32)
#all_weights['VICI_encoder'][bias_name + '3'] = tf.Variable(tf.zeros([self.n_filters], dtype=tf.float32))
tf.summary.histogram(
weight_name + '1',
all_weights['VICI_encoder'][weight_name + '1'])
tf.summary.histogram(
bias_name + '1',
all_weights['VICI_encoder'][bias_name + '1'])
#tf.summary.histogram(weight_name + '2', all_weights['VICI_encoder'][weight_name + '2'])
#tf.summary.histogram(bias_name + '2', all_weights['VICI_encoder'][bias_name + '2'])
#tf.summary.histogram(weight_name + '3', all_weights['VICI_encoder'][weight_name + '3'])
#tf.summary.histogram(bias_name + '3', all_weights['VICI_encoder'][bias_name + '3'])
dummy = self.n_filters[i]
fc_input_size = int(self.n_input * self.n_filters[-1] /
(np.prod(self.maxpool)))
else:
fc_input_size = self.n_input * self.n_channels
for i in range(self.n_hlayers):
weight_name = 'w_hidden_' + str(i)
bias_name = 'b_hidden' + str(i)
all_weights['VICI_encoder'][weight_name] = tf.Variable(
vae_utils.xavier_init(fc_input_size, self.n_weights[i]),
dtype=tf.float32)
all_weights['VICI_encoder'][bias_name] = tf.Variable(
tf.zeros([self.n_weights[i]], dtype=tf.float32))
tf.summary.histogram(weight_name,
all_weights['VICI_encoder'][weight_name])
tf.summary.histogram(bias_name,
all_weights['VICI_encoder'][bias_name])
fc_input_size = self.n_weights[i]
all_weights['VICI_encoder']['w_loc'] = tf.Variable(
vae_utils.xavier_init(self.n_weights[-1],
self.n_output * self.n_modes),
dtype=tf.float32)
all_weights['VICI_encoder']['b_loc'] = tf.Variable(
tf.zeros([self.n_output * self.n_modes], dtype=tf.float32),
dtype=tf.float32)
tf.summary.histogram('w_loc', all_weights['VICI_encoder']['w_loc'])
tf.summary.histogram('b_loc', all_weights['VICI_encoder']['b_loc'])
all_weights['VICI_encoder']['w_scale_diag'] = tf.Variable(
vae_utils.xavier_init(self.n_weights[-1],
self.n_output * self.n_modes),
dtype=tf.float32)
all_weights['VICI_encoder']['b_scale_diag'] = tf.Variable(
tf.zeros([self.n_output * self.n_modes], dtype=tf.float32),
dtype=tf.float32)
tf.summary.histogram('w_scale',
all_weights['VICI_encoder']['w_scale_diag'])
tf.summary.histogram('b_scale',
all_weights['VICI_encoder']['b_scale_diag'])
#all_weights['VICI_encoder']['w_scale_tri'] = tf.Variable(vae_utils.xavier_init(self.n_weights[-1], int((self.n_output*(self.n_output+1)*self.n_modes)/2)),dtype=tf.float32)
#all_weights['VICI_encoder']['b_scale_tri'] = tf.Variable(tf.zeros([int((self.n_output*(self.n_output+1)*self.n_modes)/2)], dtype=tf.float32), dtype=tf.float32)
#tf.summary.histogram('w_scale_tri', all_weights['VICI_encoder']['w_scale_tri'])
#tf.summary.histogram('b_scale_tri', all_weights['VICI_encoder']['b_scale_tri'])
all_weights['VICI_encoder']['w_weight'] = tf.Variable(
vae_utils.xavier_init(self.n_weights[-1], self.n_modes),
dtype=tf.float32)
all_weights['VICI_encoder']['b_weight'] = tf.Variable(
tf.zeros([self.n_modes], dtype=tf.float32), dtype=tf.float32)
tf.summary.histogram('w_weight',
all_weights['VICI_encoder']['w_weight'])
tf.summary.histogram('b_weight',
all_weights['VICI_encoder']['b_weight'])
all_weights['prior_param'] = collections.OrderedDict()
return all_weights
def _create_weights(self):
all_weights = collections.OrderedDict()
# Decoder
with tf.variable_scope("VICI_DEC"):
all_weights['VICI_decoder'] = collections.OrderedDict()
if self.n_conv is not None:
dummy = self.n_channels
for i in range(self.n_conv):
weight_name = 'w_conv_' + str(i)
bias_name = 'b_conv_' + str(i)
all_weights['VICI_decoder'][
weight_name + '1'] = tf.Variable(tf.reshape(
vae_utils.xavier_init(self.filter_size[i],
dummy * self.n_filters[i]),
[self.filter_size[i], 1, dummy, self.n_filters[i]
]),
dtype=tf.float32)
all_weights['VICI_decoder'][bias_name + '1'] = tf.Variable(
tf.zeros([self.n_filters[i]], dtype=tf.float32))
tf.summary.histogram(
weight_name + '1',
all_weights['VICI_decoder'][weight_name + '1'])
tf.summary.histogram(
bias_name + '1',
all_weights['VICI_decoder'][bias_name + '1'])
dummy = self.n_filters[i]
fc_input_size = self.n_input1 + int(
self.n_input2 * self.n_filters[-1] /
(np.prod(self.maxpool)))
else:
fc_input_size = self.n_input1 + self.n_input2 * self.n_channels
for i in range(self.n_hlayers):
weight_name = 'w_hidden_' + str(i)
bias_name = 'b_hidden' + str(i)
all_weights['VICI_decoder'][weight_name] = tf.Variable(
vae_utils.xavier_init(fc_input_size, self.n_weights[i]),
dtype=tf.float32)
all_weights['VICI_decoder'][bias_name] = tf.Variable(
tf.zeros([self.n_weights[i]], dtype=tf.float32))
tf.summary.histogram(weight_name,
all_weights['VICI_decoder'][weight_name])
tf.summary.histogram(bias_name,
all_weights['VICI_decoder'][bias_name])
fc_input_size = self.n_weights[i]
all_weights['VICI_decoder']['w_loc'] = tf.Variable(
vae_utils.xavier_init(self.n_weights[-1], self.n_output + 1),
dtype=tf.float32) # +1 for extra sky param
all_weights['VICI_decoder']['b_loc'] = tf.Variable(
tf.zeros([self.n_output + 1], dtype=tf.float32),
dtype=tf.float32) # +1 for extra sky param
tf.summary.histogram('w_loc', all_weights['VICI_decoder']['w_loc'])
tf.summary.histogram('b_loc', all_weights['VICI_decoder']['b_loc'])
all_weights['VICI_decoder']['w_scale'] = tf.Variable(
vae_utils.xavier_init(self.n_weights[-1], self.n_output - 1),
dtype=tf.float32
) # # -1 for common concentration par in VMF dist
all_weights['VICI_decoder']['b_scale'] = tf.Variable(
tf.zeros([self.n_output - 1], dtype=tf.float32),
dtype=tf.float32
) # -1 for common concentration par in VMF dist
tf.summary.histogram('w_scale',
all_weights['VICI_decoder']['w_scale'])
tf.summary.histogram('b_scale',
all_weights['VICI_decoder']['b_scale'])
all_weights['prior_param'] = collections.OrderedDict()
return all_weights
