# Get the input data and target data
input_data = Preprocessing.GetInputData(num_of_cells, num_of_CUEs, num_of_D2Ds,
(2000, 8000, 10000), image_data_format)
target_data = Preprocessing.GetTargetData(num_of_cells, num_of_CUEs,
num_of_D2Ds, (2000, 8000, 10000))
# Reshape the input data
rows, cols, channels = Preprocessing.GetInputShape(input_data)
reshaped_input_data = Preprocessing.ReshapeInputData3D(input_data,
image_data_format, rows,
cols * channels, 1)
# Split the datadset into the training set and testing set
(x_train, y_train), (x_test,
y_test) = Preprocessing.SplitDataset(reshaped_input_data,
target_data,
proportion=0.8,
shuffle=False)
# Get the input shape of input data and the output shape of target data
input_shape = Preprocessing.GetInputShape(x_train)
target_shape = Preprocessing.GetTargetShape(y_train)
# Build the model
model = Sequential()
model.add(
Conv2D(filters=16,
kernel_size=(2, 2),
data_format=image_data_format,
activation='relu',
input_shape=input_shape))
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)
# Build the model
model = Sequential()
model.add(
Conv2D(filters=16,
kernel_size=(2, 2),
data_format=image_data_format,
activation='relu',
input_shape=(None, None, 1)))
model.add(
Conv2D(filters=16,