'u_vocab_file': path + 'u.txt',
'v_vocab_file': path + 'v.txt',
't_vocab_file': path + 't.txt',
'train_data_file': path + 'train.txt',
'test_data_file': path + 'test.txt',
'coor_nor_file': path + 'coor_nor.txt',
'distance_file': path + 'distance.txt',
'train_log_file': path + 'log.txt',
'candidate_file': path + 'candidate.pk',
'id_offset': 1,
'n_epoch': 80,
'batch_size': 50,
'data_worker': 1,
'load_model': False,
'emb_dim_d': 16, # for distance embedding #best 16
'emb_dim_v': 16, #origin 32 best 16
'emb_dim_t': 8, #origin 8
'emb_dim_u': 32, # !!!jiayi copy from v3 origin 32 best 32
'hidden_dim': 16, #origin 16
'nb_cnt': 16,
'save_gap': 10,
'dropout': 0.5,
'epoch': 80
}
dataset = DataSet(opt)
manager = ModelManager(opt)
model_type = 'birnnt'
manager.build_model(model_type, dataset)
print "evaluate"
manager.evaluate(model_type, dataset)
# Go to real space
data = np.fft.fftshift(np.fft.irfftn(data_ft))
# Load the NN model
config = tf.ConfigProto(gpu_options=tf.GPUOptions(
per_process_gpu_memory_fraction=0.3))
net = ModelManager(config)
net.load(nn_model_fn)
nets = net.get_input_size()
# Reshape the array to match the input shape
data = np.resize(data, (1, nets[0], nets[1], nets[2], 1))
# Run the network and unload the NN model
denoised = net.evaluate(data)[0, ..., 0]
net.end()
# Back to Fourier space
denoised_ft = np.fft.rfftn(np.fft.fftshift(denoised))
# Apply confidence weighting
denoised_ft *= confidence
# We define a lower regime for tau2 where the
# numerical accuracy in the division becomes an issue
tau2lim = 1e-8
imix = tau2 >= tau2lim
# Define the results container
result_ft = np.zeros(data_ft.shape, dtype=np.complex)
