createDummyData.py 文件源码

def __init__(self, **kwargs):
        """
        :param **kwargs: output_dim=4: output dimension of LSTM layer;
         activation_lstm='tanh': activation function for LSTM layers;
         activation_dense='relu': activation function for Dense layer;
         activation_last='sigmoid': activation function for last layer;
         drop_out=0.2: fraction of input units to drop;
         np_epoch=10, the number of epoches to train the model. epoch is one forward pass and one backward pass of all the training examples;
         batch_size=32: number of samples per gradient update. The higher the batch size, the more memory space you'll need;
         loss='mean_square_error': loss function;
         optimizer='rmsprop'
        """
        self.output_dim = kwargs.get('output_dim', 8)
        self.activation_lstm = kwargs.get('activation_lstm', 'relu')
        self.activation_dense = kwargs.get('activation_dense', 'relu')
        self.activation_last = kwargs.get('activation_last', 'softmax')    # softmax for multiple output
        self.dense_layer = kwargs.get('dense_layer', 2)     # at least 2 layers
        self.lstm_layer = kwargs.get('lstm_layer', 2)
        self.drop_out = kwargs.get('drop_out', 0.2)
        self.nb_epoch = kwargs.get('nb_epoch', 10)
        self.batch_size = kwargs.get('batch_size', 100)
        self.loss = kwargs.get('loss', 'categorical_crossentropy')
        self.optimizer = kwargs.get('optimizer', 'rmsprop')