python - 构建后 session 图为空

Question

我尝试构建一个图表然后运行它，但我仍然得到

RuntimeError: The Session graph is empty. Add operations to the graph before calling run().

该图内置的函数是

def init_network(self):
    self.graph = tf.Graph()
    with self.graph.as_default():
        self.lstm = tf.contrib.rnn.LSTMCell(self.state_variables)
        self.state = self.lstm.zero_state(1, dtype=tf.float32)
        self.weights = tf.get_variable("Weights",
                                       shape=[self.state_variables, 1],
                                       dtype=tf.float32)

        self.lstm_output_ph = tf.placeholder(shape=[1, self.state_variables], dtype=tf.float32)
        inner_product = tf.linalg.matmul(self.lstm_output_ph, self.weights)
        q_estimate = tf.nn.softmax(inner_product)
        self.reward_ph = tf.placeholder(shape=[1], dtype=tf.float32)
        self.previous_q_ph = tf.placeholder(shape=[1], dtype=tf.float32)
        loss = tf.subtract(tf.add(self.reward_ph, tf.multiply(self.memory, self.previous_q_ph)), q_estimate)

        optimizer = tf.train.GradientDescentOptimizer(
            self.learning_rate,
            use_locking=False,
            name='SGD'
        )

        self.train_step = optimizer.minimize(loss)

        self.state_ph = tf.placeholder(shape=[1, self.state_variables], dtype=tf.float32)
        self.last_output, self.state = self.lstm(self.state_ph, self.state)
        inner_product_predict = tf.linalg.matmul(self.last_output, self.weights)
        q_estimate_init = tf.nn.softmax(inner_product_predict)
        self.predict_step = q_estimate_init

然后我尝试使用此图运行 session ，如下所示:

with tf.Session(graph=self.graph) as sess:
    sess.run(self.train_step,
             feed_dict={
                 self.lstm_output_ph: self.last_output,
                 self.reward_ph: reward,
                 self.previous_q_ph: previous_q
             })

知道为什么我的图表仍然是空的吗？调试 init_network 方法显示，即使在该方法结束时，self.graph 变量仍然为空(其 _graph_key 变量仍然设置为'grap-key-0/')。

Answer 1

这不是一个实际的答案(如果有人发布更好的答案，我会删除它，或者如果我找到实际的解释，我会改进它)。我通过以下方式更改图形定义成功解决了这个问题:

def build_graph(self, init_state):
    self.graph = tf.Graph()
    with self.graph.as_default():
        self.lstm = tf.keras.layers.CuDNNLSTM(units=4, stateful=True)
        self.state = self.lstm.get_initial_state(inputs=init_state)
        self.weights = tf.get_variable("Weights",
                                       shape=[self.state_variables, 1],
                                       dtype=tf.float32,
                                       collections=[tf.GraphKeys.GLOBAL_VARIABLES,
                                                    tf.GraphKeys.TRAINABLE_VARIABLES])

        self.input_ph = tf.placeholder(
            name="input",
            shape=[1, 1, self.state_variables],
            dtype=tf.float32)

        self.output_step = self.lstm(self.input_ph)

        self.lstm_output_ph = tf.placeholder(
            name="lstm_output",
            shape=[1, self.state_variables],
            dtype=tf.float32)

        self.predict_step = tf.linalg.matmul(self.lstm_output_ph, tf.nn.softmax(self.weights))

        self.reward_ph = tf.placeholder(
            name="reward",
            dtype=tf.float32)

        self.previous_q_ph = tf.placeholder(
            name="previous_q",
            dtype=tf.float32)

        self.loss = tf.losses.mean_squared_error(
            labels=self.reward_ph + self.memory * self.previous_q_ph,
            predictions=self.predict_step
        )

        optimizer = tf.train.GradientDescentOptimizer(
            learning_rate=self.learning_rate,
            use_locking=False,
            name='SGD'
        )

        self.train_step = optimizer.minimize(
            self.loss,
            var_list=[self.weights],
            gate_gradients=optimizer.GATE_OP,
            aggregation_method=tf.AggregationMethod.DEFAULT,
            colocate_gradients_with_ops=False,
            name='GD_optimizer'
        )

        self.var_init = tf.global_variables_initializer()

但是，我不知道确切的问题是什么。