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@@ -37,7 +37,7 @@ class TSHandler(object):
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self.logger.info("加载模型权重失败:{}".format(e.args))
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@staticmethod
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- def get_keras_model(opt, time_series=1, lstm_type=1):
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+ def get_keras_model_20250514(opt, time_series=1, lstm_type=1):
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loss = region_loss(opt)
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l1_reg = regularizers.l1(opt.Model['lambda_value_1'])
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l2_reg = regularizers.l2(opt.Model['lambda_value_2'])
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@@ -56,6 +56,28 @@ class TSHandler(object):
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model.compile(loss=loss, optimizer=adam)
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return model
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+ @staticmethod
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+ def get_keras_model(opt, time_series=1, lstm_type=1):
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+ loss = region_loss(opt)
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+ l1_reg = regularizers.l1(opt.Model['lambda_value_1'])
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+ l2_reg = regularizers.l2(opt.Model['lambda_value_2'])
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+ nwp_input = Input(shape=(opt.Model['time_step'] * time_series, opt.Model['input_size']), name='nwp')
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+
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+ con1 = Conv1D(filters=64, kernel_size=1, strides=1, padding='valid', activation='relu',
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+ kernel_regularizer=l2_reg)(nwp_input)
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+ con1_p = MaxPooling1D(pool_size=1, strides=1, padding='valid', data_format='channels_last')(con1)
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+ nwp_lstm = LSTM(units=opt.Model['hidden_size'], return_sequences=False, kernel_regularizer=l2_reg)(con1_p)
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+ if lstm_type == 2:
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+ output = Dense(opt.Model['time_step'], name='cdq_output')(nwp_lstm)
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+ else:
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+ output = Dense(opt.Model['time_step'] * time_series, name='cdq_output')(nwp_lstm)
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+
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+ model = Model(nwp_input, output)
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+ adam = optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-7, amsgrad=True)
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+ model.compile(loss=loss, optimizer=adam)
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+
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+ return model
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+
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def train_init(self):
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try:
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# 进行加强训练，支持修模
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David