# 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,