使用相同的TPU模型在谷歌Colab培训和推断（预测）

Question

我有一个代码是这样的：

def getModel():
    model = Sequential()
    model.Add(...)
    .....
    model = tf.contrib.tpu.keras_to_tpu_model(model,
            strategy=tf.contrib.tpu.TPUDistributionStrategy(
            tf.contrib.cluster_resolver.TPUClusterResolver(tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])
        ))
    model.compile(loss='mse',
                  optimizer=tf.train.AdamOptimizer(learning_rate=1e-3, ))
    return model

tpu_model = getModel()
## Main loop
    ....
    tpu_model.predict(states)
    tpu_model.fit(...)

请注意，我用批量预测和训练一样tpu_model。

tpu_model.predict()似乎很好地工作，但是当它运行tpu_model.fit(...)，它引发以下错误：

WARNING:tensorflow:tpu_model (from tensorflow.contrib.tpu.python.tpu.keras_support) is experimental and may change or be removed at any time, and without warning.
INFO:tensorflow:New input shapes; (re-)compiling: mode=infer (# of cores 8), [TensorSpec(shape=(4, 7), dtype=tf.float32, name='dense_6_input_10')]
INFO:tensorflow:Overriding default placeholder.
INFO:tensorflow:Remapping placeholder for dense_6_input
INFO:tensorflow:Started compiling
INFO:tensorflow:Finished compiling. Time elapsed: 1.464857578277588 secs
INFO:tensorflow:Setting weights on TPU model.
...
...
...
RuntimeError                              Traceback (most recent call last)
--> 101         history = tpu_model.fit(states, target_f, epochs=1, verbose=0)
    102         # Keeping track of loss
    103         loss = history.history['loss'][0]

/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/tpu/python/tpu/keras_support.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
   1505                                   validation_split, validation_data, shuffle,
   1506                                   class_weight, sample_weight, initial_epoch,
-> 1507                                   steps_per_epoch, validation_steps, **kwargs)
   1508       finally:
   1509         self._numpy_to_infeed_manager_list = []

/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/tpu/python/tpu/keras_support.py in _pipeline_fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
   1578         steps_name='steps_per_epoch',
   1579         steps=steps_per_epoch,
-> 1580         validation_split=validation_split)
   1581 
   1582     # Prepare validation data

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split)
    990         x, y, sample_weight = next_element
    991     x, y, sample_weights = self._standardize_weights(x, y, sample_weight,
--> 992                                                      class_weight, batch_size)
    993     return x, y, sample_weights
    994 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in _standardize_weights(self, x, y, sample_weight, class_weight, batch_size)
   1036     if y is not None:
   1037       if not self.optimizer:
-> 1038         raise RuntimeError('You must compile a model before '
   1039                            'training/testing. '
   1040                            'Use `model.compile(optimizer, loss)`.')

RuntimeError: You must compile a model before training/testing. Use `model.compile(optimizer, loss)`.

正如你可以从日志中看到，似乎有两种模式对TPU运行： 1. mode=infer 2. mode=training

看来两者不能同时进行。有没有办法解决？

因为我做的强化学习，其中一批是基于添加到列表动态实时采样从中，预测批次取样（和某些值改变）和训练有素我不能使用发电机。

Answer 1

我认为你可以做到这一点如下：

• 民俗tensorflow keras亚当和添加一些代码到get_update（）： 如果self.iterations = 0： LR = 0 其他： LR = self.lr
• 使用此自建亚当，建立与外形=小火车的数据“data_for_graph_build”（BATCHSIZE，你的其他形状）
• 做tpu_model.fit（data_for_graph_build，历元= 1，=的batch_size BATCHSIZE）
• 终于做你tpu_model.predict（州）和tpu_model.fit（...）

这似乎很棘手。我希望它的作品。但作为优化权重建立在data_for_graph_build可能造成的差异

Answer 2

通常你会想打电话给fit你打电话predict因为fit训练模型和predict使用训练的模型做预测之前。看看这些Cloud TPU Tutorials看看this guide了解Keras API。