main.py

tb = TensorBoard(
        log_dir='./logs/{}'.format(args.exp_dir),
        histogram_freq=0,
        batch_size=args.batch_size,
        write_graph=True,
        write_batch_performance=True,
        write_grads=False,
        write_images=False,
        embeddings_freq=0,
        embeddings_layer_names=None,
        embeddings_metadata=None
    )

tensorboard.py

from __future__ import absolute_import
from __future__ import print_function

import os
import csv
import six

import numpy as np
import time
import json
import warnings

from collections import deque
from collections import OrderedDict
from collections import Iterable
from keras.utils.generic_utils import Progbar
import keras.backend as K

try:
    import requests
except ImportError:
    requests = None

if K.backend() == 'tensorflow':
    import tensorflow as tf
    from tensorflow.contrib.tensorboard.plugins import projector


class CallbackList(object):
    """Container abstracting a list of callbacks.

    # Arguments
        callbacks: List of `Callback` instances.
        queue_length: Queue length for keeping
            running statistics over callback execution time.
    """

    def __init__(self, callbacks=None, queue_length=10):
        callbacks = callbacks or []
        self.callbacks = [c for c in callbacks]
        self.queue_length = queue_length

    def append(self, callback):
        self.callbacks.append(callback)

    def set_params(self, params):
        for callback in self.callbacks:
            callback.set_params(params)

    def set_model(self, model):
        for callback in self.callbacks:
            callback.set_model(model)

    def on_epoch_begin(self, epoch, logs=None):
        """Called at the start of an epoch.

        # Arguments
            epoch: integer, index of epoch.
            logs: dictionary of logs.
        """
        logs = logs or {}
        for callback in self.callbacks:
            callback.on_epoch_begin(epoch, logs)
        self._delta_t_batch = 0.
        self._delta_ts_batch_begin = deque([], maxlen=self.queue_length)
        self._delta_ts_batch_end = deque([], maxlen=self.queue_length)

    def on_epoch_end(self, epoch, logs=None):
        """Called at the end of an epoch.

        # Arguments
            epoch: integer, index of epoch.
            logs: dictionary of logs.
        """
        logs = logs or {}
        for callback in self.callbacks:
            callback.on_epoch_end(epoch, logs)

    def on_batch_begin(self, batch, logs=None):
        """Called right before processing a batch.

        # Arguments
            batch: integer, index of batch within the current epoch.
            logs: dictionary of logs.
        """
        logs = logs or {}
        t_before_callbacks = time.time()
        for callback in self.callbacks:
            callback.on_batch_begin(batch, logs)
        self._delta_ts_batch_begin.append(time.time() - t_before_callbacks)
        delta_t_median = np.median(self._delta_ts_batch_begin)
        if (self._delta_t_batch > 0. and
           delta_t_median > 0.95 * self._delta_t_batch and
           delta_t_median > 0.1):
            warnings.warn('Method on_batch_begin() is slow compared '
                          'to the batch update (%f). Check your callbacks.'
                          % delta_t_median)
        self._t_enter_batch = time.time()

    def on_batch_end(self, batch, logs=None):
        """Called at the end of a batch.

        # Arguments
            batch: integer, index of batch within the current epoch.
            logs: dictionary of logs.
        """
        logs = logs or {}
        if not hasattr(self, '_t_enter_batch'):
            self._t_enter_batch = time.time()
        self._delta_t_batch = time.time() - self._t_enter_batch
        t_before_callbacks = time.time()
        for callback in self.callbacks:
            callback.on_batch_end(batch, logs)
        self._delta_ts_batch_end.append(time.time() - t_before_callbacks)
        delta_t_median = np.median(self._delta_ts_batch_end)
        if (self._delta_t_batch > 0. and
           (delta_t_median > 0.95 * self._delta_t_batch and delta_t_median > 0.1)):
            warnings.warn('Method on_batch_end() is slow compared '
                          'to the batch update (%f). Check your callbacks.'
                          % delta_t_median)

    def on_train_begin(self, logs=None):
        """Called at the beginning of training.

        # Arguments
            logs: dictionary of logs.
        """
        logs = logs or {}
        for callback in self.callbacks:
            callback.on_train_begin(logs)

    def on_train_end(self, logs=None):
        """Called at the end of training.

        # Arguments
            logs: dictionary of logs.
        """
        logs = logs or {}
        for callback in self.callbacks:
            callback.on_train_end(logs)

    def __iter__(self):
        return iter(self.callbacks)


class Callback(object):
    """Abstract base class used to build new callbacks.

    # Properties
        params: dict. Training parameters
            (eg. verbosity, batch size, number of epochs...).
        model: instance of `keras.models.Model`.
            Reference of the model being trained.

    The `logs` dictionary that callback methods
    take as argument will contain keys for quantities relevant to
    the current batch or epoch.

    Currently, the `.fit()` method of the `Sequential` model class
    will include the following quantities in the `logs` that
    it passes to its callbacks:

        on_epoch_end: logs include `acc` and `loss`, and
            optionally include `val_loss`
            (if validation is enabled in `fit`), and `val_acc`
            (if validation and accuracy monitoring are enabled).
        on_batch_begin: logs include `size`,
            the number of samples in the current batch.
        on_batch_end: logs include `loss`, and optionally `acc`
            (if accuracy monitoring is enabled).
    """

    def __init__(self):
        self.validation_data = None

    def set_params(self, params):
        self.params = params

    def set_model(self, model):
        self.model = model

    def on_epoch_begin(self, epoch, logs=None):
        pass

    def on_epoch_end(self, epoch, logs=None):
        pass

    def on_batch_begin(self, batch, logs=None):
        pass

    def on_batch_end(self, batch, logs=None):
        pass

    def on_train_begin(self, logs=None):
        pass

    def on_train_end(self, logs=None):
        pass


class BaseLogger(Callback):
    """Callback that accumulates epoch averages of metrics.

    This callback is automatically applied to every Keras model.
    """

    def on_epoch_begin(self, epoch, logs=None):
        self.seen = 0
        self.totals = {}

    def on_batch_end(self, batch, logs=None):
        logs = logs or {}
        batch_size = logs.get('size', 0)
        self.seen += batch_size

        for k, v in logs.items():
            if k in self.totals:
                self.totals[k] += v * batch_size
            else:
                self.totals[k] = v * batch_size

    def on_epoch_end(self, epoch, logs=None):
        if logs is not None:
            for k in self.params['metrics']:
                if k in self.totals:
                    # Make value available to next callbacks.
                    logs[k] = self.totals[k] / self.seen


class TerminateOnNaN(Callback):
    """Callback that terminates training when a NaN loss is encountered."""

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

    def on_batch_end(self, batch, logs=None):
        logs = logs or {}
        loss = logs.get('loss')
        if loss is not None:
            if np.isnan(loss) or np.isinf(loss):
                print('Batch %d: Invalid loss, terminating training' % (batch))
                self.model.stop_training = True


class ProgbarLogger(Callback):
    """Callback that prints metrics to stdout.

    # Arguments
        count_mode: One of "steps" or "samples".
            Whether the progress bar should
            count samples seens or steps (batches) seen.

    # Raises
        ValueError: In case of invalid `count_mode`.
    """

    def __init__(self, count_mode='samples'):
        super(ProgbarLogger, self).__init__()
        if count_mode == 'samples':
            self.use_steps = False
        elif count_mode == 'steps':
            self.use_steps = True
        else:
            raise ValueError('Unknown `count_mode`: ' + str(count_mode))

    def on_train_begin(self, logs=None):
        self.verbose = self.params['verbose']
        self.epochs = self.params['epochs']

    def on_epoch_begin(self, epoch, logs=None):
        if self.verbose:
            print('Epoch %d/%d' % (epoch + 1, self.epochs))
            if self.use_steps:
                target = self.params['steps']
            else:
                target = self.params['samples']
            self.target = target
            self.progbar = Progbar(target=self.target,
                                   verbose=self.verbose)
        self.seen = 0

    def on_batch_begin(self, batch, logs=None):
        if self.seen < self.target:
            self.log_values = []

    def on_batch_end(self, batch, logs=None):
        logs = logs or {}
        batch_size = logs.get('size', 0)
        if self.use_steps:
            self.seen += 1
        else:
            self.seen += batch_size

        for k in self.params['metrics']:
            if k in logs:
                self.log_values.append((k, logs[k]))

        # Skip progbar update for the last batch;
        # will be handled by on_epoch_end.
        if self.verbose and self.seen < self.target:
            self.progbar.update(self.seen, self.log_values)

    def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}
        for k in self.params['metrics']:
            if k in logs:
                self.log_values.append((k, logs[k]))
        if self.verbose:
            self.progbar.update(self.seen, self.log_values, force=True)


class History(Callback):
    """Callback that records events into a `History` object.

    This callback is automatically applied to
    every Keras model. The `History` object
    gets returned by the `fit` method of models.
    """

    def on_train_begin(self, logs=None):
        self.epoch = []
        self.history = {}

    def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}
        self.epoch.append(epoch)
        for k, v in logs.items():
            self.history.setdefault(k, []).append(v)


class ModelCheckpoint(Callback):
    """Save the model after every epoch.

    `filepath` can contain named formatting options,
    which will be filled the value of `epoch` and
    keys in `logs` (passed in `on_epoch_end`).

    For example: if `filepath` is `weights.{epoch:02d}-{val_loss:.2f}.hdf5`,
    then the model checkpoints will be saved with the epoch number and
    the validation loss in the filename.

    # Arguments
        filepath: string, path to save the model file.
        monitor: quantity to monitor.
        verbose: verbosity mode, 0 or 1.
        save_best_only: if `save_best_only=True`,
            the latest best model according to
            the quantity monitored will not be overwritten.
        mode: one of {auto, min, max}.
            If `save_best_only=True`, the decision
            to overwrite the current save file is made
            based on either the maximization or the
            minimization of the monitored quantity. For `val_acc`,
            this should be `max`, for `val_loss` this should
            be `min`, etc. In `auto` mode, the direction is
            automatically inferred from the name of the monitored quantity.
        save_weights_only: if True, then only the model's weights will be
            saved (`model.save_weights(filepath)`), else the full model
            is saved (`model.save(filepath)`).
        period: Interval (number of epochs) between checkpoints.
    """

    def __init__(self, filepath, monitor='val_loss', verbose=0,
                 save_best_only=False, save_weights_only=False,
                 mode='auto', period=1):
        super(ModelCheckpoint, self).__init__()
        self.monitor = monitor
        self.verbose = verbose
        self.filepath = filepath
        self.save_best_only = save_best_only
        self.save_weights_only = save_weights_only
        self.period = period
        self.epochs_since_last_save = 0

        if mode not in ['auto', 'min', 'max']:
            warnings.warn('ModelCheckpoint mode %s is unknown, '
                          'fallback to auto mode.' % (mode),
                          RuntimeWarning)
            mode = 'auto'

        if mode == 'min':
            self.monitor_op = np.less
            self.best = np.Inf
        elif mode == 'max':
            self.monitor_op = np.greater
            self.best = -np.Inf
        else:
            if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):
                self.monitor_op = np.greater
                self.best = -np.Inf
            else:
                self.monitor_op = np.less
                self.best = np.Inf

    def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}
        self.epochs_since_last_save += 1
        if self.epochs_since_last_save >= self.period:
            self.epochs_since_last_save = 0
            filepath = self.filepath.format(epoch=epoch, **logs)
            if self.save_best_only:
                current = logs.get(self.monitor)
                if current is None:
                    warnings.warn('Can save best model only with %s available, '
                                  'skipping.' % (self.monitor), RuntimeWarning)
                else:
                    if self.monitor_op(current, self.best):
                        if self.verbose > 0:
                            print('Epoch %05d: %s improved from %0.5f to %0.5f,'
                                  ' saving model to %s'
                                  % (epoch, self.monitor, self.best,
                                     current, filepath))
                        self.best = current
                        if self.save_weights_only:
                            self.model.save_weights(filepath, overwrite=True)
                        else:
                            self.model.save(filepath, overwrite=True)
                    else:
                        if self.verbose > 0:
                            print('Epoch %05d: %s did not improve' %
                                  (epoch, self.monitor))
            else:
                if self.verbose > 0:
                    print('Epoch %05d: saving model to %s' % (epoch, filepath))
                if self.save_weights_only:
                    self.model.save_weights(filepath, overwrite=True)
                else:
                    self.model.save(filepath, overwrite=True)


class EarlyStopping(Callback):
    """Stop training when a monitored quantity has stopped improving.

    # Arguments
        monitor: quantity to be monitored.
        min_delta: minimum change in the monitored quantity
            to qualify as an improvement, i.e. an absolute
            change of less than min_delta, will count as no
            improvement.
        patience: number of epochs with no improvement
            after which training will be stopped.
        verbose: verbosity mode.
        mode: one of {auto, min, max}. In `min` mode,
            training will stop when the quantity
            monitored has stopped decreasing; in `max`
            mode it will stop when the quantity
            monitored has stopped increasing; in `auto`
            mode, the direction is automatically inferred
            from the name of the monitored quantity.
    """

    def __init__(self, monitor='val_loss',
                 min_delta=0, patience=0, verbose=0, mode='auto'):
        super(EarlyStopping, self).__init__()

        self.monitor = monitor
        self.patience = patience
        self.verbose = verbose
        self.min_delta = min_delta
        self.wait = 0
        self.stopped_epoch = 0

        if mode not in ['auto', 'min', 'max']:
            warnings.warn('EarlyStopping mode %s is unknown, '
                          'fallback to auto mode.' % (self.mode),
                          RuntimeWarning)
            mode = 'auto'

        if mode == 'min':
            self.monitor_op = np.less
        elif mode == 'max':
            self.monitor_op = np.greater
        else:
            if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):
                self.monitor_op = np.greater
            else:
                self.monitor_op = np.less

        if self.monitor_op == np.greater:
            self.min_delta *= 1
        else:
            self.min_delta *= -1

    def on_train_begin(self, logs=None):
        # Allow instances to be re-used
        self.wait = 0
        self.stopped_epoch = 0
        self.best = np.Inf if self.monitor_op == np.less else -np.Inf

    def on_epoch_end(self, epoch, logs=None):
        current = logs.get(self.monitor)
        if current is None:
            warnings.warn('Early stopping requires %s available!' %
                          (self.monitor), RuntimeWarning)

        if self.monitor_op(current - self.min_delta, self.best):
            self.best = current
            self.wait = 0
        else:
            if self.wait >= self.patience:
                self.stopped_epoch = epoch
                self.model.stop_training = True
            self.wait += 1

    def on_train_end(self, logs=None):
        if self.stopped_epoch > 0 and self.verbose > 0:
            print('Epoch %05d: early stopping' % (self.stopped_epoch))


class RemoteMonitor(Callback):
    """Callback used to stream events to a server.

    Requires the `requests` library.
    Events are sent to `root + '/publish/epoch/end/'` by default. Calls are
    HTTP POST, with a `data` argument which is a
    JSON-encoded dictionary of event data.

    # Arguments
        root: String; root url of the target server.
        path: String; path relative to `root` to which the events will be sent.
        field: String; JSON field under which the data will be stored.
        headers: Dictionary; optional custom HTTP headers.
            Defaults to:
            `{'Accept': 'application/json', 'Content-Type': 'application/json'}`
    """

    def __init__(self,
                 root='http://localhost:9000',
                 path='/publish/epoch/end/',
                 field='data',
                 headers=None):
        super(RemoteMonitor, self).__init__()
        if headers is None:
            headers = {'Accept': 'application/json',
                       'Content-Type': 'application/json'}
        self.root = root
        self.path = path
        self.field = field
        self.headers = headers

    def on_epoch_end(self, epoch, logs=None):
        if requests is None:
            raise ImportError('RemoteMonitor requires '
                              'the `requests` library.')
        logs = logs or {}
        send = {}
        send['epoch'] = epoch
        for k, v in logs.items():
            send[k] = v
        try:
            requests.post(self.root + self.path,
                          {self.field: json.dumps(send)},
                          headers=self.headers)
        except requests.exceptions.RequestException:
            warnings.warn('Warning: could not reach RemoteMonitor '
                          'root server at ' + str(self.root))


class LearningRateScheduler(Callback):
    """Learning rate scheduler.

    # Arguments
        schedule: a function that takes an epoch index as input
            (integer, indexed from 0) and returns a new
            learning rate as output (float).
    """

    def __init__(self, schedule):
        super(LearningRateScheduler, self).__init__()
        self.schedule = schedule

    def on_epoch_begin(self, epoch, logs=None):
        if not hasattr(self.model.optimizer, 'lr'):
            raise ValueError('Optimizer must have a "lr" attribute.')
        lr = self.schedule(epoch)
        if not isinstance(lr, (float, np.float32, np.float64)):
            raise ValueError('The output of the "schedule" function '
                             'should be float.')
        K.set_value(self.model.optimizer.lr, lr)


class TensorBoard(Callback):
    """Tensorboard basic visualizations.

    This callback writes a log for TensorBoard, which allows
    you to visualize dynamic graphs of your training and test
    metrics, as well as activation histograms for the different
    layers in your model.

    TensorBoard is a visualization tool provided with TensorFlow.

    If you have installed TensorFlow with pip, you should be able
    to launch TensorBoard from the command line:
    ```
    tensorboard --logdir=/full_path_to_your_logs
    ```
    You can find more information about TensorBoard
    [here](https://www.tensorflow.org/get_started/summaries_and_tensorboard).

    # Arguments
        log_dir: the path of the directory where to save the log
            files to be parsed by TensorBoard.
        histogram_freq: frequency (in epochs) at which to compute activation
            and weight histograms for the layers of the model. If set to 0,
            histograms won't be computed. Validation data (or split) must be
            specified for histogram visualizations.
        write_graph: whether to visualize the graph in TensorBoard.
            The log file can become quite large when
            write_graph is set to True.
        write_grads: whether to visualize gradient histograms in TensorBoard.
            `histogram_freq` must be greater than 0.
        batch_size: size of batch of inputs to feed to the network
            for histograms computation.
        write_images: whether to write model weights to visualize as
            image in TensorBoard.
        write_batch_performance: whether to write training metrics on batch
            completion
        embeddings_freq: frequency (in epochs) at which selected embedding
            layers will be saved.
        embeddings_layer_names: a list of names of layers to keep eye on. If
            None or empty list all the embedding layer will be watched.
        embeddings_metadata: a dictionary which maps layer name to a file name
            in which metadata for this embedding layer is saved. See the
            [details](https://www.tensorflow.org/how_tos/embedding_viz/#metadata_optional)
            about metadata files format. In case if the same metadata file is
            used for all embedding layers, string can be passed.
    """

    def __init__(self, log_dir='./logs',
                 histogram_freq=0,
                 batch_size=32,
                 write_graph=True,
                 write_grads=False,
                 write_images=False,
                 write_batch_performance=False,
                 embeddings_freq=0,
                 embeddings_layer_names=None,
                 embeddings_metadata=None):
        super(TensorBoard, self).__init__()
        if K.backend() != 'tensorflow':
            raise RuntimeError('TensorBoard callback only works '
                               'with the TensorFlow backend.')
        self.log_dir = log_dir
        self.histogram_freq = histogram_freq
        self.merged = None
        self.write_graph = write_graph
        self.write_grads = write_grads
        self.write_images = write_images
        self.write_batch_performance = write_batch_performance
        self.embeddings_freq = embeddings_freq
        self.embeddings_layer_names = embeddings_layer_names
        self.embeddings_metadata = embeddings_metadata or {}
        self.batch_size = batch_size
        self.seen = 0

    def set_model(self, model):
        self.model = model
        self.sess = K.get_session()
        if self.histogram_freq and self.merged is None:
            for layer in self.model.layers:

                for weight in layer.weights:
                    tf.summary.histogram(weight.name, weight)
                    if self.write_grads:
                        grads = model.optimizer.get_gradients(model.total_loss,
                                                              weight)
                        tf.summary.histogram('{}_grad'.format(weight.name), grads)
                    if self.write_images:
                        w_img = tf.squeeze(weight)
                        shape = K.int_shape(w_img)
                        if len(shape) == 2:  # dense layer kernel case
                            if shape[0] > shape[1]:
                                w_img = tf.transpose(w_img)
                                shape = K.int_shape(w_img)
                            w_img = tf.reshape(w_img, [1,
                                                       shape[0],
                                                       shape[1],
                                                       1])
                        elif len(shape) == 3:  # convnet case
                            if K.image_data_format() == 'channels_last':
                                # switch to channels_first to display
                                # every kernel as a separate image
                                w_img = tf.transpose(w_img, perm=[2, 0, 1])
                                shape = K.int_shape(w_img)
                            w_img = tf.reshape(w_img, [shape[0],
                                                       shape[1],
                                                       shape[2],
                                                       1])
                        elif len(shape) == 1:  # bias case
                            w_img = tf.reshape(w_img, [1,
                                                       shape[0],
                                                       1,
                                                       1])
                        else:
                            # not possible to handle 3D convnets etc.
                            continue

                        shape = K.int_shape(w_img)
                        assert len(shape) == 4 and shape[-1] in [1, 3, 4]
                        tf.summary.image(weight.name, w_img)

                if hasattr(layer, 'output'):
                    tf.summary.histogram('{}_out'.format(layer.name),
                                         layer.output)
        self.merged = tf.summary.merge_all()

        if self.write_graph:
            self.writer = tf.summary.FileWriter(self.log_dir,
                                                self.sess.graph)
        else:
            self.writer = tf.summary.FileWriter(self.log_dir)

        if self.embeddings_freq:
            embeddings_layer_names = self.embeddings_layer_names

            if not embeddings_layer_names:
                embeddings_layer_names = [layer.name for layer in self.model.layers
                                          if type(layer).__name__ == 'Embedding']

            embeddings = {layer.name: layer.weights[0]
                          for layer in self.model.layers
                          if layer.name in embeddings_layer_names}

            self.saver = tf.train.Saver(list(embeddings.values()))

            embeddings_metadata = {}

            if not isinstance(self.embeddings_metadata, str):
                embeddings_metadata = self.embeddings_metadata
            else:
                embeddings_metadata = {layer_name: self.embeddings_metadata
                                       for layer_name in embeddings.keys()}

            config = projector.ProjectorConfig()
            self.embeddings_ckpt_path = os.path.join(self.log_dir,
                                                     'keras_embedding.ckpt')

            for layer_name, tensor in embeddings.items():
                embedding = config.embeddings.add()
                embedding.tensor_name = tensor.name

                if layer_name in embeddings_metadata:
                    embedding.metadata_path = embeddings_metadata[layer_name]

            projector.visualize_embeddings(self.writer, config)

    def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}
        if self.validation_data and self.histogram_freq:
            if epoch % self.histogram_freq == 0:

                val_data = self.validation_data
                tensors = (self.model.inputs +
                           self.model.targets +
                           self.model.sample_weights)

                if self.model.uses_learning_phase:
                    tensors += [K.learning_phase()]

                assert len(val_data) == len(tensors)
                val_size = val_data[0].shape[0]
                i = 0
                while i < val_size:
                    step = min(self.batch_size, val_size - i)
                    batch_val = []
                    batch_val.append(val_data[0][i:i + step])
                    batch_val.append(val_data[1][i:i + step])
                    batch_val.append(val_data[2][i:i + step])
                    if self.model.uses_learning_phase:
                        batch_val.append(val_data[3])
                    feed_dict = dict(zip(tensors, batch_val))
                    result = self.sess.run([self.merged], feed_dict=feed_dict)
                    summary_str = result[0]
                    self.writer.add_summary(summary_str, self.seen)
                    i += self.batch_size

        if self.embeddings_freq and self.embeddings_ckpt_path:
            if epoch % self.embeddings_freq == 0:
                self.saver.save(self.sess,
                                self.embeddings_ckpt_path,
                                epoch)

        for name, value in logs.items():
            if name in ['batch', 'size']:
                continue
            summary = tf.Summary()
            summary_value = summary.value.add()
            summary_value.simple_value = value.item()
            summary_value.tag = name
            self.writer.add_summary(summary, self.seen)
        self.writer.flush()
        self.seen += self.batch_size

    def on_train_end(self, _):
        self.writer.close()

    def on_batch_end(self, batch, logs=None):
        logs = logs or {}

        if self.write_batch_performance == True:
            for name, value in logs.items():
                if name in ['batch','size']:
                    continue
                summary = tf.Summary()
                summary_value = summary.value.add()
                summary_value.simple_value = value.item()
                summary_value.tag = name
                self.writer.add_summary(summary, self.seen)
            self.writer.flush()

        self.seen += self.batch_size


class ReduceLROnPlateau(Callback):
    """Reduce learning rate when a metric has stopped improving.

    Models often benefit from reducing the learning rate by a factor
    of 2-10 once learning stagnates. This callback monitors a
    quantity and if no improvement is seen for a 'patience' number
    of epochs, the learning rate is reduced.

    # Example
        ```python
        reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,
                                      patience=5, min_lr=0.001)
        model.fit(X_train, Y_train, callbacks=[reduce_lr])
        ```

    # Arguments
        monitor: quantity to be monitored.
        factor: factor by which the learning rate will
            be reduced. new_lr = lr * factor
        patience: number of epochs with no improvement
            after which learning rate will be reduced.
        verbose: int. 0: quiet, 1: update messages.
        mode: one of {auto, min, max}. In `min` mode,
            lr will be reduced when the quantity
            monitored has stopped decreasing; in `max`
            mode it will be reduced when the quantity
            monitored has stopped increasing; in `auto`
            mode, the direction is automatically inferred
            from the name of the monitored quantity.
        epsilon: threshold for measuring the new optimum,
            to only focus on significant changes.
        cooldown: number of epochs to wait before resuming
            normal operation after lr has been reduced.
        min_lr: lower bound on the learning rate.
    """

    def __init__(self, monitor='val_loss', factor=0.1, patience=10,
                 verbose=0, mode='auto', epsilon=1e-4, cooldown=0, min_lr=0):
        super(ReduceLROnPlateau, self).__init__()

        self.monitor = monitor
        if factor >= 1.0:
            raise ValueError('ReduceLROnPlateau '
                             'does not support a factor >= 1.0.')
        self.factor = factor
        self.min_lr = min_lr
        self.epsilon = epsilon
        self.patience = patience
        self.verbose = verbose
        self.cooldown = cooldown
        self.cooldown_counter = 0  # Cooldown counter.
        self.wait = 0
        self.best = 0
        self.mode = mode
        self.monitor_op = None
        self._reset()

    def _reset(self):
        """Resets wait counter and cooldown counter.
        """
        if self.mode not in ['auto', 'min', 'max']:
            warnings.warn('Learning Rate Plateau Reducing mode %s is unknown, '
                          'fallback to auto mode.' % (self.mode),
                          RuntimeWarning)
            self.mode = 'auto'
        if (self.mode == 'min' or
           (self.mode == 'auto' and 'acc' not in self.monitor)):
            self.monitor_op = lambda a, b: np.less(a, b - self.epsilon)
            self.best = np.Inf
        else:
            self.monitor_op = lambda a, b: np.greater(a, b + self.epsilon)
            self.best = -np.Inf
        self.cooldown_counter = 0
        self.wait = 0
        self.lr_epsilon = self.min_lr * 1e-4

    def on_train_begin(self, logs=None):
        self._reset()

    def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}
        logs['lr'] = K.get_value(self.model.optimizer.lr)
        current = logs.get(self.monitor)
        if current is None:
            warnings.warn('Learning Rate Plateau Reducing requires %s available!' %
                          self.monitor, RuntimeWarning)
        else:
            if self.in_cooldown():
                self.cooldown_counter -= 1
                self.wait = 0

            if self.monitor_op(current, self.best):
                self.best = current
                self.wait = 0
            elif not self.in_cooldown():
                if self.wait >= self.patience:
                    old_lr = float(K.get_value(self.model.optimizer.lr))
                    if old_lr > self.min_lr + self.lr_epsilon:
                        new_lr = old_lr * self.factor
                        new_lr = max(new_lr, self.min_lr)
                        K.set_value(self.model.optimizer.lr, new_lr)
                        if self.verbose > 0:
                            print('\nEpoch %05d: reducing learning rate to %s.' % (epoch, new_lr))
                        self.cooldown_counter = self.cooldown
                        self.wait = 0
                self.wait += 1

    def in_cooldown(self):
        return self.cooldown_counter > 0


class CSVLogger(Callback):
    """Callback that streams epoch results to a csv file.

    Supports all values that can be represented as a string,
    including 1D iterables such as np.ndarray.

    # Example
        ```python
        csv_logger = CSVLogger('training.log')
        model.fit(X_train, Y_train, callbacks=[csv_logger])
        ```

    # Arguments
        filename: filename of the csv file, e.g. 'run/log.csv'.
        separator: string used to separate elements in the csv file.
        append: True: append if file exists (useful for continuing
            training). False: overwrite existing file,
    """

    def __init__(self, filename, separator=',', append=False):
        self.sep = separator
        self.filename = filename
        self.append = append
        self.writer = None
        self.keys = None
        self.append_header = True
        self.file_flags = 'b' if six.PY2 and os.name == 'nt' else ''
        super(CSVLogger, self).__init__()

    def on_train_begin(self, logs=None):
        if self.append:
            if os.path.exists(self.filename):
                with open(self.filename, 'r' + self.file_flags) as f:
                    self.append_header = not bool(len(f.readline()))
            self.csv_file = open(self.filename, 'a' + self.file_flags)
        else:
            self.csv_file = open(self.filename, 'w' + self.file_flags)

    def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}

        def handle_value(k):
            is_zero_dim_ndarray = isinstance(k, np.ndarray) and k.ndim == 0
            if isinstance(k, six.string_types):
                return k
            elif isinstance(k, Iterable) and not is_zero_dim_ndarray:
                return '"[%s]"' % (', '.join(map(str, k)))
            else:
                return k

        if not self.writer:
            self.keys = sorted(logs.keys())

            class CustomDialect(csv.excel):
                delimiter = self.sep

            self.writer = csv.DictWriter(self.csv_file,
                                         fieldnames=['epoch'] + self.keys, dialect=CustomDialect)
            if self.append_header:
                self.writer.writeheader()

        row_dict = OrderedDict({'epoch': epoch})
        row_dict.update((key, handle_value(logs[key])) for key in self.keys)
        self.writer.writerow(row_dict)
        self.csv_file.flush()

    def on_train_end(self, logs=None):
        self.csv_file.close()
        self.writer = None


class LambdaCallback(Callback):
    """Callback for creating simple, custom callbacks on-the-fly.

    This callback is constructed with anonymous functions that will be called
    at the appropriate time. Note that the callbacks expects positional
    arguments, as:

     - `on_epoch_begin` and `on_epoch_end` expect two positional arguments:
        `epoch`, `logs`
     - `on_batch_begin` and `on_batch_end` expect two positional arguments:
        `batch`, `logs`
     - `on_train_begin` and `on_train_end` expect one positional argument:
        `logs`

    # Arguments
        on_epoch_begin: called at the beginning of every epoch.
        on_epoch_end: called at the end of every epoch.
        on_batch_begin: called at the beginning of every batch.
        on_batch_end: called at the end of every batch.
        on_train_begin: called at the beginning of model training.
        on_train_end: called at the end of model training.

    # Example
        ```python
        # Print the batch number at the beginning of every batch.
        batch_print_callback = LambdaCallback(
            on_batch_begin=lambda batch,logs: print(batch))

        # Plot the loss after every epoch.
        import numpy as np
        import matplotlib.pyplot as plt
        plot_loss_callback = LambdaCallback(
            on_epoch_end=lambda epoch, logs: plt.plot(np.arange(epoch),
                                                      logs['loss']))

        # Terminate some processes after having finished model training.
        processes = ...
        cleanup_callback = LambdaCallback(
            on_train_end=lambda logs: [
                p.terminate() for p in processes if p.is_alive()])

        model.fit(...,
                  callbacks=[batch_print_callback,
                             plot_loss_callback,
                             cleanup_callback])
        ```
    """

    def __init__(self,
                 on_epoch_begin=None,
                 on_epoch_end=None,
                 on_batch_begin=None,
                 on_batch_end=None,
                 on_train_begin=None,
                 on_train_end=None,
                 **kwargs):
        super(LambdaCallback, self).__init__()
        self.__dict__.update(kwargs)
        if on_epoch_begin is not None:
            self.on_epoch_begin = on_epoch_begin
        else:
            self.on_epoch_begin = lambda epoch, logs: None
        if on_epoch_end is not None:
            self.on_epoch_end = on_epoch_end
        else:
            self.on_epoch_end = lambda epoch, logs: None
        if on_batch_begin is not None:
            self.on_batch_begin = on_batch_begin
        else:
            self.on_batch_begin = lambda batch, logs: None
        if on_batch_end is not None:
            self.on_batch_end = on_batch_end
        else:
            self.on_batch_end = lambda batch, logs: None
        if on_train_begin is not None:
            self.on_train_begin = on_train_begin
        else:
            self.on_train_begin = lambda logs: None
        if on_train_end is not None:
            self.on_train_end = on_train_end
        else:
            self.on_train_end = lambda logs: None