在基础 Tensorflow 2.0 中运行简单回归

Posted 2023-03-12

【中文标题】在基础 Tensorflow 2.0 中运行简单回归

【英文标题】：Running simple regression in base Tensorflow 2.0

【发布时间】：2019-09-08 03:06:30

【问题描述】：

我正在学习 Tensorflow 2.0，我认为在 Tensorflow 中实现最基本的简单线性回归是一个好主意。不幸的是，我遇到了几个问题，我想知道这里是否有人可以提供帮助。

考虑以下设置：

import tensorflow as tf # 2.0.0-alpha0
import numpy as np

x_data = np.random.randn(2000, 1)

w_real = [0.7] # coefficients
b_real = -0.2 # global bias
noise = np.random.randn(1, 2000) * 0.5 # level of noise
y_data = np.matmul(w_real, x_data.T) + b_real + noise

现在开始定义模型：

# modelling this data with tensorflow (manually!)
class SimpleRegressionNN(tf.keras.Model):

    def __init__(self):
        super(SimpleRegressionNN, self).__init__()
        self.input_layer = tf.keras.layers.Input
        self.output_layer = tf.keras.layers.Dense(1)


    def call(self, data_input):
        model = self.input_layer(data_input)
        model = self.output_layer(model)
        # open question: how to account for the intercept/bias term?
        # Ideally, we'd want to generate preds as matmult(X,W) + b
        return model

nn_regressor = SimpleRegressionNN()

reg_loss = tf.keras.losses.MeanSquaredError()
reg_optimiser = tf.keras.optimizers.SGD(0.1)
metric_accuracy = tf.keras.metrics.mean_squared_error

# define forward step
@tf.function
def train_step(x_sample, y_sample):
  with tf.GradientTape() as tape:
    predictions = nn_regressor(x_sample)
    loss = reg_loss(y_sample, predictions)
    gradients = tape.gradient(loss, nn_regressor.trainable_variables) # had to indent this!
  reg_optimiser.apply_gradients(zip(gradients, nn_regressor.trainable_variables))  
  metric_accuracy(y_sample, predictions)

#%%
# run the model
for epoch in range(10):
    for x_point, y_point in zip(x_data.T[0], y_data[0]): # batch of 1
        train_step(x_sample=x_point, y_sample=y_point)
    print("MSE: ".format(metric_accuracy.result()))

很遗憾，我收到以下错误：

TypeError: You are attempting to use Python control flow in a layer that was not declared to be dynamic. Pass `dynamic=True` to the class constructor.
Encountered error:
"""
Tensor objects are only iterable when eager execution is enabled. To iterate over this tensor use tf.map_fn.
"""

完整的错误输出在这里：

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
    611                                                       inputs)) as auto_updater:
--> 612                 outputs = self.call(inputs, *args, **kwargs)
    613                 auto_updater.set_outputs(outputs)

<ipython-input-5-8464ad8bcf07> in call(self, data_input)
      7     def call(self, data_input):
----> 8         model = self.input_layer(data_input)
      9         model = self.output_layer(model)

/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/engine/input_layer.py in Input(shape, batch_size, name, dtype, sparse, tensor, **kwargs)
    232       sparse=sparse,
--> 233       input_tensor=tensor)
    234   # Return tensor including `_keras_history`.

/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/engine/input_layer.py in __init__(self, input_shape, batch_size, dtype, input_tensor, sparse, name, **kwargs)
     93       if input_shape is not None:
---> 94         batch_input_shape = (batch_size,) + tuple(input_shape)
     95       else:

/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in __iter__(self)
    448       raise TypeError(
--> 449           "Tensor objects are only iterable when eager execution is "
    450           "enabled. To iterate over this tensor use tf.map_fn.")

TypeError: Tensor objects are only iterable when eager execution is enabled. To iterate over this tensor use tf.map_fn.


During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)

<ipython-input-22-e1bde858b0fc> in <module>()
      3     #train_step(x_sample=x_data.T[0], y_sample=y_data[0])
      4     for x_point, y_point in zip(x_data.T[0], y_data[0]):
----> 5         train_step(x_sample=x_point, y_sample=y_point)
      6     print("MSE: ".format(metric_accuracy.result()))
      7 

/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    416       # In this case we have not created variables on the first call. So we can
    417       # run the first trace but we should fail if variables are created.
--> 418       results = self._stateful_fn(*args, **kwds)
    419       if self._created_variables:
    420         raise ValueError("Creating variables on a non-first call to a function"

/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
   1285   def __call__(self, *args, **kwargs):
   1286     """Calls a graph function specialized to the inputs."""
-> 1287     graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
   1288     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
   1289 

/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   1609           relaxed_arg_shapes)
   1610       graph_function = self._create_graph_function(
-> 1611           args, kwargs, override_flat_arg_shapes=relaxed_arg_shapes)
   1612       self._function_cache.arg_relaxed[rank_only_cache_key] = graph_function
   1613 

/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   1510             arg_names=arg_names,
   1511             override_flat_arg_shapes=override_flat_arg_shapes,
-> 1512             capture_by_value=self._capture_by_value),
   1513         self._function_attributes)
   1514 

/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
    692                                           converted_func)
    693 
--> 694       func_outputs = python_func(*func_args, **func_kwargs)
    695 
    696       # invariant: `func_outputs` contains only Tensors, IndexedSlices,

/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
    315         # __wrapped__ allows AutoGraph to swap in a converted function. We give
    316         # the function a weak reference to itself to avoid a reference cycle.
--> 317         return weak_wrapped_fn().__wrapped__(*args, **kwds)
    318     weak_wrapped_fn = weakref.ref(wrapped_fn)
    319 

/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    684                   optional_features=autograph_options,
    685                   force_conversion=True,
--> 686               ), args, kwargs)
    687 
    688         # Wrapping around a decorator allows checks like tf_inspect.getargspec

/anaconda3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py in converted_call(f, owner, options, args, kwargs)
    390     return _call_unconverted(f, args, kwargs)
    391 
--> 392   result = converted_f(*effective_args, **kwargs)
    393 
    394   # The converted function's closure is simply inserted into the function's

/var/folders/8_/pl9fgq297ld3b7kgy5tmvf700000gn/T/tmpluzodr7d.py in tf__train_step(x_sample, y_sample)
      2 def tf__train_step(x_sample, y_sample):
      3   with tf.GradientTape() as tape:
----> 4     predictions = ag__.converted_call(nn_regressor, None, ag__.ConversionOptions(recursive=True, verbose=0, strip_decorators=(tf.function, defun, ag__.convert, ag__.do_not_convert, ag__.converted_call), force_conversion=False, optional_features=(), internal_convert_user_code=True), (x_sample,), )
      5     loss = ag__.converted_call(reg_loss, None, ag__.ConversionOptions(recursive=True, verbose=0, strip_decorators=(tf.function, defun_1, ag__.convert, ag__.do_not_convert, ag__.converted_call), force_conversion=False, optional_features=(), internal_convert_user_code=True), (y_sample, predictions), )
      6     gradients = ag__.converted_call('gradient', tape, ag__.ConversionOptions(recursive=True, verbose=0, strip_decorators=(tf.function, defun_2, ag__.convert, ag__.do_not_convert, ag__.converted_call), force_conversion=False, optional_features=(), internal_convert_user_code=True), (loss, nn_regressor.trainable_variables), )

/anaconda3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py in converted_call(f, owner, options, args, kwargs)
    265 
    266   if not options.force_conversion and conversion.is_whitelisted_for_graph(f):
--> 267     return _call_unconverted(f, args, kwargs)
    268 
    269   # internal_convert_user_code is for example turned off when issuing a dynamic

/anaconda3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py in _call_unconverted(f, args, kwargs)
    186     return f.__self__.call(args, kwargs)
    187 
--> 188   return f(*args, **kwargs)
    189 
    190 

/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
    623                                   'dynamic. Pass `dynamic=True` to the class '
    624                                   'constructor.\nEncountered error:\n"""\n' +
--> 625                                   exception_str + '\n"""')
    626               raise
    627           else:

TypeError: You are attempting to use Python control flow in a layer that was not declared to be dynamic. Pass `dynamic=True` to the class constructor.
Encountered error:
"""
Tensor objects are only iterable when eager execution is enabled. To iterate over this tensor use tf.map_fn.
"""

麻烦的是，2.0默认设置为eager execution！

除了这个问题，我还有几个问题：

在这里解释截距项的最佳方法是什么？ 一般方法是否合理，或者我在这里做了什么奇怪的事情？ （忽略批量大小和我必须验证数据的事实，这只是一个玩具示例）

非常感谢！

【问题讨论】：

(1) tf.keras.layers.Dense() 默认添加拦截。错误点的确切线是什么？

感谢您的快速回复。恐怕定位错误的根源并不是那么简单。我已经包含了整个错误输出，希望对您有所帮助。我刚刚尝试单独运行train_step 函数，它给了我同样的错误，我可以缩小范围。

【参考方案1】：

我有以下言论：

您的SimpleRegression 模型中不需要Input 层。此外，不要通过“model”名称调用层的张量输出（就像在 call() 方法中所做的那样）。这真是令人困惑。 您没有将正确的形状传递给您的train_step 函数。它预计在您通过时收到(n_samples, input_dim) (input_dim, )。 请记住，在tensorflow 中，张量的第一维始终是批量大小（即样本数）。总是这样使用它，而不是移调。 为什么要调用metric_accuracy = tf.keras.metrics.mean_squared_error 准确度？你有一个回归问题，回归中没有准确性。还有，为什么要定义两次，计算两次mse？ 如果您使用tf.convert_to_tensor() 转换数据，执行速度会更快。 函数train_step() 执行向前和向后传球，而不仅仅是向前传球。 为玩具示例使用小型数据集（2-10 个样本，而不是 2000 个），尤其是在您不知道自己的代码是否有效的情况下！ 您的函数train_step() 没有返回任何内容，您希望如何打印mse 损失的值。

这是您的代码的更正版本：

import tensorflow as tf # 2.0.0-alpha0
import numpy as np

x_data = np.random.randn(5, 2)

w_real = 0.7 # coefficients
b_real = -0.2 # global bias
noise = np.random.randn(5, 2) * 0.01 # level of noise
y_data = w_real * x_data + b_real + noise

class SimpleRegressionNN(tf.keras.Model):

    def __init__(self):
        super(SimpleRegressionNN, self).__init__()

        self.output_layer = tf.keras.layers.Dense(1, input_shape=(2, ))

    def call(self, data_input):
        result = self.output_layer(data_input)
        return result

reg_loss = tf.keras.losses.MeanSquaredError()
reg_optimiser = tf.keras.optimizers.SGD(0.1)

nn_regressor = SimpleRegressionNN()

@tf.function
def train_step(x_sample, y_sample):
    with tf.GradientTape() as tape:
        predictions = nn_regressor(x_sample)
        loss = reg_loss(y_sample, predictions)
    gradients = tape.gradient(loss, nn_regressor.trainable_variables) # had to indent this!
    reg_optimiser.apply_gradients(zip(gradients, nn_regressor.trainable_variables))  

    return loss

for x_point, y_point in zip(x_data, y_data): # batch of 1
    x_point, y_point = tf.convert_to_tensor([x_point]), tf.convert_to_tensor([y_point])
    mse = train_step(x_sample=x_point, y_sample=y_point)
    print("MSE: ".format(mse.numpy()))

【讨论】：

弗拉德，非常感谢！我正在关注一些可以在网上找到的示例（但在 TF 2.0 上没有太多示例），这就是为什么我的代码中有一些奇怪之处。您的示例非常有道理，再次感谢您！

很高兴它有帮助。我已经纠正了我的代码中的小错误。最好在GradientTape 上下文之外计算和应用梯度（如果您不打算计算二阶导数）。这不是一个错误，但如果您在 GradientTape 上下文中执行此操作，它会将这些操作记录到缓冲区中，这会增加不必要的开销。