import matplotlib.pyplot as plt # Parameters setting num_of_cells = 2 num_of_CUEs = 2 num_of_D2Ds = 2 batch_size = 64 epochs = 8 # Get the image data format which Keras follows image_data_format = Preprocessing.GetImageDataFormat() # 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
num_of_CUEs = 2 num_of_D2Ds = (2, 3) batch_size = 64 epochs = 10 # Get the image data format which Keras follows image_data_format = Preprocessing.GetImageDataFormat() # Get the input data and target data input_data_list = [ Preprocessing.GetInputData(num_of_cells, num_of_CUEs, i, (2000, 8000, 10000), image_data_format) for i in num_of_D2Ds ] target_data_list = [ 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)