def test1():
mnist = input_data.read_data_sets('./data/MNIST_data/', one_hot=True)
batch_size = 200
model = tf_wrap.tf_model_top([None, 784], [None, 1],\
tf_prediction_func, tf_optimize_func, tf_error_func, \
arg = batch_size, dtype = np.float32)
d_loss, g_loss, dopt, gopt = model.model_wrap.optimize
G, D1, D2 = model.model_wrap.prediction
for i in range(1200):
x, y = mnist.train.next_batch(batch_size)
z = np.random.uniform(0, 1, (batch_size, 1)).astype(np.float32)
loss_d, _ = model.sess.run([d_loss, dopt], {
model.data: x,
model.target: z
})
z = np.random.uniform(0, 1, (batch_size, 1)).astype(np.float32)
loss_g, _ = model.sess.run([g_loss, gopt], {
model.data: x,
model.target: z
})
print('{}: {}\t{}'.format(i, loss_d, loss_g))
if i % 1000 == 0:
getG = model.sess.run(G, {
model.data: x,
model.target: z
}).reshape((batch_size, 28, 28))
ut.plotim3(sum(getG, 0))
model.save('../save_data/test_model_save')
def test2():
mat_contents = sio.loadmat(pathdat+'im_pca.mat')#im.mat
I = np.array(mat_contents["I"].astype(np.float32))
nx, ny, nz, ndiv = I.shape
#print(I.shape)
imall = I.reshape([nx*ny*nz, ndiv])
npar = 7
imall = imall/np.ndarray.max(imall.flatten())
#ut.plotim3(imall.reshape(I.shape)[...,0])
#for i in range(imall.shape[0]):
# tc = imall[i,:] #- np.mean(imall[i,:])
# normtc = np.linalg.norm(tc)
# if normtc > 1e-3:
# imall[i,:] = tc/normtc
# else:
# imall[i,:] = np.zeros([1,ndiv])
#imall =imall/np.ndarray.max(imall.flatten())#0.2
#for kk in range(imall.shape[1]):
# imall [:,kk] = (imall[:,kk])/np.std(imall[:,kk])# - np.mean(imall[:,kk])
ut.plotim3(imall.reshape(I.shape)[...,0],[5, -1],pause_close = 1)
model = tf_wrap.tf_model_top([None, ndiv], [None, npar], tf_prediction_func, tf_optimize_func, tf_error_func, arg = 1.0)
model.restore(pathdat + 'test_model_save')
prey = model.prediction(imall, np.zeros([imall.shape[0],npar]))
immatch = prey.reshape([nx, ny, nz, npar])
ut.plotim3(immatch[...,0],[10, -1],bar = 1, pause_close = 5)
ut.plotim3(immatch[...,1],[10, -1],bar = 1, pause_close = 5)
ut.plotim3(immatch[...,2],[10, -1],bar = 1, pause_close = 5)
ut.plotim3(immatch[...,3],[10, -1],bar = 1, pause_close = 5)
ut.plotim3(immatch[...,4],[10, -1],bar = 1, pause_close = 5)
sio.savemat(pathdat + 'MRF_cnn_matchtt.mat', {'immatch':immatch, 'imall':imall})
def test1():
batch_size = 12
model = tf_wrap.tf_model_top((batch_size, 1), (batch_size, 1),
tf_prediction_func,
tf_optimize_func,
tf_error_func,
arg=batch_size,
dtype=np.float32)
d_loss, g_loss, dopt, gopt = model.model_wrap.optimize
G, D1, D2 = model.model_wrap.prediction
ganinst = DATA(model.data, model.target, batch_size, G, D1, D2)
for i in range(1200):
x = ganinst.data.sample(batch_size).reshape(
(batch_size, 1)).astype(np.float32)
z = ganinst.gen.sample(batch_size).reshape(
(batch_size, 1)).astype(np.float32)
loss_d, _ = model.sess.run([d_loss, dopt], {
model.data: x,
model.target: z
})
z = ganinst.gen.sample(batch_size).reshape(
(batch_size, 1)).astype(np.float32)
loss_g, _ = model.sess.run([g_loss, gopt], {
model.data: x,
model.target: z
})
print('{}: {}\t{}'.format(i, loss_d, loss_g))
ganinst._plot_distributions(model.sess)
model.save('../save_data/test_model_save')
def test2():
mat_contents = sio.loadmat(pathdat + 'im_pca_2fa_2freq.mat') #im.mat
I = np.array(mat_contents["I"].astype(np.float32))
nx, ny, nz, ndiv = I.shape
#print(I.shape)
imall = I.reshape([nx * ny * nz, ndiv])
npar = 8
imall = imall / np.ndarray.max(imall.flatten())
ut.plotim3(imall.reshape(I.shape)[..., 0], [5, -1], pause_close=1)
model = tf_wrap.tf_model_top([None, ndiv], [None, npar],
tf_prediction_func,
tf_optimize_func,
tf_error_func,
arg=1.0)
model.restore(pathdat + 'test_model_save_b1_2fa_2freq')
prey = model.prediction(imall, np.zeros([imall.shape[0], npar]))
immatch = prey.reshape([nx, ny, nz, npar])
#ut.plotim3(immatch[...,0],[10, -1],bar = 1, pause_close = 5)
#ut.plotim3(immatch[...,1],[10, -1],bar = 1, pause_close = 5)
#ut.plotim3(immatch[...,2],[10, -1],bar = 1, pause_close = 5)
#ut.plotim3(immatch[...,3],[10, -1],bar = 1, pause_close = 5)
#ut.plotim3(immatch[...,4],[10, -1],bar = 1, pause_close = 5)
sio.savemat(pathdat + 'MRF_cnn_matchtt_b1_2fa_2freq.mat', {
'immatch': immatch,
'imall': imall
})
def test2():
mnist = input_data.read_data_sets('./data/MNIST_data/', one_hot=True)
data = tf.placeholder(tf.float32, [None, 784])
target = tf.placeholder(tf.float32, [None, 10])
model = tf_wrap.tf_model_top(data, target, tf_prediction_func, tf_optimize_func, tf_error_func)
model.restore('../save_data/test_model_save')
model.test(mnist.test.images, mnist.test.labels)
def test2():
IMAGE_LIB = '../save_data/heart_data/2d_images/'
MASK_LIB = '../save_data/heart_data/2d_masks/'
IMG_HEIGHT, IMG_WIDTH = 100, 100
batch_size = 8
model = tf_wrap.tf_model_top([None, IMG_HEIGHT, IMG_WIDTH,1], [None, IMG_HEIGHT, IMG_WIDTH,1], \
tf_prediction_func_unet, tf_optimize_func, \
tf_error_func, arg = 1.0)
#x_train, x_test, y_train, y_test = get_data( IMAGE_LIB, MASK_LIB, IMG_HEIGHT, IMG_WIDTH )
if 0: #get_data is 1:# os.path.isfile('/home/pcao/git/save_data/heart_data/get_data.mat')
x_train, x_test, y_train, y_test, all_images = get_data(
IMAGE_LIB, MASK_LIB, IMG_HEIGHT, IMG_WIDTH)
sio.savemat('../save_data/heart_data/get_data.mat', \
{'x_train':x_train, 'x_test':x_test, 'y_train':y_train, 'y_test':y_test, 'all_images':all_images})
else:
mat_contents = sio.loadmat('../save_data/heart_data/get_data.mat')
x_train = np.array(mat_contents["x_train"])
y_train = np.array(mat_contents["y_train"])
x_test = np.array(mat_contents["x_test"])
y_test = np.array(mat_contents["y_test"])
model.restore('../save_data/test_Unet_on_heart_data')
model.test(x_test, y_test)
y_predict = model.prediction(x_test, np.zeros(y_test.shape))
fig, ax = plt.subplots(8, 2, figsize=(8, 4))
for i in range(8):
ax[i, 0].imshow(x_test[i, :, :, 0], cmap='gray')
ax[i, 1].imshow(y_predict[i, :, :, 0], cmap='gray')
plt.show()
sio.savemat('../save_data/heart_data/predict_data.mat', \
{'x_test':x_test, 'y_predict':y_predict, 'y_test':y_test})
def test2():
mat_contents = sio.loadmat(pathdat + 'im_pca.mat') #im.mat
I = np.array(mat_contents["I"].astype(np.float32))
nx, ny, nz, ndiv = I.shape
imall = I.reshape([nx * ny * nz, ndiv])
imall = imall / np.ndarray.max(imall.flatten())
#ut.plotim3(imall.reshape(I.shape)[...,0])
for i in range(imall.shape[0]):
tc = imall[i, :] #- np.mean(imall[i,:])
normtc = np.linalg.norm(tc)
if normtc > 5e-2:
imall[i, :] = tc / normtc
else:
imall[i, :] = np.zeros([1, ndiv])
#imall = imall/np.ndarray.max(imall.flatten())
ut.plotim3(imall.reshape(I.shape)[..., 0])
model = tf_wrap.tf_model_top([None, 13], [None, 3], tf_prediction_func,
tf_optimize_func, tf_error_func)
model.restore('../save_data/test_model_save')
prey = model.prediction(imall, np.zeros([imall.shape[0], 3]))
immatch = prey.reshape([nx, ny, nz, 3])
ut.plotim3(immatch[..., 0], bar=1)
ut.plotim3(immatch[..., 1], bar=1)
ut.plotim3(immatch[..., 2], bar=1)
sio.savemat(pathdat + 'MRF_cnn_matchtt.mat', {
'immatch': immatch,
'imall': imall
})
def test2():
mnist = input_data.read_data_sets('./data/MNIST_data/', one_hot=True)
model = tf_wrap.tf_model_top([None, 28, 28, 1], [None, 1],
tf_prediction_func, tf_optimize_func,
tf_error_func)
batch_z = np.random.uniform(0, 1, (mnist.test.images.shape[0], )).astype(
np.float32)
model.restore('../save_data/test_model_save')
model.test({'image': mnist.test.images, 'z': batch_z}, None)
def test2():
mnist = input_data.read_data_sets('./data/MNIST_data/', one_hot=True)
model = tf_wrap.tf_model_top([None, 784], [None, 784],
tf_prediction_func,
tf_optimize_func,
tf_error_func,
arg=1.0)
model.restore('../save_data/test_model_save')
model.test(mnist.test.images, mnist.test.images)
def test1():
mnist = input_data.read_data_sets('./data/MNIST_data/', one_hot=True)
model = tf_wrap.tf_model_top([None, 784], [None, 10], tf_prediction_func, tf_optimize_func, tf_error_func, arg = 0.5)
for _ in range(100):
model.test(mnist.test.images, mnist.test.labels)
for _ in range(100):
batch_xs, batch_ys = mnist.train.next_batch(1000)
model.train(batch_xs, batch_ys)
model.save('../save_data/test_model_save')
def test1():
mnist = input_data.read_data_sets('./data/MNIST_data/', one_hot=True)
data = tf.placeholder(tf.float32, [None, 784])
target = tf.placeholder(tf.float32, [None, 10])
model = tf_wrap.tf_model_top(data, target, tf_prediction_func, tf_optimize_func, tf_error_func)
for _ in range(100):
model.test(mnist.test.images, mnist.test.labels)
for _ in range(100):
batch_xs, batch_ys = mnist.train.next_batch(1000)
model.train(batch_xs, batch_ys)
model.save('../save_data/test_model_save')
def test1():
Nk = 960 #far.shape[0]
model = tf_wrap.tf_model_top([None, 2 * Nk], [None, 4], tf_prediction_func,
tf_optimize_func, tf_error_func, 0.5)
batch_size = 800
# generate far and trr
far_amp = np.random.uniform(0, 15.0 / 180.0 * np.pi, (Nk, ))
far_phase = np.random.uniform(-np.pi, np.pi, (Nk, ))
far = np.multiply(far_amp,
np.exp(far_phase)).astype(np.complex128).squeeze()
trr = np.random.uniform(3.0, 16.0, (Nk, )).astype(np.float64).squeeze()
# prepare for sequence simulation, y->x_hat
ti = 10 #ms
M0 = np.array([0.0, 0.0, 1.0]).astype(np.float64)
#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
Nite = 2000
#run for 2000
for i in range(Nite):
batch_ys = np.random.uniform(0, 1, (batch_size, 4)).astype(np.float64)
#batch_ys[:,2] = np.zeros(batch_size)
batch_ys[:, 3] = np.ones(batch_size)
batch_xs = np.zeros((batch_size, 2 * Nk), dtype=np.float64)
batch_xs_c = np.zeros((batch_size, Nk), dtype=np.complex128)
# 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[...,0:Nk] = ssmrf.bloch_sim_batch_cuda( batch_size, 100, Nk, PDr,\
T1r, T2r, dfr, M0, trr, far, ti )
#seperate real/imag parts or abs/angle parts, no noise output
batch_xs[:, 0:Nk] = np.real(batch_xs_c)
batch_xs[:, Nk:2 * Nk] = np.imag(batch_xs_c)
#input with noise
batch_xsnoise = batch_xs + np.random.uniform(-0.1, 0.1,
(batch_size, 2 * Nk))
#train_step.run(feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 0.5})
model.train(batch_xsnoise, batch_ys)
if i % 10 == 0:
model.test(batch_xsnoise, batch_ys)
if i % 1000 == 0 or i >= (Nite - 1):
model.save('../save_data/MRF_encoder_t1t2b0')
sio.savemat('../save_data/MRF_far_trr.mat', {
'far': far,
'trr': trr
})
def test1():
# need to download data from https://www.kaggle.com/kmader/siim-medical-image-analysis-tutorial
IMAGE_LIB = '../save_data/heart_data/2d_images/'
MASK_LIB = '../save_data/heart_data/2d_masks/'
IMG_HEIGHT, IMG_WIDTH = 100, 100
batch_size = 8
model = tf_wrap.tf_model_top([None, IMG_HEIGHT, IMG_WIDTH,1], [None, IMG_HEIGHT, IMG_WIDTH,1], \
tf_prediction_func_cnn, tf_optimize_func, \
tf_error_func, arg = 0.5)
if 0: #get_data is 1:# os.path.isfile('/home/pcao/git/save_data/heart_data/get_data.mat')
x_train, x_test, y_train, y_test, all_images = get_data(
IMAGE_LIB, MASK_LIB, IMG_HEIGHT, IMG_WIDTH)
sio.savemat('../save_data/heart_data/get_data.mat', \
{'x_train':x_train, 'x_test':x_test, 'y_train':y_train, 'y_test':y_test, 'all_images':all_images})
else:
mat_contents = sio.loadmat('../save_data/heart_data/get_data.mat')
x_train = np.array(mat_contents["x_train"])
y_train = np.array(mat_contents["y_train"])
x_test = np.array(mat_contents["x_test"])
y_test = np.array(mat_contents["y_test"])
print('training data size %g; testing data size %g' %
(x_train.shape[0], x_test.shape[0]))
for i in range(2000):
for _ in range(100):
image_batch, mask_batch = next(
my_generator(x_train, y_train, batch_size))
#fix, ax = plt.subplots(8,2, figsize=(batch_size,20))
#for i in range(8):
# ax[i,0].imshow(image_batch[i,:,:,0])
# ax[i,1].imshow(mask_batch[i,:,:,0])
#plt.show()
#image_batch = image_batch + np.random.ranf(1)[0]*np.random.uniform(-0.1,0.1,(image_batch.shape))
model.train(image_batch, mask_batch)
model.test(x_test, y_test)
if i % 2 == 0:
y_predict = model.prediction(x_test, np.zeros(y_test.shape))
#fig, ax = plt.subplots(8,2, figsize = (8,4))
#for i in range(8):
# ax[i,0].imshow(x_test[i,:,:,0], cmap='gray')
# ax[i,1].imshow(y_predict[i,:,:,0], cmap='gray')
#plt.show()
ut.compare_plot(x_test[..., 0],
y_predict[..., 0],
8,
pause_close=1)
model.save('../save_data/test_Unet_on_heart_data_fcn_dropout')
sio.savemat('../save_data/heart_data/predict_data_fcn_dropout.mat', \
{'x_test':x_test, 'y_predict':y_predict, 'y_test':y_test})
def test1():
model = tf_wrap.tf_model_top([None, 2*960], [None, 4], tf_prediction_func, tf_optimize_func, tf_error_func)
batch_size = 800
# load far and trr
# read rf and tr arrays from mat file
mat_contents = sio.loadmat(pathdat+'mrf_t1t2b0pd_mrf_randphasecyc_traintest.mat');
far = np.array(mat_contents["rf"].astype(np.complex128).squeeze())
trr = np.array(mat_contents["trr"].astype(np.float64).squeeze())
# prepare for sequence simulation, y->x_hat
Nk = far.shape[0]
ti = 10 #ms
M0 = np.array([0.0,0.0,1.0]).astype(np.float64)
#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
#run for 2000
for i in range(2000):
batch_ys = np.random.uniform(0,1,(batch_size,4)).astype(np.float64)
#batch_ys[:,2] = np.zeros(batch_size)
#batch_ys[:,3] = np.ones(batch_size)
batch_xs = np.zeros((batch_size,2*Nk), dtype = np.float64)
# 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 )
#seperate real/imag parts or abs/angle parts, no noise output
batch_xs[:,0:Nk] = np.real(batch_xs_c)
batch_xs[:,Nk:2*Nk] = np.imag(batch_xs_c)
#input with noise
#batch_xsnoise = batch_xs + np.random.uniform(-0.01,0.01,(batch_size,2*Nk))
#train_step.run(feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 0.5})
model.train(batch_xs, batch_ys)
if i%10 == 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.test(batch_xs, batch_ys)
if i%100 == 0:
model.save('../save_data/MRF_encoder_t1t2b0')
def test4():
# read rf and tr arrays from mat file
mat_contents = sio.loadmat('../save_data/MRF_far_trr.mat');
far = np.array(mat_contents["far"].astype(np.complex128).squeeze())
trr = np.array(mat_contents["trr"].astype(np.float64).squeeze())
Nk = far.shape[0]#960#
model = tf_wrap.tf_model_top( [None, 2 * Nk], [None, 4], tf_prediction_func, tf_optimize_func, tf_error_func, 1.0)
model.restore('../save_data/MRF_encoder_t1t2b0')
mat_contents2 = sio.loadmat(pathdat+'test_outy.mat');
data_y = mat_contents2["test_outy"]
batch_size = data_y.shape[0]
# load far and trr
# prepare for sequence simulation, y->x_hat
ti = 10 #ms
M0 = np.array([0.0,0.0,1.0]).astype(np.float64)
#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
batch_ys = data_y#np.random.uniform(0,1,(batch_size,4)).astype(np.float64)
#batch_ys[:,2] = np.zeros(batch_size)
#batch_ys[:,3] = np.ones(batch_size)
batch_xs = np.zeros((batch_size, 2 * Nk), dtype = np.float64)
batch_xs_c = np.zeros((batch_size, Nk), dtype = np.complex128)
# 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[...,0:Nk] = ssmrf.bloch_sim_batch_cuda( batch_size, 100, Nk, PDr,\
T1r, T2r, dfr, M0, trr, far, ti )
#seperate real/imag parts or abs/angle parts, no noise output
batch_xs[:,0:Nk] = np.real(batch_xs_c)
batch_xs[:,Nk:2*Nk] = np.imag(batch_xs_c)
#input with noise
#batch_xsnoise = batch_xs #+ np.random.uniform(-0.05,0.05,(batch_size,2*Nk))
prey = model.prediction(batch_xs,np.zeros(batch_ys.shape))
model.test(batch_xs, batch_ys)
ut.plot(prey[...,0], batch_ys[...,0], line_type = '.')
ut.plot(prey[...,1], batch_ys[...,1], line_type = '.')
ut.plot(prey[...,2], batch_ys[...,2], line_type = '.')
ut.plot(prey[...,3], batch_ys[...,3], line_type = '.')
sio.savemat(pathdat+'out_cnn_testdata3.mat', {'testim_outy': prey})
def test3():
# read rf and tr arrays from mat file
mat_contents = sio.loadmat('../save_data/MRF_far_trr.mat');
far = np.array(mat_contents["far"].astype(np.complex128).squeeze())
Nk = far.shape[0]#960#
model = tf_wrap.tf_model_top( [None, 2 * Nk], [None, 4], tf_prediction_func, tf_optimize_func, tf_error_func, 1.0)
model.restore('../save_data/MRF_encoder_t1t2b0')
mat_contents2 = sio.loadmat(pathdat+'datax3.mat');
data_x = mat_contents2["datax3"]
batch_size = data_x.shape[0]#800
batch_ys = np.zeros((batch_size,4), dtype = np.float64)
prey = model.prediction(data_x,np.zeros(batch_ys.shape))
sio.savemat(pathdat+'out_cnn_testdata3.mat', {'testim_outy': prey})
def test2():
mat_contents = sio.loadmat(pathdat + 'im_pca.mat') #im.mat
I = np.array(mat_contents["I"])[:, :, 25:45, :]
if len(I.shape) == 3:
nx, ny, ndiv = I.shape
nz = 1
elif len(I.shape) == 4:
nx, ny, nz, ndiv = I.shape
imall = I.reshape([nx * ny * nz, ndiv])
npar = 4
abs_flag = 0 #apply pca on absolute time course, else compute absolute value after pca
if abs_flag is 0:
imall = np.absolute(imall).astype(np.float32)
else:
imall = imall.astype(np.float32)
#imall = imall/np.ndarray.max(imall.flatten())
#ut.plotim3(imall.reshape(I.shape)[...,0])
#for i in range(imall.shape[0]):
# tc = imall[i,:] #- np.mean(imall[i,:])
# normtc = np.linalg.norm(tc)
# if normtc > 1e-3:
# imall[i,:] = tc/normtc
# else:
# imall[i,:] = np.zeros([1,ndiv])
imall = 1000.0 * imall / np.ndarray.max(imall.flatten()) #0.2
ut.plotim3(imall.reshape(I.shape)[..., 0], [10, 6], pause_close=1)
model = tf_wrap.tf_model_top([None, ndiv], [None, npar],
tf_prediction_func, tf_optimize_func,
tf_error_func)
model.restore(pathdat + 'test_model_savecnn')
prey = model.prediction(imall, np.zeros([imall.shape[0], npar]))
immatch = prey.reshape([nx, ny, nz, npar])
ut.plotim3(immatch[..., 0], [10, 6], bar=1, pause_close=5)
ut.plotim3(immatch[..., 1], [10, 6], bar=1, pause_close=5)
ut.plotim3(immatch[..., 2], [10, 6], bar=1, pause_close=5)
ut.plotim3(immatch[..., 3], [10, 6], bar=1, pause_close=5)
sio.savemat(pathdat + 'MRF_cnn_matchttt.mat', {
'immatch': immatch,
'imall': imall
})
def test3():
mat_contents = sio.loadmat(pathdat + 'dict_pca.mat')
dictall = np.array(mat_contents["avedictall"].astype(np.float32))
label = np.array(mat_contents["dict_label"].astype(np.float32))
#dictall = dictall/np.ndarray.max(dictall.flatten())
for i in range(dictall.shape[0]):
tc = dictall[i, :] - np.mean(dictall[i, :])
dictall[i, :] = tc / np.linalg.norm(tc)
dictall = 1000 * dictall / np.ndarray.max(dictall.flatten())
model = tf_wrap.tf_model_top([None, 13], [None, 3], tf_prediction_func,
tf_optimize_func, tf_error_func)
model.restore('../save_data/test_model_save')
model.test(dictall, label)
prey = model.prediction(dictall, np.zeros(label.shape))
ut.plot(prey[..., 0], label[..., 0], line_type='.')
ut.plot(prey[..., 1], label[..., 1], line_type='.')
ut.plot(prey[..., 2], label[..., 2], line_type='.')
def test1():
mnist = input_data.read_data_sets('./data/MNIST_data/', one_hot=True)
model = tf_wrap.tf_model_top([None, 28, 28, 1], [None, 1],
tf_prediction_func, tf_optimize_func,
tf_error_func)
for _ in range(100):
model.test(mnist.test.images, mnist.test.images)
for _ in range(100):
batch_size = 1000
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
batch_z = np.random.uniform(0, 1,
(batch_size, )).astype(np.float32)
model.set_arg('train_D').train({
'image': batch_image,
'z': batch_z
}, None)
model.set_arg('train_G').train({
'image': batch_image,
'z': batch_z
}, None)
model.save('../save_data/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))
batch_size = 2000
#dictall = dictall/np.ndarray.max(dictall.flatten())
for i in range(dictall.shape[0]):
tc = dictall[i, :] #- np.mean(dictall[i,:])
dictall[i, :] = tc / np.linalg.norm(tc)
#dictall = dictall/np.ndarray.max(dictall.flatten())
#data = tf.placeholder(tf.float32, [None, 13])
#target = tf.placeholder(tf.float32, [None, 3])
model = tf_wrap.tf_model_top([None, 13], [None, 3], tf_prediction_func,
tf_optimize_func, tf_error_func)
for i in range(1000000):
batch_start = (i * batch_size) % dictall.shape[0]
batch_stop = batch_start + batch_size
batch_xs = dictall[batch_start:batch_stop, :]
batch_ys = label[batch_start:batch_stop, :]
#batch_ys[:,2] = np.random.ranf(1)[0]
batch_xs = batch_xs * np.random.ranf(1)[0] # batch_ys[0,2] #*
#batch_xsnoise = batch_xs + np.random.ranf(1)[0]*np.random.uniform(-0.05,0.05,(batch_xs.shape))
#ut.plot(batch_xs[0,:].squeeze())
model.train(batch_xs, batch_ys)
model.test(batch_xs, batch_ys)
#if i % 1000 == 0:
#prey = model.prediction(batch_xs,np.zeros(batch_ys.shape))
#ut.plot(prey[...,0], batch_ys[...,0], line_type = '.')
#ut.plot(prey[...,1], batch_ys[...,1], line_type = '.')
#ut.plot(prey[...,2], batch_ys[...,2], line_type = '.')
if i % 1000 == 0:
model.save('../save_data/test_model_save')
def test1():
mat_contents = sio.loadmat(pathdat+'mrf_t1t2b0pd_mrf_randphasecyc_traintest.mat');
far = np.array(mat_contents["rf"].astype(np.complex128).squeeze())
trr = np.array(mat_contents["trr"].astype(np.float64).squeeze())
Nk = far.shape[0]#960#
model = tf_wrap.tf_model_top( [None, 2 * Nk], [None, 4], tf_prediction_func, tf_optimize_func, tf_error_func, 0.5)
batch_size = 800
# generate far and trr
#far_amp = np.random.uniform(0, 15.0/180.0 * np.pi, (Nk,))
#far_phase = np.random.uniform(-np.pi, np.pi, (Nk,))
#far = np.multiply(far_amp, np.exp(far_phase)).astype(np.complex128).squeeze()
#trr = np.random.uniform(3.0, 16.0, (Nk,)).astype(np.float64).squeeze()
#far, trr = simut.rftr_const(Nk, 15.0, 4.0)
#far,trr = simut.rftr_rand(Nk, fa, 3, 16)
# prepare for sequence simulation, y->x_hat
ti = 10 #ms
M0 = np.array([0.0,0.0,1.0]).astype(np.float64)
#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
Nite = 200000
#run for 2000
for i in range(Nite):
batch_ys = np.random.uniform(0,1,(batch_size,4)).astype(np.float64)
#batch_ys = np.random.randint(1,10,(batch_size,4)).astype(np.float64)/10.0
#batch_ys[:,2] = np.random.uniform(0,0.2,(batch_size)).astype(np.float64)#np.zeros(batch_size)
#batch_ys[:,3] = np.ones(batch_size)
batch_xs = np.zeros((batch_size,2 * Nk), dtype = np.float64)
batch_xs_c = np.zeros((batch_size, Nk), dtype = np.complex128)
# 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[...,0:Nk] = ssmrf.bloch_sim_batch_cuda( batch_size, 100, Nk, PDr,\
T1r, T2r, dfr, M0, trr, far, ti )
#seperate real/imag parts or abs/angle parts, no noise output
batch_xs[:,0:Nk] = np.real(batch_xs_c)
batch_xs[:,Nk:2*Nk] = np.imag(batch_xs_c)
#input with noise
batch_xsnoise = batch_xs + np.random.ranf(1)[0]*np.random.uniform(-0.4,0.4,(batch_size,2*Nk))
model.train(batch_xsnoise, batch_ys)
if i%10 == 0:
model.test(batch_xsnoise, batch_ys)
if i%100 == 0 or i >= (Nite - 1):
model.save('../save_data/MRF_encoder_t1t2b0')
sio.savemat('../save_data/MRF_far_trr.mat', {'far':far, 'trr':trr})
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)
def test2():
# read rf and tr arrays from mat file
mat_contents = sio.loadmat(pathdat+'mrf_t1t2b0pd_mrf_randphasecyc_traintest.mat');
far = np.array(mat_contents["rf"].astype(np.complex128).squeeze())
trr = np.array(mat_contents["trr"].astype(np.float64).squeeze())
# input MRF time courses
mat_contents2 = sio.loadmat(pathdat+'datax1.mat');
data_x = np.array(mat_contents2["datax1"]).astype(np.float64)
# prepare for sequence simulation, y->x_hat
Nk = far.shape[0]
Nexample = data_x.shape[0]
ti = 10 #ms
M0 = np.array([0.0,0.0,1.0]).astype(np.float64)
#image size
nx = 217
ny = 181
# mask in ksp
mask = ut.mask3d( nx, ny, Nk, [15,15,0], 0.4)
#FTm = opts.FFT2d_kmask(mask)
FTm = opts.FFT2d_kmask(mask)
#intial timing
timing = utc.timing()
# x real and imag parts should be combined
data_x_c = np.zeros((Nexample, Nk),np.complex128)
data_x_c = ssmrf.seqdata_realimag_2complex(data_x)
#could do mask M here, data_x_c = M*data_x_c
# intial tmp data
test_outy0 = np.zeros((Nexample,4),dtype=np.float64)
test_outy = np.zeros((Nexample,4),dtype=np.float64)
tmptest_outy = np.zeros((Nexample,4),dtype=np.float64)
acctest_outy = np.zeros((Nexample,4),dtype=np.float64)
# f'(y), cnn model
timing.start()
#ssmrf.batch_apply_tf_cuda( Nexample, ny, sess.run, x, data_x, y_conv, test_outy0, keep_prob )
model = tf_wrap.tf_model_top([None, 2*960], [None, 4], tf_prediction_func, tf_optimize_func, tf_error_func)
mat_contents3 = sio.loadmat(pathdat+'test_outy.mat');
test_outy = np.array(mat_contents3["test_outy"]).astype(np.float64)
T1r, T2r, dfr, PDr = ssmrf.set_par((test_outy) )
data_x_c = ssmrf.bloch_sim_batch_cuda( Nexample, 7*ny, Nk, PDr, T1r, T2r, dfr, M0, trr, far, ti )
data_x_c = mask_ksp3d(nx, ny, Nk, FTm, data_x_c)
data_x = ssmrf.seqdata_complex_2realimag(data_x_c)
data_x0 = data_x
model.restore('../save_data/MRF_encoder_t1t2b0')
test_outy0 = model.prediction(data_x, np.zeros(test_outy0.shape))
timing.stop().display('model first estimation ')
test_outy0 = constraints( test_outy0 )
acctest_outy = test_outy0
# for loop start here
for _ in range(40):
#cuda simulate bloch for each pixel
T1r, T2r, dfr, PDr = ssmrf.set_par((test_outy) )
timing.start()
data_x_c = ssmrf.bloch_sim_batch_cuda( Nexample, 7*ny, Nk, PDr, T1r, T2r, dfr, M0, trr, far, ti )
timing.stop().display('Bloch sim in loop ')
#0.5* d||M*f(p)-b||_2^2/dp = f'*(M*f(p)-b) = f'(M*f(p)) - f'(b)
# could do mask, M, here, e.g. data_x_c=M*data_x_c
data_x_c = mask_ksp3d(nx, ny, Nk, FTm, data_x_c)
#seperate real/imag parts or abs/angle parts
data_x = ssmrf.seqdata_complex_2realimag(data_x_c)
# apply CNN model, f'(i)
#ssmrf.batch_apply_tf_cuda( Nexample, ny, sess.run, x, data_x, y_conv, tmptest_outy, keep_prob )
tmptest_outy = model.prediction(data_x, np.zeros(tmptest_outy.shape))
tmptest_outy = constraints( tmptest_outy )
# gradient = f'(M * f(p)) - f'(b)
tmptest_outy = tmptest_outy - test_outy0 #+ 0.2*test_outy
# gradient descent for test_outy
acctest_outy = acctest_outy - tmptest_outy
print('gradient 0.5* d||f(p)-b||_2^2/dp: %g' % np.linalg.norm(tmptest_outy))
#print('test_outy: %g' % np.linalg.norm(test_outy))
acctest_outy = constraints( acctest_outy )
#could do soft thresholding on test_outy here, i.e. test_outy = threshold(test_outy)
#test_outy = l0_par(nx, ny, acctest_outy) #
test_outy = tv_par(nx, ny, acctest_outy)
sio.savemat(pathdat +'cnn_cs_testouty.mat', {'test_outy': test_outy})
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))
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')
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')
