keras_utils.py 文件源码

def fit(self, X, y):
        ## scaler
        self.scaler = StandardScaler()
        X = self.scaler.fit_transform(X)

        #### build model
        self.model = Sequential()
        ## input layer
        self.model.add(Dropout(self.input_dropout, input_shape=(X.shape[1],)))
        ## hidden layers
        first = True
        hidden_layers = self.hidden_layers
        while hidden_layers > 0:
            self.model.add(Dense(self.hidden_units))
            if self.batch_norm == "before_act":
                self.model.add(BatchNormalization())
            if self.hidden_activation == "prelu":
                self.model.add(PReLU())
            elif self.hidden_activation == "elu":
                self.model.add(ELU())
            else:
                self.model.add(Activation(self.hidden_activation))
            if self.batch_norm == "after_act":
                self.model.add(BatchNormalization())
            self.model.add(Dropout(self.hidden_dropout))
            hidden_layers -= 1

        ## output layer
        output_dim = 1
        output_act = "linear"
        self.model.add(Dense(output_dim))
        self.model.add(Activation(output_act))

        ## loss
        if self.optimizer == "sgd":
            sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
            self.model.compile(loss="mse", optimizer=sgd)
        else:
            self.model.compile(loss="mse", optimizer=self.optimizer)

        ## fit
        self.model.fit(X, y,
                    nb_epoch=self.nb_epoch, 
                    batch_size=self.batch_size,
                    validation_split=0, verbose=0)
        return self