Keras：在使用 train_on_batch 处理批处理数据时进行测试、交叉验证和准确性

Question

有人能指出一个完整的例子吗？

• 使用train_on_batch() 将批处理（和腌制）数据放入循环中
• 为验证目的保留每批次的数据
• 在处理完所有个批次后留出测试数据以进行准确性评估（请参阅下面我示例的最后一行）。

我在互联网上找到了很多 1 - 5 行代码 sn-ps，说明如何调用 train_on_batch() 或 fit_generator()，但到目前为止，还没有任何内容能清楚说明如何分离和处理验证和测试数据在使用train_on_batch()时。

F。 Chollet 的出色示例 Cifar10_cnn (https://github.com/fchollet/keras/blob/master/examples/cifar10_cnn.py) 并未说明我上面列出的所有要点。

您可以说，“嘿，处理测试数据是您的问题。手动操作。”美好的！但我不明白这些例程做得好到什么程度，甚至不知道这是否有必要。它们大多是黑匣子，据我所知，它们在后台自动处理验证和测试数据。我希望更完整的示例能够消除混淆。

例如，在下面的示例中，我从 pickle 文件中迭代地读取批次，我将如何修改对 train_on_batch 的调用以处理 validation_data？以及如何留出测试数据（test_x 和test_y）以便在算法结束时评估准确性？

while 1:
    try:
        batch = np.array(pickle.load(fvecs))
        polarities = np.array(pickle.load(fpols)) 

        # Divide a batch of 1000 documents (movie reviews) into:
        # 800 rows of training data, and
        # 200 rows of test (validation?) data
        train_x, val_x, train_y, val_y = train_test_split(batch, polarities, test_size=0.2)

        doc_size = 30
        x_batch = pad_sequences(train_x, maxlen=doc_size)
        y_batch = train_y

        # Fit the model 
        model.train_on_batch(x_batch, y_batch)
        # model.fit(train_x, train_y, validation_data=(val_x, val_y), epochs=2, batch_size=800, verbose=2)

    except EOFError:
        print("EOF detected.")
        break

# Final evaluation of the model
scores = model.evaluate(test_x, test_y, verbose=0)
print("Accuracy: %.2f%%" % (scores[1] * 100))

Answer 1

我无法为您提供完整的示例，但正如您所见 here 您同时拥有 train_on_batch 和 test_on_batch 这应该表明“train_on_batch”函数应该只训练模型而不是测试它。

为了更加确定您可以在代码 itself 中看到该函数使用整个批次进行训练，并且没有用于测试/验证。

为方便起见，我在下面引用相关代码：

def train_on_batch(self, x, y,
                   sample_weight=None,
                   class_weight=None):
    """Runs a single gradient update on a single batch of data.
    # Arguments
        x: Numpy array of training data,
            or list of Numpy arrays if the model has multiple inputs.
            If all inputs in the model are named,
            you can also pass a dictionary
            mapping input names to Numpy arrays.
        y: Numpy array of target data,
            or list of Numpy arrays if the model has multiple outputs.
            If all outputs in the model are named,
            you can also pass a dictionary
            mapping output names to Numpy arrays.
        sample_weight: Optional array of the same length as x, containing
            weights to apply to the model's loss for each sample.
            In the case of temporal data, you can pass a 2D array
            with shape (samples, sequence_length),
            to apply a different weight to every timestep of every sample.
            In this case you should make sure to specify
            sample_weight_mode="temporal" in compile().
        class_weight: Optional dictionary mapping
            class indices (integers) to
            a weight (float) to apply to the model's loss for the samples
            from this class during training.
            This can be useful to tell the model to "pay more attention" to
            samples from an under-represented class.
    # Returns
        Scalar training loss
        (if the model has a single output and no metrics)
        or list of scalars (if the model has multiple outputs
        and/or metrics). The attribute `model.metrics_names` will give you
        the display labels for the scalar outputs.
    """
    x, y, sample_weights = self._standardize_user_data(
        x, y,
        sample_weight=sample_weight,
        class_weight=class_weight,
        check_batch_axis=True)
    if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
        ins = x + y + sample_weights + [1.]
    else:
        ins = x + y + sample_weights
    self._make_train_function()
    outputs = self.train_function(ins)
    if len(outputs) == 1:
        return outputs[0]
    return outputs