Keras之model.compile与train_on_batch和TensorBoard for train_on_batch

在利用TensorFlow的TensorBoard对train_on_batch的输出进行画图时发现了一些问题。下面对train_on_batch的输出进行讲解。在讲解train_on_batch之前，先看一下Keras的model.compile函数。下面利用Keras版Faster R-CNN代码进行讲解。示例代码如下：

# define the RPN, built on the base layers

rpn = nn.rpn(shared_layers, num_anchors)

classifier = nn.classifier(shared_layers, roi_input, cfg.num_rois, nb_classes=len(classes_count), trainable=True)

model_rpn = Model(img_input, rpn[:2])
model_classifier = Model([img_input, roi_input], classifier)
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.
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optimizer = Adam(lr=1e-5)
optimizer_classifier = Adam(lr=1e-5)
model_rpn.compile(optimizer=optimizer,
                     loss=[losses_fn.rpn_loss_cls(num_anchors), losses_fn.rpn_loss_regr(num_anchors)])
model_classifier.compile(optimizer=optimizer_classifier,
                           loss=[losses_fn.class_loss_cls, losses_fn.class_loss_regr(len(classes_count) - 1)],
                           metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'})
.
.
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loss_rpn = model_rpn.train_on_batch(X, Y)
loss_class = model_classifier.train_on_batch([X, X2[:, sel_samples, :]],
                                                             [Y1[:, sel_samples, :], Y2[:, sel_samples, :]])

在model_rpn中loss=[losses_fn.rpn_loss_cls(num_anchors), losses_fn.rpn_loss_regr(num_anchors)]，由model_rpn的定义model_rpn = Model(img_input, rpn[:2])可知，model_rpn的输入是img_input，输出为rpn[:2]，即对应两个输出，则对应两个loss输出，但是在进行训练train_on_batch的时候是输出三个值的list，其中第一个值为输出的总的loss，其名为'loss'，是两个输出的loss的总和。两个输出loss的名字由定义网络的输出名加上'_loss'。下面是rpn网络的定义，注意其定义的输出名name的值即可，如下：

def rpn(base_layers, num_anchors):
    x = Convolution2D(512, (3, 3), padding='same', activation='relu', kernel_initializer='normal', name='rpn_conv1')(
        base_layers)

    x_class = Convolution2D(num_anchors, (1, 1), activation='sigmoid', kernel_initializer='uniform',
                            name='rpn_out_class')(x)
    x_regr = Convolution2D(num_anchors * 6, (1, 1), activation='linear', kernel_initializer='zero',
                           name='rpn_out_regress')(x)  

    return [x_class, x_regr, base_layers]

可以利用model.metrics_names查看train_on_batch的输出的每个值的特性，下面是model_rpn的输出：

同理，可得model_classifier的输出如下：

model_classifier的metrics_names与model_rpn不同的是，model_classifier在compile时指定了'accuracy'输出。

下面具体看一下model.compile的部分定义：

class Model(Network):
    """The `Model` class adds training & evaluation routines to a `Network`.
    """

    def compile(self, optimizer,
                loss=None,
                metrics=None,
                loss_weights=None,
                sample_weight_mode=None,
                weighted_metrics=None,
                target_tensors=None,
                **kwargs):
        """Configures the model for training.

        # Arguments
            optimizer: String (name of optimizer) or optimizer instance.
                See [optimizers](/optimizers).
            loss: String (name of objective function) or objective function.
                See [losses](/losses).
                If the model has multiple outputs, you can use a different loss
                on each output by passing a dictionary or a list of losses.
                The loss value that will be minimized by the model
                will then be the sum of all individual losses.
            metrics: List of metrics to be evaluated by the model
                during training and testing.
                Typically you will use `metrics=['accuracy']`.
                To specify different metrics for different outputs of a
                multi-output model, you could also pass a dictionary,
                such as `metrics={'output_a': 'accuracy'}`.
            loss_weights: Optional list or dictionary specifying scalar
                coefficients (Python floats) to weight the loss contributions
                of different model outputs.
                The loss value that will be minimized by the model
                will then be the *weighted sum* of all individual losses,
                weighted by the `loss_weights` coefficients.
                If a list, it is expected to have a 1:1 mapping
                to the model's outputs. If a tensor, it is expected to map
                output names (strings) to scalar coefficients.
            sample_weight_mode: If you need to do timestep-wise
                sample weighting (2D weights), set this to `"temporal"`.
                `None` defaults to sample-wise weights (1D).
                If the model has multiple outputs, you can use a different
                `sample_weight_mode` on each output by passing a
                dictionary or a list of modes.
            weighted_metrics: List of metrics to be evaluated and weighted
                by sample_weight or class_weight during training and testing.
            target_tensors: By default, Keras will create placeholders for the
                model's target, which will be fed with the target data during
                training. If instead you would like to use your own
                target tensors (in turn, Keras will not expect external
                Numpy data for these targets at training time), you
                can specify them via the `target_tensors` argument. It can be
                a single tensor (for a single-output model), a list of tensors,
                or a dict mapping output names to target tensors.
            **kwargs: When using the Theano/CNTK backends, these arguments
                are passed into `K.function`.
                When using the TensorFlow backend,
                these arguments are passed into `tf.Session.run`.

        # Raises
            ValueError: In case of invalid arguments for
                `optimizer`, `loss`, `metrics` or `sample_weight_mode`.
        """
        self.optimizer = optimizers.get(optimizer)
        self.loss = loss or []
        self.metrics = metrics or []
        self.loss_weights = loss_weights
        self.sample_weight_mode = sample_weight_mode
        self.weighted_metrics = weighted_metrics

        if not self.built:
            # Model is not compilable because
            # it does not know its number of inputs
            # and outputs, nor their shapes and names.
            # We will compile after the first
            # time the model gets called on training data.
            return
        self._is_compiled = True

        # Prepare loss functions.
        if isinstance(loss, dict):
            for name in loss:
                if name not in self.output_names:
                    raise ValueError('Unknown entry in loss '
                                     'dictionary: "' + name + '". '
                                     'Only expected the following keys: ' +
                                     str(self.output_names))
            loss_functions = []
            for name in self.output_names:
                if name not in loss:
                    warnings.warn('Output "' + name +
                                  '" missing from loss dictionary. '
                                  'We assume this was done on purpose, '
                                  'and we will not be expecting '
                                  'any data to be passed to "' + name +
                                  '" during training.', stacklevel=2)
                loss_functions.append(losses.get(loss.get(name)))
        elif isinstance(loss, list):
            if len(loss) != len(self.outputs):
                raise ValueError('When passing a list as loss, '
                                 'it should have one entry per model outputs. '
                                 'The model has ' + str(len(self.outputs)) +
                                 ' outputs, but you passed loss=' +
                                 str(loss))
            loss_functions = [losses.get(l) for l in loss]
        else:
            loss_function = losses.get(loss)
            loss_functions = [loss_function for _ in range(len(self.outputs))]
        self.loss_functions = loss_functions
        weighted_losses = [
            weighted_masked_objective(fn) for fn in loss_functions]
        skip_target_indices = []
        skip_target_weighing_indices = []
        self._feed_outputs = []
        self._feed_output_names = []
        self._feed_output_shapes = []
        self._feed_loss_fns = []
        for i in range(len(weighted_losses)):
            if weighted_losses[i] is None:
                skip_target_indices.append(i)
                skip_target_weighing_indices.append(i)

下面具体看一下train_on_batch的部分定义：

    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)
        if self._uses_dynamic_learning_phase():
            ins = x + y + sample_weights + [1.]
        else:
            ins = x + y + sample_weights
        self._make_train_function()
        outputs = self.train_function(ins)
        return unpack_singleton(outputs)

关于Keras中model.compile与train_on_batch代码的解读，有时间的话我会详细梳理一下，敬请期待~

下面就是关于TensorBoard for train_on_batch的代码了：

import numpy as np
import tensorflow as tf
from keras.callbacks import TensorBoard
from keras.layers import Input, Dense
from keras.models import Model


def write_log(callback, names, logs, batch_no):
    for name, value in zip(names, logs):
        summary = tf.Summary()
        summary_value = summary.value.add()
        summary_value.simple_value = value
        summary_value.tag = name
        callback.writer.add_summary(summary, batch_no)
        callback.writer.flush()
    
net_in = Input(shape=(3,))
net_out = Dense(1)(net_in)
model = Model(net_in, net_out)
model.compile(loss='mse', optimizer='sgd', metrics=['mae'])

log_path = './graph'
callback = TensorBoard(log_path)
callback.set_model(model)
train_names = ['train_loss', 'train_mae']
val_names = ['val_loss', 'val_mae']
for batch_no in range(100):
    X_train, Y_train = np.random.rand(32, 3), np.random.rand(32, 1)
    logs = model.train_on_batch(X_train, Y_train)
    write_log(callback, train_names, logs, batch_no)
    
    if batch_no % 10 == 0:
        X_val, Y_val = np.random.rand(32, 3), np.random.rand(32, 1)
        logs = model.train_on_batch(X_val, Y_val)
        write_log(callback, val_names, logs, batch_no//10)

注意for name, value in zip(names, logs)这行代码中name与value的准确对应，请参考前述中model.compile与train_on_batch的讲解。

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