def FlowVisNet(t1,
t2,
F,
n_coupling_layers=5,
slope_const=1e2,
sigma=None,
binary_slope=10,
obs_prob=None,
generator='gaussian'):
filt = 64
kern = 3
acti = None
tlib = {}
tind = 0
telescopes = np.concatenate([t1, t2])
for k in range(len(telescopes)):
if telescopes[k] not in tlib:
tlib[telescopes[k]] = tind
tind += 1
if 'SPT' not in telescopes:
tlib['SPT'] = tind
tind += 1
elif 'GLT' not in telescopes:
tlib['GLT'] = tind
tind += 1
n_sites = tind
Fm1 = np.zeros((len(t1), n_sites))
Fm2 = np.zeros((len(t1), n_sites))
for k in range(len(t1)):
Fm1[k, tlib[t1[k]]] = 1
Fm2[k, tlib[t2[k]]] = 1
Fm1 = tf.constant(Fm1, dtype=tf.float32)
Fm2 = tf.constant(Fm2, dtype=tf.float32)
input_shape = (32, 32, 1)
input_im = keras.layers.Input(shape=input_shape, name='input')
vis = keras.layers.Lambda(hp.Lambda_dft(F))(input_im)
if sigma is not None:
vis = keras.layers.GaussianNoise(sigma)(vis)
if generator == 'realnvp':
encoder, resnet_list, permute_list = realnvp_encoder(
output_dim=n_sites, n_coupling=n_coupling_layers)
decoder = realnvp_decoder(resnet_list,
permute_list,
output_dim=n_sites,
n_coupling=n_coupling_layers)
z = keras.layers.Lambda(Lambda_Gaussian(output_dim=n_sites),
name='random_seeds')(input_im)
flow_sample, logdet = decoder(z)
elif generator == 'gaussian':
flow_sample, logdet = Gaussian_sampling(
output_dim=n_sites, name='gaussian_samples')(input_im)
site_mask = STE_layer(name='sampling')(flow_sample)
logdet2 = keras.layers.Lambda(Lambda_logdet_sigmoid(1.0))(flow_sample)
logprob = keras.layers.Add(name='logprob')([logdet, logdet2])
if obs_prob is None:
vis_mask = keras.layers.Lambda(hp.Lambda_vis_mask2(Fm1, Fm2),
name='vis_mask')(site_mask)
else:
prob_mask = np.zeros(n_sites)
for k in obs_prob.keys():
prob_mask[tlib[k]] = obs_prob[k]
site_mask_weather = Site_mask_prob(prob_mask)(site_mask)
vis_mask = keras.layers.Lambda(hp.Lambda_vis_mask2(Fm1, Fm2),
name='vis_mask')(site_mask_weather)
# site_mask = keras.layers.Lambda(hp.Lambda_binary_convert(binary_slope), name='sampling')(ising_sample)
# vis_mask = keras.layers.Lambda(hp.Lambda_vis_mask2(Fm1, Fm2), name='vis_mask')(site_mask)
vis_selected = keras.layers.Lambda(hp.Lambda_select0)([vis_mask, vis])
vis_reshape = keras.layers.Reshape((2 * F.shape[0], ))(vis_selected)
dirty_im = keras.layers.Dense(32 * 32 * 1,
activation=acti,
use_bias=True,
kernel_initializer=RandomUniform(
minval=-5e-4, maxval=5e-4, seed=None),
name='dense_dirtyimage')(vis_reshape)
dirty_im_reshape = keras.layers.Reshape((32, 32, 1))(dirty_im)
outputs = _unet_from_tensor(dirty_im_reshape, filt, kern, acti)
outputs = keras.layers.ReLU(name='recon')(outputs)
model = keras.models.Model(inputs=input_im,
outputs=[outputs, site_mask, logprob])
return model
def FlowCpAmpNet(t1,
t2,
tc1,
tc2,
tc3,
F,
F_cphase,
cphase_proj,
n_coupling_layers=5,
slope_const=1e1,
sigma=None,
binary_slope=10,
obs_prob=None,
generator='gaussian'):
filt = 64
kern = 3
acti = None
n_vis = F.shape[0]
n_cphase = F_cphase.shape[0]
F = tf.constant(F, dtype=tf.complex64)
F_cphase = tf.constant(F_cphase, dtype=tf.complex64)
cphase_proj = tf.constant(cphase_proj, dtype=tf.float32)
tlib = {}
tind = 0
telescopes = np.concatenate([t1, t2])
for k in range(len(telescopes)):
if telescopes[k] not in tlib:
tlib[telescopes[k]] = tind
tind += 1
if 'SPT' not in telescopes:
tlib['SPT'] = tind
tind += 1
elif 'GLT' not in telescopes:
tlib['GLT'] = tind
tind += 1
n_sites = tind
Fm1 = np.zeros((len(t1), n_sites))
Fm2 = np.zeros((len(t1), n_sites))
for k in range(len(t1)):
Fm1[k, tlib[t1[k]]] = 1
Fm2[k, tlib[t2[k]]] = 1
Fm1 = tf.constant(Fm1, dtype=tf.float32)
Fm2 = tf.constant(Fm2, dtype=tf.float32)
Fcm1 = np.zeros((len(tc1), n_sites))
Fcm2 = np.zeros((len(tc1), n_sites))
Fcm3 = np.zeros((len(tc1), n_sites))
for k in range(len(tc1)):
Fcm1[k, tlib[tc1[k]]] = 1
Fcm2[k, tlib[tc2[k]]] = 1
Fcm3[k, tlib[tc3[k]]] = 1
Fcm1 = tf.constant(Fcm1, dtype=tf.float32)
Fcm2 = tf.constant(Fcm2, dtype=tf.float32)
Fcm3 = tf.constant(Fcm3, dtype=tf.float32)
input_shape = (32, 32, 1)
input_im = keras.layers.Input(shape=input_shape, name='input')
vis = keras.layers.Lambda(hp.Lambda_dft3(F), name='vis')(input_im)
if sigma is not None:
vis_split = keras.layers.Lambda(hp.Lambda_split)(vis)
vis_split = keras.layers.GaussianNoise(sigma)(vis_split)
vis = keras.layers.Lambda(hp.Lambda_combine)(vis_split)
vis_amp = keras.layers.Lambda(hp.Lambda_amp, name='vis_amp')(vis)
vis_angle_noisy = keras.layers.Lambda(hp.Lambda_angle)(vis)
cphase = keras.layers.Lambda(
hp.Lambda_cphase(cphase_proj))(vis_angle_noisy)
else:
vis_amp = keras.layers.Lambda(hp.Lambda_amp, name='vis_amp')(vis)
cphase = keras.layers.Lambda(hp.Lambda_cphase2(F_cphase),
name='cphase')(input_im)
# Sampling section
if generator == 'realnvp':
encoder, resnet_list, permute_list = realnvp_encoder(
output_dim=n_sites, n_coupling=n_coupling_layers)
decoder = realnvp_decoder(resnet_list,
permute_list,
output_dim=n_sites,
n_coupling=n_coupling_layers)
z = keras.layers.Lambda(Lambda_Gaussian(output_dim=n_sites),
name='random_seeds')(input_im)
flow_sample, logdet = decoder(z)
elif generator == 'gaussian':
flow_sample, logdet = Gaussian_sampling(
output_dim=n_sites, name='gaussian_samples')(input_im)
site_mask = STE_layer(name='sampling')(flow_sample)
logdet2 = keras.layers.Lambda(Lambda_logdet_sigmoid(1.0))(flow_sample)
logprob = keras.layers.Add(name='logprob')([logdet, logdet2])
if obs_prob is None:
vis_mask = keras.layers.Lambda(hp.Lambda_vis_mask2(Fm1, Fm2),
name='vis_mask')(site_mask)
cphase_mask = keras.layers.Lambda(hp.Lambda_cphase_mask2(
Fcm1, Fcm2, Fcm3),
name='cphase_mask')(site_mask)
else:
prob_mask = np.zeros(n_sites)
for k in obs_prob.keys():
prob_mask[tlib[k]] = obs_prob[k]
site_mask_weather = Site_mask_prob(prob_mask)(site_mask)
vis_mask = keras.layers.Lambda(hp.Lambda_vis_mask2(Fm1, Fm2),
name='vis_mask')(site_mask_weather)
cphase_mask = keras.layers.Lambda(
hp.Lambda_cphase_mask2(Fcm1, Fcm2, Fcm3),
name='cphase_mask')(site_mask_weather)
# vis_mask = keras.layers.Lambda(hp.Lambda_vis_mask2(Fm1, Fm2), name='vis_mask')(site_mask)
# cphase_mask = keras.layers.Lambda(hp.Lambda_cphase_mask2(Fcm1, Fcm2, Fcm3), name='cphase_mask')(site_mask)
vis_amp_selected = keras.layers.Lambda(
hp.Lambda_select)([vis_mask, vis_amp])
cphase_selected = keras.layers.Lambda(
hp.Lambda_select)([cphase_mask, cphase])
# Reconstruction section
inputs = keras.layers.concatenate([vis_amp_selected, cphase_selected], -1)
vis = keras.layers.Dense(1000,
activation=None,
use_bias=True,
kernel_initializer=RandomUniform(minval=-5e-4,
maxval=5e-4,
seed=None),
name='dense_vis1')(inputs)
vis = keras.layers.LeakyReLU(alpha=0.3, name='acti_vis1')(vis)
vis = keras.layers.BatchNormalization()(vis)
vis = keras.layers.Dense(1000,
activation=None,
use_bias=True,
kernel_initializer=RandomUniform(minval=-5e-4,
maxval=5e-4,
seed=None),
name='dense_vis2')(vis)
vis = keras.layers.LeakyReLU(alpha=0.3, name='acti_vis2')(vis)
vis = keras.layers.BatchNormalization()(vis)
vis = keras.layers.Dense(1000,
activation=None,
use_bias=True,
kernel_initializer=RandomUniform(minval=-5e-4,
maxval=5e-4,
seed=None),
name='dense_vis3')(vis)
vis = keras.layers.LeakyReLU(alpha=0.3, name='acti_vis3')(vis)
vis = keras.layers.BatchNormalization()(vis)
vis_angle = keras.layers.Dense(n_vis,
activation=None,
use_bias=True,
kernel_initializer=RandomUniform(
minval=-5e-4, maxval=5e-4, seed=None),
name='vis_angle_pred')(vis)
vis_pred = keras.layers.Lambda(
hp.Lambda_Vis, name='vis_pred')([vis_amp_selected, vis_angle])
# vis_amp_pred = keras.layers.Dense(n_vis, activation=None, use_bias=True, kernel_initializer=RandomUniform(minval=-5e-4, maxval=5e-4, seed=None), name='vis_amp_pred')(vis)
# vis_pred = keras.layers.Lambda(hp.Lambda_Vis, name='vis_pred')([vis_amp_pred, vis_angle])
dirty_im = keras.layers.Dense(32 * 32 * 1,
activation=acti,
use_bias=True,
kernel_initializer=RandomUniform(
minval=-5e-4, maxval=5e-4, seed=None),
name='dense_dirtyimage')(vis_pred)
dirty_im_reshape = keras.layers.Reshape((32, 32, 1))(dirty_im)
recon = _unet_from_tensor(dirty_im_reshape, filt, kern, acti)
recon = keras.layers.ReLU(name='recon')(recon)
cphase_pred = keras.layers.Lambda(hp.Lambda_cphase(cphase_proj),
name='cphase_pred')(vis_angle)
# model = keras.models.Model(inputs=input_im, outputs=[recon, site_mask, energy, vis_pred, cphase_pred])
model = keras.models.Model(
inputs=input_im,
outputs=[recon, site_mask, logprob, vis_angle, cphase_pred])
return model
def IsingCpAmpFeatureNet(t1,
t2,
tc1,
tc2,
tc3,
cphase_proj,
n_ising_layers=5,
slope_const=1e2,
n_layers=3,
n_hid=1000,
sigma=None,
feature_name='both',
binary_slope=10):
filt = 64
kern = 3
acti = None
tlib = {}
tind = 0
telescopes = np.concatenate([t1, t2])
for k in range(len(telescopes)):
if telescopes[k] not in tlib:
tlib[telescopes[k]] = tind
tind += 1
if 'SPT' not in telescopes:
tlib['SPT'] = tind
tind += 1
elif 'GLT' not in telescopes:
tlib['GLT'] = tind
tind += 1
n_sites = tind
Fm1 = np.zeros((len(t1), n_sites))
Fm2 = np.zeros((len(t1), n_sites))
for k in range(len(t1)):
Fm1[k, tlib[t1[k]]] = 1
Fm2[k, tlib[t2[k]]] = 1
Fm1 = tf.constant(Fm1, dtype=tf.float32)
Fm2 = tf.constant(Fm2, dtype=tf.float32)
Fcm1 = np.zeros((len(tc1), n_sites))
Fcm2 = np.zeros((len(tc1), n_sites))
Fcm3 = np.zeros((len(tc1), n_sites))
for k in range(len(tc1)):
Fcm1[k, tlib[tc1[k]]] = 1
Fcm2[k, tlib[tc2[k]]] = 1
Fcm3[k, tlib[tc3[k]]] = 1
Fcm1 = tf.constant(Fcm1, dtype=tf.float32)
Fcm2 = tf.constant(Fcm2, dtype=tf.float32)
Fcm3 = tf.constant(Fcm3, dtype=tf.float32)
input_shape = (len(t1), )
vis = keras.layers.Input(shape=input_shape,
dtype=tf.complex64,
name='input')
if sigma is not None:
vis_split = keras.layers.Lambda(hp.Lambda_split)(vis)
vis_split = keras.layers.GaussianNoise(sigma)(vis_split)
vis = keras.layers.Lambda(hp.Lambda_combine)(vis_split)
vis_amp = keras.layers.Lambda(hp.Lambda_amp, name='vis_amp')(vis)
vis_angle_noisy = keras.layers.Lambda(hp.Lambda_angle)(vis)
cphase = keras.layers.Lambda(
hp.Lambda_cphase(cphase_proj))(vis_angle_noisy)
else:
vis_amp = keras.layers.Lambda(hp.Lambda_amp, name='vis_amp')(vis)
vis_angle = keras.layers.Lambda(hp.Lambda_angle)(vis)
cphase = keras.layers.Lambda(hp.Lambda_cphase(cphase_proj))(vis_angle)
ising_sample, energy = Ising_sampling2(output_dim=n_sites,
name='ising',
my_initializer=Constant(0.1))(
vis,
n_layers=n_ising_layers,
const=slope_const)
site_mask = keras.layers.Lambda(hp.Lambda_binary_convert(binary_slope),
name='sampling')(ising_sample)
vis_mask = keras.layers.Lambda(hp.Lambda_vis_mask2(Fm1, Fm2),
name='vis_mask')(site_mask)
cphase_mask = keras.layers.Lambda(hp.Lambda_cphase_mask2(Fcm1, Fcm2, Fcm3),
name='cphase_mask')(site_mask)
vis_amp_selected = keras.layers.Lambda(
hp.Lambda_select)([vis_mask, vis_amp])
cphase_selected = keras.layers.Lambda(
hp.Lambda_select)([cphase_mask, cphase])
# Reconstruction section
inputs = keras.layers.concatenate([vis_amp_selected, cphase_selected], -1)
vis_pred = keras.layers.Dense(1000,
activation=None,
use_bias=True,
kernel_initializer=RandomUniform(
minval=-5e-4, maxval=5e-4, seed=None),
name='dense_vis1')(inputs)
vis_pred = keras.layers.LeakyReLU(alpha=0.3, name='acti_vis1')(vis_pred)
vis_pred = keras.layers.BatchNormalization()(vis_pred)
vis_pred = keras.layers.Dense(1000,
activation=None,
use_bias=True,
kernel_initializer=RandomUniform(
minval=-5e-4, maxval=5e-4, seed=None),
name='dense_vis2')(vis_pred)
vis_pred = keras.layers.LeakyReLU(alpha=0.3, name='acti_vis2')(vis_pred)
vis_pred = keras.layers.BatchNormalization()(vis_pred)
vis_angle = keras.layers.Dense(len(t1),
activation=None,
use_bias=True,
kernel_initializer=RandomUniform(
minval=-5e-4, maxval=5e-4, seed=None),
name='dense_vis3')(vis_pred)
layer_input = keras.layers.Lambda(
hp.Lambda_Vis, name='vis_pred')([vis_amp_selected, vis_angle])
for k in range(n_layers):
hidden = keras.layers.Dense(
n_hid,
activation=acti,
use_bias=True,
kernel_initializer=RandomUniform(minval=-5e-4,
maxval=5e-4,
seed=None))(layer_input)
hidden_act = keras.layers.ReLU()(hidden)
layer_input = keras.layers.BatchNormalization()(hidden_act)
if feature_name == 'class':
bh_class = keras.layers.Dense(
2,
activation=acti,
use_bias=True,
kernel_initializer=RandomUniform(minval=-5e-4,
maxval=5e-4,
seed=None))(layer_input)
bh_class = keras.layers.Softmax(axis=-1, name='bh_class')(bh_class)
model = keras.models.Model(inputs=vis,
outputs=[bh_class, site_mask, energy])
elif feature_name == 'spin':
spin_class = keras.layers.Dense(7,
activation=acti,
use_bias=True,
kernel_initializer=RandomUniform(
minval=-5e-4,
maxval=5e-4,
seed=None))(layer_input)
spin_class = keras.layers.Softmax(axis=-1,
name='spin_class')(spin_class)
model = keras.models.Model(inputs=vis,
outputs=[spin_class, site_mask, energy])
elif feature_name == 'both':
combined_class = keras.layers.Dense(13,
activation=acti,
use_bias=True,
kernel_initializer=RandomUniform(
minval=-5e-4,
maxval=5e-4,
seed=None))(layer_input)
combined_class = keras.layers.Softmax(
axis=-1, name='combined_class')(combined_class)
model = keras.models.Model(inputs=vis,
outputs=[combined_class, site_mask, energy])
# model = keras.models.Model(inputs=vis, outputs=[bh_class, spin_class, site_mask, energy])
# model = keras.models.Model(inputs=vis, outputs=[combined_class, site_mask, energy])
return model
def IsingMutipleCpAmpNet(t1_list,
t2_list,
tc1_list,
tc2_list,
tc3_list,
F_list,
F_cphase_list,
cphase_proj_list,
n_ising_layers=5,
slope_const=1e1,
sigma=None):
filt = 64
kern = 3
acti = None
Fm1_list = []
Fm2_list = []
Fcm1_list = []
Fcm2_list = []
Fcm3_list = []
for k in range(len(t1_list)):
t1 = t1_list[k]
t2 = t2_list[k]
tc1 = tc1_list[k]
tc2 = tc2_list[k]
tc3 = tc3_list[k]
F = F_list[k]
F_cphase = F_cphase_list[k]
cphase_proj = cphase_proj_list[k]
if k == 0:
tlib = {}
tind = 0
telescopes = np.concatenate([t1, t2])
for k in range(len(telescopes)):
if telescopes[k] not in tlib:
tlib[telescopes[k]] = tind
tind += 1
if 'SPT' not in telescopes:
tlib['SPT'] = tind
tind += 1
elif 'GLT' not in telescopes:
tlib['GLT'] = tind
tind += 1
n_sites = tind
Fm1 = np.zeros((len(t1), n_sites))
Fm2 = np.zeros((len(t1), n_sites))
for k in range(len(t1)):
Fm1[k, tlib[t1[k]]] = 1
Fm2[k, tlib[t2[k]]] = 1
Fm1 = tf.constant(Fm1, dtype=tf.float32)
Fm2 = tf.constant(Fm2, dtype=tf.float32)
Fcm1 = np.zeros((len(tc1), n_sites))
Fcm2 = np.zeros((len(tc1), n_sites))
Fcm3 = np.zeros((len(tc1), n_sites))
for k in range(len(tc1)):
Fcm1[k, tlib[tc1[k]]] = 1
Fcm2[k, tlib[tc2[k]]] = 1
Fcm3[k, tlib[tc3[k]]] = 1
Fcm1 = tf.constant(Fcm1, dtype=tf.float32)
Fcm2 = tf.constant(Fcm2, dtype=tf.float32)
Fcm3 = tf.constant(Fcm3, dtype=tf.float32)
Fm1_list.append(Fm1)
Fm2_list.append(Fm2)
Fcm1_list.append(Fcm1)
Fcm2_list.append(Fcm2)
Fcm3_list.append(Fcm3)
input_shape = (32, 32, 1)
input_im = keras.layers.Input(shape=input_shape, name='input')
# Sampling section
ising_sample, energy = Ising_sampling2(output_dim=n_sites,
name='ising',
my_initializer=Constant(0.1))(
input_im,
n_layers=n_ising_layers,
const=slope_const)
site_mask = keras.layers.Lambda(hp.Lambda_binary_convert(10),
name='sampling')(ising_sample)
outputs_list = []
for k in range(len(t1_list)):
F = F_list[k]
F_cphase = F_cphase_list[k]
cphase_proj = cphase_proj_list[k]
Fm1 = Fm1_list[k]
Fm2 = Fm2_list[k]
Fcm1 = Fcm1_list[k]
Fcm2 = Fcm2_list[k]
Fcm3 = Fcm3_list[k]
n_vis = F.shape[0]
n_cphase = F_cphase.shape[0]
F = tf.constant(F, dtype=tf.complex64)
F_cphase = tf.constant(F_cphase, dtype=tf.complex64)
cphase_proj = tf.constant(cphase_proj, dtype=tf.float32)
vis = keras.layers.Lambda(hp.Lambda_dft3(F),
name='vis' + str(k))(input_im)
if sigma is not None:
vis_split = keras.layers.Lambda(hp.Lambda_split)(vis)
vis_split = keras.layers.GaussianNoise(sigma)(vis_split)
vis = keras.layers.Lambda(hp.Lambda_combine)(vis_split)
vis_amp = keras.layers.Lambda(hp.Lambda_amp,
name='vis_amp' + str(k))(vis)
vis_angle_noisy = keras.layers.Lambda(hp.Lambda_angle)(vis)
cphase = keras.layers.Lambda(
hp.Lambda_cphase(cphase_proj))(vis_angle_noisy)
else:
vis_amp = keras.layers.Lambda(hp.Lambda_amp,
name='vis_amp' + str(k))(vis)
cphase = keras.layers.Lambda(hp.Lambda_cphase2(F_cphase),
name='cphase' + str(k))(input_im)
vis_mask = keras.layers.Lambda(hp.Lambda_vis_mask2(Fm1, Fm2),
name='vis_mask' + str(k))(site_mask)
cphase_mask = keras.layers.Lambda(
hp.Lambda_cphase_mask2(Fcm1, Fcm2, Fcm3),
name='cphase_mask' + str(k))(site_mask)
vis_amp_selected = keras.layers.Lambda(
hp.Lambda_select)([vis_mask, vis_amp])
cphase_selected = keras.layers.Lambda(
hp.Lambda_select)([cphase_mask, cphase])
# Reconstruction section
inputs = keras.layers.concatenate([vis_amp_selected, cphase_selected],
-1)
vis = keras.layers.Dense(1000,
activation=None,
use_bias=True,
kernel_initializer=RandomUniform(minval=-5e-4,
maxval=5e-4,
seed=None),
name='dense_vis1' + str(k))(inputs)
vis = keras.layers.LeakyReLU(alpha=0.3, name='acti_vis1' + str(k))(vis)
vis = keras.layers.BatchNormalization()(vis)
vis = keras.layers.Dense(1000,
activation=None,
use_bias=True,
kernel_initializer=RandomUniform(minval=-5e-4,
maxval=5e-4,
seed=None),
name='dense_vis2' + str(k))(vis)
vis = keras.layers.LeakyReLU(alpha=0.3, name='acti_vis2' + str(k))(vis)
vis = keras.layers.BatchNormalization()(vis)
vis_angle = keras.layers.Dense(n_vis,
activation=None,
use_bias=True,
kernel_initializer=RandomUniform(
minval=-5e-4,
maxval=5e-4,
seed=None),
name='dense_vis3' + str(k))(vis)
vis_pred = keras.layers.Lambda(hp.Lambda_Vis, name='vis_pred' +
str(k))([vis_amp_selected, vis_angle])
dirty_im = keras.layers.Dense(
32 * 32 * 1,
activation=acti,
use_bias=True,
kernel_initializer=RandomUniform(minval=-5e-4,
maxval=5e-4,
seed=None),
name='dense_dirtyimage' + str(k))(vis_pred)
dirty_im_reshape = keras.layers.Reshape((32, 32, 1))(dirty_im)
recon = _unet_from_tensor(dirty_im_reshape, filt, kern, acti)
recon = keras.layers.ReLU(name='recon' + str(k))(recon)
cphase_pred = keras.layers.Lambda(hp.Lambda_cphase(cphase_proj),
name='cphase_pred' +
str(k))(vis_angle)
outputs_list.append(recon)
outputs_list.append(vis_pred)
outputs_list.append(cphase_pred)
model = keras.models.Model(inputs=input_im,
outputs=outputs_list + [site_mask, energy])
return model
def IsingMutipleVisNet(t1_list,
t2_list,
F_list,
n_ising_layers=5,
slope_const=1e2,
sigma=None):
filt = 64
kern = 3
acti = None
Fm1_list = []
Fm2_list = []
for k in range(len(t1_list)):
t1 = t1_list[k]
t2 = t2_list[k]
F = F_list[k]
if k == 0:
tlib = {}
tind = 0
telescopes = np.concatenate([t1, t2])
for k in range(len(telescopes)):
if telescopes[k] not in tlib:
tlib[telescopes[k]] = tind
tind += 1
if 'SPT' not in telescopes:
tlib['SPT'] = tind
tind += 1
elif 'GLT' not in telescopes:
tlib['GLT'] = tind
tind += 1
n_sites = tind
Fm1 = np.zeros((len(t1), n_sites))
Fm2 = np.zeros((len(t1), n_sites))
for k in range(len(t1)):
Fm1[k, tlib[t1[k]]] = 1
Fm2[k, tlib[t2[k]]] = 1
Fm1 = tf.constant(Fm1, dtype=tf.float32)
Fm2 = tf.constant(Fm2, dtype=tf.float32)
Fm1_list.append(Fm1)
Fm2_list.append(Fm2)
input_shape = (32, 32, 1)
input_im = keras.layers.Input(shape=input_shape, name='input')
ising_sample, energy = Ising_sampling2(output_dim=n_sites,
name='ising',
my_initializer=Constant(0.1))(
input_im,
n_layers=n_ising_layers,
const=slope_const)
site_mask = keras.layers.Lambda(hp.Lambda_binary_convert(10),
name='sampling')(ising_sample)
outputs_list = []
for k in range(len(t1_list)):
F = F_list[k]
Fm1 = Fm1_list[k]
Fm2 = Fm2_list[k]
vis = keras.layers.Lambda(hp.Lambda_dft(F))(input_im)
if sigma is not None:
vis = keras.layers.GaussianNoise(sigma)(vis)
vis_mask = keras.layers.Lambda(hp.Lambda_vis_mask2(Fm1, Fm2),
name='vis_mask' + str(k))(site_mask)
vis_selected = keras.layers.Lambda(hp.Lambda_select0)([vis_mask, vis])
vis_reshape = keras.layers.Reshape((2 * F.shape[0], ))(vis_selected)
dirty_im = keras.layers.Dense(
32 * 32 * 1,
activation=acti,
use_bias=True,
kernel_initializer=RandomUniform(minval=-5e-4,
maxval=5e-4,
seed=None),
name='dense_dirtyimage' + str(k))(vis_reshape)
dirty_im_reshape = keras.layers.Reshape((32, 32, 1))(dirty_im)
outputs = _unet_from_tensor(dirty_im_reshape, filt, kern, acti)
outputs = keras.layers.ReLU(name='recon' + str(k))(outputs)
outputs_list.append(outputs)
# model = keras.models.Model(inputs=input_im, outputs=[outputs, site_mask, energy])
model = keras.models.Model(inputs=input_im,
outputs=outputs_list + [site_mask, energy])
return model
def IsingVisNet(t1,
t2,
F,
n_ising_layers=5,
slope_const=1e2,
sigma=None,
binary_slope=10,
obs_prob=None):
filt = 64
kern = 3
acti = None
tlib = {}
tind = 0
telescopes = np.concatenate([t1, t2])
for k in range(len(telescopes)):
if telescopes[k] not in tlib:
tlib[telescopes[k]] = tind
tind += 1
if 'SPT' not in telescopes:
tlib['SPT'] = tind
tind += 1
elif 'GLT' not in telescopes:
tlib['GLT'] = tind
tind += 1
n_sites = tind
Fm1 = np.zeros((len(t1), n_sites))
Fm2 = np.zeros((len(t1), n_sites))
for k in range(len(t1)):
Fm1[k, tlib[t1[k]]] = 1
Fm2[k, tlib[t2[k]]] = 1
Fm1 = tf.constant(Fm1, dtype=tf.float32)
Fm2 = tf.constant(Fm2, dtype=tf.float32)
input_shape = (32, 32, 1)
input_im = keras.layers.Input(shape=input_shape, name='input')
vis = keras.layers.Lambda(hp.Lambda_dft(F))(input_im)
if sigma is not None:
vis = keras.layers.GaussianNoise(sigma)(vis)
ising_sample, energy = Ising_sampling2(output_dim=n_sites,
name='ising',
my_initializer=Constant(0.1))(
input_im,
n_layers=n_ising_layers,
const=slope_const)
# ising_sample, energy= Ising_sampling(output_dim=n_sites, name='ising',
# my_initializer=Constant(0.1))(input_im, n_layers=n_ising_layers, const=slope_const, xi=0.3, L=1)
if obs_prob is None:
site_mask = keras.layers.Lambda(hp.Lambda_binary_convert(binary_slope),
name='sampling')(ising_sample)
vis_mask = keras.layers.Lambda(hp.Lambda_vis_mask2(Fm1, Fm2),
name='vis_mask')(site_mask)
else:
site_mask = keras.layers.Lambda(hp.Lambda_binary_convert(binary_slope),
name='sampling')(ising_sample)
prob_mask = np.zeros(n_sites)
for k in obs_prob.keys():
prob_mask[tlib[k]] = obs_prob[k]
site_mask_weather = Site_mask_prob(prob_mask)(site_mask)
vis_mask = keras.layers.Lambda(hp.Lambda_vis_mask2(Fm1, Fm2),
name='vis_mask')(site_mask_weather)
# site_mask = keras.layers.Lambda(hp.Lambda_binary_convert(binary_slope), name='sampling')(ising_sample)
# vis_mask = keras.layers.Lambda(hp.Lambda_vis_mask2(Fm1, Fm2), name='vis_mask')(site_mask)
vis_selected = keras.layers.Lambda(hp.Lambda_select0)([vis_mask, vis])
vis_reshape = keras.layers.Reshape((2 * F.shape[0], ))(vis_selected)
dirty_im = keras.layers.Dense(32 * 32 * 1,
activation=acti,
use_bias=True,
kernel_initializer=RandomUniform(
minval=-5e-4, maxval=5e-4, seed=None),
name='dense_dirtyimage')(vis_reshape)
dirty_im_reshape = keras.layers.Reshape((32, 32, 1))(dirty_im)
outputs = _unet_from_tensor(dirty_im_reshape, filt, kern, acti)
outputs = keras.layers.ReLU(name='recon')(outputs)
model = keras.models.Model(inputs=input_im,
outputs=[outputs, site_mask, energy])
return model
def IsingVisFeatureNet(t1,
t2,
n_ising_layers=5,
slope_const=1e2,
n_layers=3,
n_hid=1000,
sigma=None):
filt = 64
kern = 3
acti = None
tlib = {}
tind = 0
telescopes = np.concatenate([t1, t2])
for k in range(len(telescopes)):
if telescopes[k] not in tlib:
tlib[telescopes[k]] = tind
tind += 1
if 'SPT' not in telescopes:
tlib['SPT'] = tind
tind += 1
elif 'GLT' not in telescopes:
tlib['GLT'] = tind
tind += 1
n_sites = tind
Fm1 = np.zeros((len(t1), n_sites))
Fm2 = np.zeros((len(t1), n_sites))
for k in range(len(t1)):
Fm1[k, tlib[t1[k]]] = 1
Fm2[k, tlib[t2[k]]] = 1
Fm1 = tf.constant(Fm1, dtype=tf.float32)
Fm2 = tf.constant(Fm2, dtype=tf.float32)
input_shape = (len(t1), )
vis = keras.layers.Input(shape=input_shape,
dtype=tf.complex64,
name='input')
vis_split = keras.layers.Lambda(hp.Lambda_split)(vis)
if sigma is not None:
vis_split = keras.layers.GaussianNoise(sigma)(vis_split)
ising_sample, energy = Ising_sampling2(output_dim=n_sites,
name='ising',
my_initializer=Constant(0.1))(
vis,
n_layers=n_ising_layers,
const=slope_const)
site_mask = keras.layers.Lambda(hp.Lambda_binary_convert(10),
name='sampling')(ising_sample)
vis_mask = keras.layers.Lambda(hp.Lambda_vis_mask2(Fm1, Fm2),
name='vis_mask')(site_mask)
vis_selected = keras.layers.Lambda(
hp.Lambda_select0)([vis_mask, vis_split])
vis_reshape = keras.layers.Reshape((2 * len(t1), ))(vis_selected)
layer_input = vis_reshape
for k in range(n_layers):
hidden = keras.layers.Dense(
n_hid,
activation=acti,
use_bias=True,
kernel_initializer=RandomUniform(minval=-5e-4,
maxval=5e-4,
seed=None))(layer_input)
hidden_act = keras.layers.ReLU()(hidden)
layer_input = keras.layers.BatchNormalization()(hidden_act)
# bh_class = keras.layers.Dense(2, activation=acti, use_bias=True, kernel_initializer=RandomUniform(minval=-5e-4, maxval=5e-4, seed=None))(layer_input)
# bh_class = keras.layers.Softmax(axis=-1, name='bh_class')(bh_class)
# spin_class = keras.layers.Dense(7, activation=acti, use_bias=True, kernel_initializer=RandomUniform(minval=-5e-4, maxval=5e-4, seed=None))(layer_input)
# spin_class = keras.layers.Softmax(axis=-1, name='spin_class')(spin_class)
combined_class = keras.layers.Dense(13,
activation=acti,
use_bias=True,
kernel_initializer=RandomUniform(
minval=-5e-4,
maxval=5e-4,
seed=None))(layer_input)
combined_class = keras.layers.Softmax(
axis=-1, name='combined_class')(combined_class)
# model = keras.models.Model(inputs=vis, outputs=[bh_class, spin_class, site_mask, energy])
model = keras.models.Model(inputs=vis,
outputs=[combined_class, site_mask, energy])
return model
