# 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))
model.add(
Conv2D(filters=16,
kernel_size=(2, 2),
data_format=image_data_format,
