didineuralmodel.py 文件源码

def get_input(self):
        # Input data.
        # Load the training, validation and test data into constants that are
        # attached to the graph.
        self.x_train, self.y_train,self.x_validation,self.y_validation = self.get_train_validationset()
        self.x_train, self.y_train,self.x_validation,self.y_validation = self.x_train.as_matrix(), self.y_train.as_matrix().reshape((-1,1)),\
                                                                         self.x_validation.as_matrix(),self.y_validation.as_matrix().reshape((-1,1))
#         self.x_train, self.y_train,self.x_validation,self.y_validation = self.x_train.astype(np.float32), self.y_train.astype(np.float32),\
#                                                                          self.x_validation.astype(np.float32),self.y_validation.astype(np.float32)
        sc = MinMaxScaler()
        sc.fit(self.x_train)
        self.x_train= sc.transform(self.x_train)
        self.x_validation= sc.transform(self.x_validation)

        self.inputlayer_num = len(self.get_used_features())
        self.outputlayer_num = 1

        # Input placehoolders
        with tf.name_scope('input'):
            self.x = tf.placeholder(tf.float32, [None, self.inputlayer_num], name='x-input')
            self.y_true = tf.placeholder(tf.float32, [None, self.outputlayer_num ], name='y-input')
        self.keep_prob = tf.placeholder(tf.float32, name='drop_out')

        return