Preprocessing.GetTargetData(num_of_cells, num_of_CUEs, i,
(2000, 8000, 10000)) for i in num_of_D2Ds
]
# Reshape the input data
for index, input_data in enumerate(input_data_list):
rows, cols, channels = Preprocessing.GetInputShape(input_data)
input_data_list[index] = Preprocessing.ReshapeInputData3D(
input_data, image_data_format, rows, cols * channels, 1)
# Get the maximum length of the target data in the target data list
max_length = Preprocessing.GetMaxLength(target_data_list)
# Zero padding
for index, target_data in enumerate(target_data_list):
target_data_list[index] = Preprocessing.ZeroPadding(
target_data, max_length)
# Split the datadset into the training set and testing set
x_train_list, y_train_list, x_test_list, y_test_list = [[None] *
len(input_data_list)
for _ in range(4)]
for index, (input_data,
target_data) in enumerate(zip(input_data_list, target_data_list)):
(x_train_list[index],
y_train_list[index]), (x_test_list[index],
y_test_list[index]) = Preprocessing.SplitDataset(
input_data,
target_data,
proportion=0.8,
shuffle=False)
