Design and Purchase Intention Analysis of Cultural and Creative Goods Based on Deep Learning Neural Networks

Table 3

rDNN model training process.

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Model.compile (loss = “mean_squared_error,” optimizer = “sgd,” metrics = [“accuracy”]

Early_stopping = EarlyStopping (monitor = “val_loss,” patience = 50)

History = model.fit (data, label, batch_size = 100, nb_epoch = 200, shuffle = True, callbacks = [early_stopping])

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