Question

我正在尝试从google colab中保存我的tensorflow模型，但这给了我一个错误，我不知道为什么它给出了与'无法序列化tensorflow.GraphDef类型的协议缓冲区作为序列化大小的错误有关的错误（ 2897149641字节）将大于限制（2147483647字节）' 我在附上我用过的代码另外，在下面我附上了弹出的错误

x = tf.placeholder(tf.float32, shape = [None, 4])
y_true = tf.placeholder(tf.float32, shape = [None, 4])

hidden_layer_1 = tf.layers.dense(x, 100, activation = tf.nn.relu)
hidden_layer_2 = tf.layers.dense(hidden_layer_1, 100, activation = tf.nn.relu)
output = tf.layers.dense(hidden_layer_2, 4, activation = tf.nn.sigmoid)

cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true,logits=output))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
train = optimizer.minimize(cost_func)

init = tf.global_variables_initializer()
saver = tf.train.Saver()

steps = 100
cost_train = []
cost_test = []
accu_train = []
accu_test = []
with tf.Session() as sess:

    sess.run(init)

    for i in range(steps):


        _, c_train, pred_train = sess.run([train, cost_func, output],feed_dict={x:X_train,y_true:y_train})
        _, c_test, pred_test = sess.run([train, cost_func, output],feed_dict={x:X_test,y_true:y_test})

        matches_train = tf.equal(tf.argmax(pred_train,1),tf.argmax(y_train,1))
        matches_test = tf.equal(tf.argmax(pred_test,1),tf.argmax(y_test,1))

        acc_train = tf.reduce_mean(tf.cast(matches_train,tf.float32))
        acc_test = tf.reduce_mean(tf.cast(matches_test,tf.float32))

        a_train = sess.run(acc_train,feed_dict={x:X_train,y_true:y_train,})
        a_test = sess.run(acc_test,feed_dict={x:X_test,y_true:y_test,})

        cost_train.append(c_train)
        cost_test.append(c_test)

        accu_train.append(a_train)
        accu_test.append(a_test)

        print('Currently on step {}'.format(i))
        print('TRAIN ERROR =', c_train,  '\t', 'TEST ERROR =', c_test)
        print('TRAIN ACCURACY =', a_train,  '\t', 'TEST ACCURACY =', a_test)
        print('---------------------------------------------------------------------------------------------------------------------------------------------------------')
    save_path = saver.save(sess, "/content/drive/My Drive/data/model/model.ckpt")
    final_pred = sess.run(output,feed_dict={x:test})

---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-56-f10b5e5ffc9f> in <module>()
     29         accu_test.append(a_test)
     30 
---> 31         save_path = saver.save(sess, "model.ckpt")
     32 
     33         print('Currently on step {}'.format(i))

3 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py in save(self, sess, save_path, global_step, latest_filename, meta_graph_suffix, write_meta_graph, write_state, strip_default_attrs, save_debug_info)
   1198               meta_graph_filename,
   1199               strip_default_attrs=strip_default_attrs,
-> 1200               save_debug_info=save_debug_info)
   1201 
   1202     if self._is_empty:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py in export_meta_graph(self, filename, collection_list, as_text, export_scope, clear_devices, clear_extraneous_savers, strip_default_attrs, save_debug_info)
   1241     return export_meta_graph(
   1242         filename=filename,
-> 1243         graph_def=ops.get_default_graph().as_graph_def(add_shapes=True),
   1244         saver_def=self.saver_def,
   1245         collection_list=collection_list,

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in as_graph_def(self, from_version, add_shapes)
   3463     """
   3464     # pylint: enable=line-too-long
-> 3465     result, _ = self._as_graph_def(from_version, add_shapes)
   3466     return result
   3467 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in _as_graph_def(self, from_version, add_shapes)
   3388     with self._lock:
   3389       with c_api_util.tf_buffer() as buf:
-> 3390         c_api.TF_GraphToGraphDef(self._c_graph, buf)
   3391         data = c_api.TF_GetBuffer(buf)
   3392       graph = graph_pb2.GraphDef()

InvalidArgumentError: Cannot serialize protocol buffer of type tensorflow.GraphDef as the serialized size (2897149641bytes) would be larger than the limit (2147483647 bytes)

Answer 1

每次循环运行时，您都将matches_train/test和acc_train/test创建为 Tensors ，并将它们添加到图形中。将它们移出循环，或替换为numpy函数。

无法从Google Colab保存Tensorflow模型

1 个答案: