def test1():
mat_contents = sio.loadmat(pathdat + 'dict_pca_2fa_2freq.mat')
#dict_pca
coeff = np.array(mat_contents["coeff"].astype(np.float32))
par = mat_contents["par"]
batch_size = 800
Nk = par[0]['irfreq'][0][0][0] #892#far.shape[0]#par.irfreq#
Ndiv = coeff.shape[1] #par[0]['ndiv'][0][0][0]#16
Nscan = 3
orig_Ndiv = coeff.shape[0] // Nscan
npar = 8 ##
model = tf_wrap.tf_model_top([None, Ndiv], [None, npar],
tf_prediction_func,
tf_optimize_func,
tf_error_func,
arg=0.5)
#model.restore(pathdat + 'test_model_save_2fa_2freq')
fa1 = 45.0 #par[0]['fa1'][0][0][0].astype(np.float32)#35#30 #deg
fa2 = 30.0 #par[0]['fa2'][0][0][0].astype(np.float32)
tr = par[0]['tr'][0][0][0].astype(np.float32) #3.932#4.337 #ms
ti = par[0]['ti'][0][0][0].astype(np.float32) #11.0 #ms
te = par[0]['te'][0][0][0].astype(np.float32) #1.5 #ms
far, trr, ter = simut.rftr_const(Nk, 1.0, tr, te)
M0 = simut.def_M0()
#run tensorflow on cpu, count of gpu = 0
config = tf.ConfigProto() #(device_count = {'GPU': 0})
#allow tensorflow release gpu memory
config.gpu_options.allow_growth = True
t1t2_group = np.zeros((4, 2), dtype=np.float64)
t1t2_group[0, 0] = 600.0 / 5000.0 #600 #0.12
t1t2_group[0, 1] = 40.0 / 500.0 #40 #0.08
t1t2_group[1, 0] = 1000.0 / 5000.0 #1000 #0.2
t1t2_group[1, 1] = 80.0 / 500.0 #80 #0.16
t1t2_group[2, 0] = 3000.0 / 5000.0 #3000 #0.6
t1t2_group[2, 1] = 200.0 / 500.0 #200 #0.4
t1t2_group[3, 0] = 0.0 / 5000.0
t1t2_group[3, 1] = 0.0 / 500.0
for i in range(1000000):
batch_ys = np.random.uniform(0, 1,
(batch_size, npar)).astype(np.float64)
batch_ys[:, 2] = np.random.uniform(0, 1.0 / tr, (batch_size)).astype(
np.float64) #np.zeros(batch_size)#
batch_ys_tmp = np.random.uniform(0, 4, (batch_size))
for k in range(batch_size):
if batch_ys_tmp[k] <= 4 and batch_ys_tmp[k] > 3:
batch_ys[k, 0] = t1t2_group[0, 0] + np.random.uniform(
-0.05, 0.025) #0.07 to 0.145
batch_ys[k, 1] = t1t2_group[0, 1] + np.random.uniform(
-0.05, 0.025) #0.04 to 0.105
batch_ys[k, 4] = 1.0
batch_ys[k, 5] = 0.0
batch_ys[k, 6] = 0.0
elif batch_ys_tmp[k] <= 3 and batch_ys_tmp[k] > 2:
batch_ys[k, 0] = t1t2_group[1, 0] + np.random.uniform(
-0.035, 0.035) #-0.035,0.035 #0.145 to 0.255
batch_ys[k, 1] = t1t2_group[1, 1] + np.random.uniform(
-0.035, 0.035) #0.105 to 0.215
batch_ys[k, 4] = 0.0
batch_ys[k, 5] = 1.0
batch_ys[k, 6] = 0.0
elif batch_ys_tmp[k] <= 2 and batch_ys_tmp[k] > 1:
batch_ys[k, 0] = t1t2_group[2, 0] + np.random.uniform(
-0.15, 0.25) #-0.15,0.25 #0.45 to 0.85
batch_ys[k, 1] = t1t2_group[2, 1] + np.random.uniform(
-0.15, 0.25) #0.55 to 0.65
batch_ys[k, 4] = 0.0
batch_ys[k, 5] = 0.0
batch_ys[k, 6] = 1.0
else:
batch_ys[k, 0] = t1t2_group[3,
0] #+ np.random.uniform(-0.0,0.02)
batch_ys[k, 1] = t1t2_group[3,
1] #+ np.random.uniform(-0.0,0.08)
batch_ys[k, 4] = 0.0
batch_ys[k, 5] = 0.0
batch_ys[k, 6] = 0.0
T1r, T2r, dfr, PDr = ssmrf.set_par(batch_ys[..., 0:4])
b1r = 2.0 * batch_ys[..., 7] + 0.5
batch_xs_c1 = ssmrf.bloch_sim_batch_cuda3(batch_size, 100, Nk, PDr,
T1r, T2r, dfr, b1r, M0, trr,
ter, fa1 * far, ti)
batch_xs_c2 = ssmrf.bloch_sim_batch_cuda3(batch_size, 100, Nk, PDr,
T1r, T2r, dfr, b1r, M0, trr,
ter, fa2 * far, ti)
#batch_xs_c3 = ssmrf.bloch_sim_batch_cuda2( batch_size, 100, Nk, PDr, T1r, T2r, dfr, M0, trr, ter, np.absolute(fa2*far), ti )
batch_xs_c3 = ssmrf.bloch_sim_batch_cuda3(batch_size, 100, Nk, PDr,
T1r, T2r, dfr + 107.0, b1r,
M0, trr, ter, fa2 * far, ti)
#ut.plot(np.absolute(batch_xs_c[0,:]))
batch_xs = np.zeros((batch_size, Nscan * orig_Ndiv),
dtype=batch_xs_c1.dtype)
if orig_Ndiv is not Nk:
batch_xs[:, 0:orig_Ndiv] = (
simut.average_dict(batch_xs_c1, orig_Ndiv)
) #(np.dot(np.absolute(simut.average_dict(batch_xs_c, Ndiv)), coeff))
batch_xs[:, orig_Ndiv:2 * orig_Ndiv] = (
simut.average_dict(batch_xs_c2, orig_Ndiv)
) #(np.dot(np.absolute(simut.average_dict(batch_xs_c, Ndiv)), coeff))
batch_xs[:, 2 * orig_Ndiv:3 * orig_Ndiv] = (
simut.average_dict(batch_xs_c3, orig_Ndiv)
) #(np.dot(np.absolute(simut.average_dict(batch_xs_c, Ndiv)), coeff))
batch_xs = batch_xs + np.random.ranf(1)[0] * np.random.uniform(
-0.002, 0.002, (batch_xs.shape))
batch_xs = np.absolute(batch_xs)
if 1:
batch_xs = np.dot(batch_xs, coeff)
else:
batch_xs = batch_xs
for dd in range(batch_xs.shape[0]):
tc1 = batch_xs[dd, :]
normtc1 = np.linalg.norm(tc1)
if normtc1 > 0.01:
batch_xs[dd, :] = tc1
else:
batch_ys[dd, :] = np.zeros([1, npar])
batch_xs = batch_xs / np.ndarray.max(batch_xs.flatten())
model.test(batch_xs, batch_ys)
model.train(batch_xs, batch_ys)
if i % 100 == 0:
prey = model.prediction(batch_xs, np.zeros(batch_ys.shape))
for nn in range(npar):
ut.plot(prey[..., nn],
batch_ys[..., nn],
line_type='.',
pause_close=1)
model.save(pathdat + 'test_model_save_b1_2fa_2freq')
def test1():
mat_contents = sio.loadmat(pathdat + 'dict_pca.mat')
#dict_pca
#dictall = np.array(mat_contents["avedictall"].astype(np.float32))
#label = np.array(mat_contents["dict_label"].astype(np.float32))
coeff = np.array(mat_contents["coeff"].astype(np.float32))
#cn_orders = np.array(mat_contents["cn_orders"].astype(np.float32))
par = mat_contents["par"]
batch_size = 800
Nk = par[0]['irfreq'][0][0][0] #892#far.shape[0]#par.irfreq#
Ndiv = coeff.shape[1] #par[0]['ndiv'][0][0][0]#16
orig_Ndiv = coeff.shape[0]
npar = 4
model = tf_wrap.tf_model_top([None, Ndiv], [None, npar],
tf_prediction_func,
tf_optimize_func,
tf_error_func,
arg=0.5)
fa = par[0]['fa'][0][0][0].astype(np.float32) #35#30 #deg
tr = par[0]['tr'][0][0][0].astype(np.float32) #3.932#4.337 #ms
ti = par[0]['ti'][0][0][0].astype(np.float32) #11.0 #ms
#print(fa)
#print(tr)
#print(ti)
#print(Nk)
#print(Ndiv)
far, trr = simut.rftr_const(Nk, fa, tr)
M0 = simut.def_M0()
#run tensorflow on cpu, count of gpu = 0
config = tf.ConfigProto() #(device_count = {'GPU': 0})
#allow tensorflow release gpu memory
config.gpu_options.allow_growth = True
for i in range(1000000):
batch_ys = np.random.uniform(0, 1, (batch_size, 4)).astype(np.float64)
#batch_ys[:,0] = batch_ys[:,0] + 1.0*batch_ys[:,1]/10.0
#batch_ys[:,0] = np.random.uniform(0.07,1.0,(batch_size)).astype(np.float64)
#batch_ys[:,1] = np.random.uniform(0.0,0.2,(batch_size)).astype(np.float64)
batch_ys[:, 2] = np.random.uniform(0, 1.0 / tr,
(batch_size)).astype(np.float64)
#batch_ys[:,2] = np.zeros(batch_size)
#batch_ys[:,3] = np.ones(batch_size)#np.random.uniform(0.4,1,(batch_size)).astype(np.float64)#
#batch_ys[:,0] = np.round(batch_ys[:,0]*20)/20
#batch_ys[:,1] = np.round(batch_ys[:,1]*20)/20
#batch_ys[:,2] = np.round(batch_ys[:,2]*20)/20
#batch_ys[:,3] = np.round(batch_ys[:,3]*5)/5
#batch_ys[:,3] = np.round(batch_ys[:,3]*5)/5
# intial seq simulation with t1t2b0 values
#seq_data = ssad.irssfp_arrayin_data( batch_size, Nk ).set( batch_ys )
T1r, T2r, dfr, PDr = ssmrf.set_par(batch_ys)
batch_xs_c = ssmrf.bloch_sim_batch_cuda(batch_size, 100, Nk, PDr, T1r,
T2r, dfr, M0, trr, far, ti)
#ut.plot(np.absolute(batch_xs_c[0,:]))
if orig_Ndiv is not Nk:
batch_xs = np.absolute(
simut.average_dict(batch_xs_c, orig_Ndiv)
) #(np.dot(np.absolute(simut.average_dict(batch_xs_c, Ndiv)), coeff))
#batch_xs = np.absolute(simut.average_dict_cnorders(batch_xs_c, cn_orders)) #np.absolute(np.dot(batch_xs_c, cn_orders))
else:
batch_xs = np.absolute(batch_xs_c)
#ut.plot(np.absolute(batch_xs[0,:]))
batch_xt = batch_xs
batch_xs = batch_xs + np.random.ranf(1)[0] * np.random.uniform(
-0.1, 0.1, (batch_xs.shape))
#batch_xs = batch_xs/np.ndarray.max(batch_xs.flatten())
if 1:
batch_xs = np.dot(batch_xs, coeff)
batch_xt = np.dot(batch_xt, coeff)
else:
batch_xs = batch_xs
batch_xt = batch_xs
#batch_ys[:,3] = np.zeros(batch_size)
for dd in range(batch_xs.shape[0]):
tc1 = batch_xs[dd, :] #- np.mean(imall[i,:])
tc2 = batch_xt[dd, :]
normtc1 = np.linalg.norm(tc1)
normtc2 = np.linalg.norm(tc2)
if normtc2 > 0.1: #and batch_ys[dd,0]*5000 > 3*500*batch_ys[dd,1] and batch_ys[dd,0]*5000 < 20*500*batch_ys[dd,1]
batch_xs[dd, :] = tc1 #/normtc1
batch_xt[dd, :] = tc2 #/normtc2
else:
# batch_xs[dd,:] = np.zeros([1,Ndiv])
# batch_xt[dd,:] = np.zeros([1,Ndiv])
batch_ys[dd, :] = np.zeros([1, npar])
batch_xs = batch_xs / np.ndarray.max(batch_xs.flatten())
batch_xt = batch_xt / np.ndarray.max(batch_xt.flatten())
#for kk in range(batch_xs.shape[1]):
# batch_xs [:,kk] = (batch_xs[:,kk])/np.std(batch_xs[:,kk] )#- np.mean(batch_xs[:,kk])
# batch_xt [:,kk] = (batch_xt[:,kk])/np.std(batch_xt[:,kk] )#- np.mean(batch_xt[:,kk])
#ut.plot(np.real(batch_xs[0,:]),pause_close = 1)
#batch_ys[:,3] = np.ones(batch_size) * np.random.ranf(1)[0]
#batch_xs = batch_xs * batch_ys[0,3] #* np.random.ranf(1)[0]#
model.test(batch_xs, batch_ys)
model.train(batch_xt, batch_ys)
model.train(batch_xs, batch_ys)
if i % 100 == 0:
prey = model.prediction(batch_xs, np.zeros(batch_ys.shape))
ut.plot(prey[..., 0],
batch_ys[..., 0],
line_type='.',
pause_close=1)
ut.plot(prey[..., 1],
batch_ys[..., 1],
line_type='.',
pause_close=1)
ut.plot(prey[..., 2],
batch_ys[..., 2],
line_type='.',
pause_close=1)
ut.plot(prey[..., 3],
batch_ys[..., 3],
line_type='.',
pause_close=1)
model.save(pathdat + 'test_model_save')
def test1():
mat_contents = sio.loadmat(pathdat+'dict_pca.mat');#dict_pca
#dictall = np.array(mat_contents["avedictall"].astype(np.float32))
#label = np.array(mat_contents["dict_label"].astype(np.float32))
coeff = np.array(mat_contents["coeff"].astype(np.float32))
#cn_orders = np.array(mat_contents["cn_orders"].astype(np.float32))
par = mat_contents["par"]
batch_size = 800
Nk = par[0]['irfreq'][0][0][0]#892#far.shape[0]#par.irfreq#
Ndiv = coeff.shape[1]#par[0]['ndiv'][0][0][0]#16
orig_Ndiv = coeff.shape[0]
npar = 7
model = tf_wrap.tf_model_top([None, Ndiv], [None, npar], tf_prediction_func, tf_optimize_func, tf_error_func, arg = 0.5)
fa = par[0]['fa'][0][0][0].astype(np.float32)#35#30 #deg
tr = par[0]['tr'][0][0][0].astype(np.float32)#3.932#4.337 #ms
ti = par[0]['ti'][0][0][0].astype(np.float32)#11.0 #ms
te = 1.5 #ms
#print(fa)
#print(tr)
#print(ti)
#print(Nk)
#print(Ndiv)
far, trr,ter = simut.rftr_const(Nk, fa, tr, te)
M0 = simut.def_M0()
#run tensorflow on cpu, count of gpu = 0
config = tf.ConfigProto()#(device_count = {'GPU': 0})
#allow tensorflow release gpu memory
config.gpu_options.allow_growth=True
t1t2_group = np.zeros((4, 2),dtype = np.float64)
t1t2_group[0,0] = 600.0/5000.0
t1t2_group[0,1] = 40.0/500.0
t1t2_group[1,0] = 1000.0/5000.0
t1t2_group[1,1] = 80.0/500.0
t1t2_group[2,0] = 3000.0/5000.0
t1t2_group[2,1] = 200.0/500.0
t1t2_group[3,0] = 0.0/5000.0
t1t2_group[3,1] = 0.0/500.0
for i in range(1000000):
batch_ys = np.random.uniform(0,1,(batch_size,npar)).astype(np.float64)
batch_ys[:,2] = np.zeros(batch_size)#np.random.uniform(0,1.0/tr,(batch_size)).astype(np.float64)
batch_ys_tmp = np.random.uniform(0,4,(batch_size))
for k in range(batch_size):
if batch_ys_tmp[k] <= 4 and batch_ys_tmp[k] > 3:
batch_ys[k,0] = t1t2_group[0,0] + np.random.uniform(-0.05,0.025)
batch_ys[k,1] = t1t2_group[0,1] + np.random.uniform(-0.05,0.025)
batch_ys[k,4] = 1.0
batch_ys[k,5] = 0.0
batch_ys[k,6] = 0.0
elif batch_ys_tmp[k] <= 3 and batch_ys_tmp[k] > 2:
batch_ys[k,0] = t1t2_group[1,0] + np.random.uniform(-0.025,0.025)
batch_ys[k,1] = t1t2_group[1,1] + np.random.uniform(-0.025,0.025)
batch_ys[k,4] = 0.0
batch_ys[k,5] = 1.0
batch_ys[k,6] = 0.0
elif batch_ys_tmp[k] <= 2 and batch_ys_tmp[k] > 1:
batch_ys[k,0] = t1t2_group[2,0] + np.random.uniform(-0.15,0.25)
batch_ys[k,1] = t1t2_group[2,1] + np.random.uniform(-0.15,0.25)
batch_ys[k,4] = 0.0
batch_ys[k,5] = 0.0
batch_ys[k,6] = 1.0
else:
batch_ys[k,0] = t1t2_group[3,0]
batch_ys[k,1] = t1t2_group[3,1]
batch_ys[k,4] = 0.0
batch_ys[k,5] = 0.0
batch_ys[k,6] = 0.0
T1r, T2r, dfr, PDr = ssmrf.set_par(batch_ys[...,0:4])
batch_xs_c = ssmrf.bloch_sim_batch_cuda2( batch_size, 100, Nk, PDr, T1r, T2r, dfr, M0, trr, ter, far, ti )
#ut.plot(np.absolute(batch_xs_c[0,:]))
if orig_Ndiv is not Nk:
batch_xs = np.absolute(simut.average_dict(batch_xs_c, orig_Ndiv))#(np.dot(np.absolute(simut.average_dict(batch_xs_c, Ndiv)), coeff))
else:
batch_xs = np.absolute(batch_xs_c)
#ut.plot(np.absolute(batch_xs[0,:]))
#batch_xt = batch_xs
batch_xs = batch_xs + np.random.ranf(1)[0]*np.random.uniform(-0.1,0.1,(batch_xs.shape))
#batch_xs = batch_xs/np.ndarray.max(batch_xs.flatten())
if 1:
batch_xs = np.dot(batch_xs, coeff)
#batch_xt = np.dot(batch_xt, coeff)
else:
batch_xs = batch_xs
#batch_xt = batch_xs
for dd in range(batch_xs.shape[0]):
tc1 = batch_xs[dd,:] #- np.mean(imall[i,:])
#tc2 = batch_xt[dd,:]
normtc1 = np.linalg.norm(tc1)
#normtc2 = np.linalg.norm(tc2)
if normtc1 > 0.05: #and batch_ys[dd,0]*5000 > 3*500*batch_ys[dd,1] and batch_ys[dd,0]*5000 < 20*500*batch_ys[dd,1]
batch_xs[dd,:] = tc1#/normtc1
#batch_xt[dd,:] = tc2#/normtc2
else:
# batch_xs[dd,:] = np.zeros([1,Ndiv])
# batch_xt[dd,:] = np.zeros([1,Ndiv])
batch_ys[dd,:] = np.zeros([1,npar])
batch_xs = batch_xs/np.ndarray.max(batch_xs.flatten())
#batch_xt = batch_xt/np.ndarray.max(batch_xt.flatten())
#for kk in range(batch_xs.shape[1]):
# batch_xs [:,kk] = (batch_xs[:,kk])/np.std(batch_xs[:,kk] )#- np.mean(batch_xs[:,kk])
# batch_xt [:,kk] = (batch_xt[:,kk])/np.std(batch_xt[:,kk] )#- np.mean(batch_xt[:,kk])
#ut.plot(np.real(batch_xs[0,:]),pause_close = 1)
#batch_xs = batch_xs * batch_ys[0,3] #* np.random.ranf(1)[0]#
model.test(batch_xs, batch_ys)
model.train(batch_xs, batch_ys)
if i % 100 == 0:
prey = model.prediction(batch_xs,np.zeros(batch_ys.shape))
ut.plot(prey[...,0], batch_ys[...,0], line_type = '.', pause_close = 1)
ut.plot(prey[...,1], batch_ys[...,1], line_type = '.', pause_close = 1)
ut.plot(prey[...,2], batch_ys[...,2], line_type = '.', pause_close = 1)
ut.plot(prey[...,3], batch_ys[...,3], line_type = '.', pause_close = 1)
ut.plot(prey[...,4], batch_ys[...,4], line_type = '.', pause_close = 1)
model.save(pathdat + 'test_model_save')
def test1():
mat_contents = sio.loadmat(pathdat + 'dict_pca.mat')
#dict_pca
#dictall = np.array(mat_contents["avedictall"].astype(np.float32))
#label = np.array(mat_contents["dict_label"].astype(np.float32))
par = mat_contents["par"]
read_coeff_flag = 1 # 0 not use coeff; 1 read coeff from mat file; 2 generate coeff by pca
abs_flag = 0 #apply pca on absolute time course
batch_size = 800
Nk = par[0]['irfreq'][0][0][0] #892#far.shape[0]#par.irfreq#
if read_coeff_flag is 1:
coeff = np.array(mat_contents["coeff"].astype(np.float32))
Ndiv = coeff.shape[1] #Nk #par[0]['ndiv'][0][0][0]#16
orig_Ndiv = coeff.shape[0] #Nk
elif read_coeff_flag is 2:
Ndiv = 20 #20 pca
orig_Ndiv = Nk #coeff.shape[0]#Nk
else:
Ndiv = Nk #coeff.shape[1]#Nk #par[0]['ndiv'][0][0][0]#16
orig_Ndiv = Nk #coeff.shape[0]#Nk
npar = 4
model = tf_wrap.tf_model_top([None, Ndiv], [None, npar],
tf_prediction_func, tf_optimize_func,
tf_error_func)
fa = par[0]['fa'][0][0][0].astype(np.float32) #35#30 #deg
tr = par[0]['tr'][0][0][0].astype(np.float32) #3.932#4.337 #ms
ti = par[0]['ti'][0][0][0].astype(np.float32) #11.0 #ms
#print(fa)
#print(tr)
#print(ti)
#print(Nk)
#print(Ndiv)
far, trr = simut.rftr_const(Nk, fa, tr)
#far,trr = simut.rftr_rand(Nk, fa, tr, 2*tr)
M0 = simut.def_M0()
#run tensorflow on cpu, count of gpu = 0
config = tf.ConfigProto() #(device_count = {'GPU': 0})
#allow tensorflow release gpu memory
config.gpu_options.allow_growth = True
#compute pca
if read_coeff_flag is 2:
batch_ys = np.random.uniform(0, 1, (batch_size, 4)).astype(np.float64)
#batch_ys[:,0] = np.random.uniform(0.1,0.6,(batch_size)).astype(np.float64)
#batch_ys[:,1] = np.random.uniform(0.1,0.3,(batch_size)).astype(np.float64)
#batch_ys[:,2] = np.random.uniform(0,1.0/tr,(batch_size)).astype(np.float64)
#batch_ys[:,3] = np.random.uniform(0.1,1.0,(batch_size)).astype(np.float64)# np.ones(batch_size)## np.random.uniform(0.5,1,(batch_size)).astype(np.float64)#
# intial seq simulation with t1t2b0 values
npca = Ndiv
T1r, T2r, dfr, PDr = ssmrf.set_par(batch_ys)
batch_xs_c = ssmrf.bloch_sim_batch_cuda(batch_size, 100, Nk, PDr, T1r,
T2r, dfr, M0, trr, far, ti)
#ut.plot(np.absolute(batch_xs_c[0,:]),pause_close =1)
if orig_Ndiv < Nk:
batch_xs = simut.average_dict(
batch_xs_c, orig_Ndiv
) #(np.dot(np.absolute(simut.average_dict(batch_xs_c, Ndiv)), coeff))
else:
batch_xs = batch_xs_c
pca_mtx = np.dot(np.matrix(batch_xs).getH(), batch_xs)
U, s, V = scipy.sparse.linalg.svds(pca_mtx, npca)
coeff = U[:, npca - 1::-1]
sio.savemat(pathdat + 'MRF_pca_coeff.mat', {
'coeff': coeff,
'dict': batch_xs
})
for i in range(1000000):
batch_ys = np.random.uniform(0, 1, (batch_size, 4)).astype(np.float64)
#batch_ys[:,0] = np.random.uniform(0.1,0.6,(batch_size)).astype(np.float64)
#batch_ys[:,1] = np.random.uniform(0.1,0.3,(batch_size)).astype(np.float64)
batch_ys[:, 2] = np.random.uniform(0, 1.0 / tr,
(batch_size)).astype(np.float64)
#batch_ys[:,3] = np.ones(batch_size)#np.random.uniform(0.1,1.0,(batch_size)).astype(np.float64)# # np.random.uniform(0.5,1,(batch_size)).astype(np.float64)#
#batch_ys[:,2] = np.zeros(batch_size)
#batch_ys[:,2] = np.random.uniform(0.19,0.21,(batch_size)).astype(np.float64)#0.2*np.ones(batch_size)
# intial seq simulation with t1t2b0 values
#seq_data = ssad.irssfp_arrayin_data( batch_size, Nk ).set( batch_ys )
T1r, T2r, dfr, PDr = ssmrf.set_par(batch_ys)
batch_xs_c = ssmrf.bloch_sim_batch_cuda(batch_size, 100, Nk, PDr, T1r,
T2r, dfr, M0, trr, far, ti)
#ut.plot(np.absolute(batch_xs_c[0,:]),pause_close =1)
if orig_Ndiv < Nk:
batch_xs = simut.average_dict(
batch_xs_c, orig_Ndiv
) #(np.dot(np.absolute(simut.average_dict(batch_xs_c, Ndiv)), coeff))
else:
batch_xs = batch_xs_c
batch_xs = batch_xs + np.random.ranf(1)[0] * np.random.uniform(
-0.005, 0.005, (batch_xs.shape))
#batch_xs = batch_xs/np.ndarray.max(batch_xs.flatten())
if read_coeff_flag is 1:
if abs_flag:
batch_xs = np.dot(np.absolute(batch_xs), coeff)
else:
batch_xs = np.absolute(np.dot(batch_xs, coeff))
elif read_coeff_flag is 2:
batch_xs = np.absolute(np.dot(batch_xs, coeff))
else:
batch_xs = np.absolute(batch_xs)
for dd in range(batch_xs.shape[0]):
tc1 = batch_xs[dd, :] #- np.mean(imall[i,:])
normtc1 = np.linalg.norm(tc1)
if normtc1 > 0.04 and batch_ys[dd, 0] * 5000 > 3 * 500 * batch_ys[
dd, 1]:
batch_xs[dd, :] = tc1 #/normtc1
else:
batch_ys[dd, :] = np.zeros([1, npar])
batch_xs = 1000 * batch_xs
#ut.plot(np.absolute(batch_xs[0,:]),pause_close =1)
#batch_ys[:,2] = np.zeros(batch_size)
#batch_ys[:,3] = np.zeros(batch_size)
model.train(batch_xs, batch_ys)
model.test(batch_xs, batch_ys)
if i % 100 == 0:
prey = model.prediction(batch_xs, np.zeros(batch_ys.shape))
ut.plot(prey[..., 0],
batch_ys[..., 0],
line_type='.',
pause_close=1)
ut.plot(prey[..., 1],
batch_ys[..., 1],
line_type='.',
pause_close=1)
ut.plot(prey[..., 2],
batch_ys[..., 2],
line_type='.',
pause_close=1)
ut.plot(prey[..., 3],
batch_ys[..., 3],
line_type='.',
pause_close=1)
model.save(pathdat + 'test_model_savecnn')