_kkeras_r0.py 文件源码

def modeling(self, l = [49, 30, 10, 3]):
        """
        generate model
        """
        self.c_name = 'conv'

        n_cv_flt, n_cv_ln = self.n_cv_flt, self.n_cv_ln
        cv_activation = self.cv_activation

        model = Sequential()

        # Direct: input_shape should be (l,0) not (l)
        # if l, it assume a scalar for an input feature.
        #model.add(Dense( l[1], input_shape=(l[0],))) 

        # Convolution
        print( "n_cv_flt, n_cv_ln, cv_activation", n_cv_flt, n_cv_ln, cv_activation)
        #model.add(Convolution1D( n_cv_flt, n_cv_ln, activation=cv_activation, 
        #   border_mode='same', input_shape=(1, l[0]), name = 'conv'))
        model.add(Convolution1D( n_cv_flt, n_cv_ln, activation=cv_activation, 
            border_mode='same', input_shape=(l[0],1), name = self.c_name))
        model.add(Flatten())
        model.add(Dense( l[1]))

        model.add(Activation('relu'))
        model.add(Dropout(0.2))
        model.add(Dense( l[2]))
        model.add(Activation('relu'))
        model.add(Dropout(0.2))
        model.add(Dense( l[3]))
        model.add(Activation('softmax'))

        self.layer_dict = dict([(layer.name, layer) for layer in model.layers])

        return model