Exemplo n.º 1
0
# 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))
Exemplo n.º 2
0
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,