new_sampling_rate=sampling_rate_test, \
new_BATCH_SIZE=BATCH_SIZE, new_sigma_w=sigma_w, new_n=n, new_m=m, new_training=False, use_adaptive_weights=DenoiserbyDenoiser)
LDAMP.ListNetworkParameters()
# tf Graph input
x_true = tf.placeholder(tf.float32, [n, BATCH_SIZE])
#Create handles for the measurement operator
[A_handle, At_handle, A_val,
A_val_tf] = LDAMP.GenerateMeasurementOperators(measurement_mode)
## Initialize the variable theta which stores the weights and biases
if tie_weights == True:
theta = [None]
with tf.variable_scope("Iter" + str(0)):
theta_thisIter = LDAMP.init_vars_DnCNN(init_mu, init_sigma)
theta[0] = theta_thisIter
elif DenoiserbyDenoiser:
noise_min_stds = [
0, 10, 20, 40, 60, 80, 100, 150, 300
] #This is currently hardcoded within LearnedDAMP_functionhelper
noise_max_stds = [
10, 20, 40, 60, 80, 100, 150, 300, 500
] # This is currently hardcoded within LearnedDAMP_functionhelper
theta = [None] * len(noise_min_stds)
for noise_level in range(len(noise_min_stds)):
with tf.variable_scope("Adaptive_NL" + str(noise_level)):
theta[noise_level] = LDAMP.init_vars_DnCNN(init_mu, init_sigma)
else:
n_layers_trained = n_DAMP_layers
theta = [None] * n_layers_trained
new_filter_height=filter_height, new_filter_width=filter_width, new_num_filters=num_filters, \
new_n_DnCNN_layers=n_DnCNN_layers, new_n_DAMP_layers=None,
new_sampling_rate=None, \
new_BATCH_SIZE=BATCH_SIZE, new_sigma_w=None, new_n=n, new_m=None, new_training=True)
LDAMP.ListNetworkParameters()
# tf Graph input
training_tf = tf.placeholder(tf.bool, name='training')
sigma_w_tf = tf.placeholder(tf.float32)
x_true = tf.placeholder(tf.float32, [n, BATCH_SIZE])
## Construct the measurement model and handles/placeholders
y_measured = LDAMP.AddNoise(x_true, sigma_w_tf)
## Initialize the variable theta which stores the weights and biases
theta_dncnn = LDAMP.init_vars_DnCNN(init_mu, init_sigma)
## Construct the reconstruction model
#x_hat = LDAMP.DnCNN(y_measured,None,theta_dncnn,training=training_tf)
[x_hat, div_overN] = LDAMP.DnCNN_wrapper(y_measured,
None,
theta_dncnn,
training=training_tf)
## Define loss and optimizer
nfp = np.float32(height_img * width_img)
if useSURE:
cost = LDAMP.MCSURE_loss(x_hat, div_overN, y_measured, sigma_w_tf)
else:
cost = tf.nn.l2_loss(x_true - x_hat) * 1. / nfp
new_BATCH_SIZE=BATCH_SIZE, new_sigma_w=sigma_w, new_n=n, new_m=m, new_training=True)
LDAMP.ListNetworkParameters()
# tf Graph input
training_tf = tf.placeholder(tf.bool, name='training')
x_true = tf.placeholder(tf.float32, [n, BATCH_SIZE])
## Initialize the variable theta which stores the weights and biases
if tie_weights == True:
n_layers_trained = 1
else:
n_layers_trained = n_DAMP_layers
theta = [None] * n_layers_trained
for iter in range(n_layers_trained):
with tf.variable_scope("Iter" + str(iter)):
theta_thisIter = LDAMP.init_vars_DnCNN(init_mu, init_sigma)
theta[iter] = theta_thisIter
## Construct the measurement model and handles/placeholders
[A_handle, At_handle, A_val, A_val_tf] = LDAMP.GenerateMeasurementOperators(measurement_mode)
y_measured = LDAMP.GenerateNoisyCSData_handles(x_true, A_handle, sigma_w, A_val_tf)
## Construct the reconstruction model
if alg=='DAMP':
(x_hat, MSE_history, NMSE_history, PSNR_history, r_final, rvar_final, div_overN) = LDAMP.LDAMP(y_measured,A_handle,At_handle,A_val_tf,theta,x_true,tie=tie_weights,training=training_tf,LayerbyLayer=LayerbyLayer)
elif alg=='DIT':
(x_hat, MSE_history, NMSE_history, PSNR_history) = LDAMP.LDIT(y_measured,A_handle,At_handle,A_val_tf,theta,x_true,tie=tie_weights,training=training_tf,LayerbyLayer=LayerbyLayer)
else:
raise ValueError('alg was not a supported option')
## Define loss and determine which variables to train