callbacks.py

"""Callbacks: utilities called at certain points during model training.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import csv
import six

import numpy as np
import time
import json
import warnings
import io

import datetime

from collections import deque
from collections import OrderedDict
from collections import Iterable
from collections import defaultdict
from keras.utils.generic_utils import Progbar
from keras import backend as K
from keras.engine.training_utils import standardize_input_data

try:
    import requests
except ImportError:
    requests = None


_TRAIN = 'train'
_TEST = 'test'
_PREDICT = 'predict'


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
        self.params = {}
        self.model = None
        self._reset_batch_timing()

    def _reset_batch_timing(self):
        self._delta_t_batch = 0.
        self._delta_ts = defaultdict(lambda: deque([], maxlen=self.queue_length))

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

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

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

    def _call_batch_hook(self, mode, hook, batch, logs=None):
        """Helper function for all batch_{begin | end} methods."""
        if not self.callbacks:
            return
        hook_name = 'on_{mode}_batch_{hook}'.format(mode=mode, hook=hook)
        if hook == 'end':
            if not hasattr(self, '_t_enter_batch'):
                self._t_enter_batch = time.time()
            # Batch is ending, calculate batch time
            self._delta_t_batch = time.time() - self._t_enter_batch

        logs = logs or {}
        t_before_callbacks = time.time()
        for callback in self.callbacks:
            batch_hook = getattr(callback, hook_name)
            batch_hook(batch, logs)
        self._delta_ts[hook_name].append(time.time() - t_before_callbacks)

        delta_t_median = np.median(self._delta_ts[hook_name])
        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 (%s) is slow compared '
                'to the batch update (%f). Check your callbacks.'
                % (hook_name, delta_t_median), RuntimeWarning)

        if hook == 'begin':
            self._t_enter_batch = time.time()

    def _call_begin_hook(self, mode):
        """Helper function for on_{train|test|predict}_begin methods."""
        if mode == _TRAIN:
            self.on_train_begin()
        elif mode == _TEST:
            self.on_test_begin()
        else:
            self.on_predict_begin()

    def _call_end_hook(self, mode):
        """Helper function for on_{train|test|predict}_end methods."""
        if mode == _TRAIN:
            self.on_train_end()
        elif mode == _TEST:
            self.on_test_end()
        else:
            self.on_predict_end()

    def on_batch_begin(self, batch, logs=None):
        self._call_batch_hook(_TRAIN, 'begin', batch, logs=logs)

    def on_batch_end(self, batch, logs=None):
        self._call_batch_hook(_TRAIN, 'end', batch, logs=logs)

    def on_epoch_begin(self, epoch, logs=None):
        """Calls the `on_epoch_begin` methods of its callbacks.

        This function should only be called during train mode.

        # Arguments
            epoch: integer, index of epoch.
            logs: dict, Currently no data is passed to this argument for this method
                but that may change in the future.
        """
        logs = logs or {}
        for callback in self.callbacks:
            callback.on_epoch_begin(epoch, logs)
        self._reset_batch_timing()

    def on_epoch_end(self, epoch, logs=None):
        """Calls the `on_epoch_end` methods of its callbacks.

        This function should only be called during train mode.

        # Arguments
            epoch: integer, index of epoch.
            logs: dict, metric results for this training epoch, and for the
                validation epoch if validation is performed. Validation result keys
                are prefixed with `val_`.
        """
        logs = logs or {}
        for callback in self.callbacks:
            callback.on_epoch_end(epoch, logs)

    def on_train_batch_begin(self, batch, logs=None):
        """Calls the `on_train_batch_begin` methods of its callbacks.

        # Arguments
            batch: integer, index of batch within the current epoch.
            logs: dict, has keys `batch` and `size` representing the current
                batch number and the size of the batch.
        """
        self._call_batch_hook(_TRAIN, 'begin', batch, logs=logs)

    def on_train_batch_end(self, batch, logs=None):
        """Calls the `on_train_batch_end` methods of its callbacks.

        # Arguments
            batch: integer, index of batch within the current epoch.
            logs: dict, metric results for this batch.
        """
        self._call_batch_hook(_TRAIN, 'end', batch, logs=logs)

    def on_test_batch_begin(self, batch, logs=None):
        """Calls the `on_test_batch_begin` methods of its callbacks.

        # Arguments
            batch: integer, index of batch within the current epoch.
            logs: dict, has keys `batch` and `size` representing the current
                batch number and the size of the batch.
        """
        self._call_batch_hook(_TEST, 'begin', batch, logs=logs)

    def on_test_batch_end(self, batch, logs=None):
        """Calls the `on_test_batch_end` methods of its callbacks.

        # Arguments
            batch: integer, index of batch within the current epoch.
            logs: dict, metric results for this batch.
        """
        self._call_batch_hook(_TEST, 'end', batch, logs=logs)

    def on_predict_batch_begin(self, batch, logs=None):
        """Calls the `on_predict_batch_begin` methods of its callbacks.

        # Arguments
            batch: integer, index of batch within the current epoch.
            logs: dict, has keys `batch` and `size` representing the current
                batch number and the size of the batch.
        """
        self._call_batch_hook(_PREDICT, 'begin', batch, logs=logs)

    def on_predict_batch_end(self, batch, logs=None):
        """Calls the `on_predict_batch_end` methods of its callbacks.

        # Argument
            batch: integer, index of batch within the current epoch.
            logs: dict, metric results for this batch.
        """
        self._call_batch_hook(_PREDICT, 'end', batch, logs=logs)

    def on_train_begin(self, logs=None):
        """Calls the `on_train_begin` methods of its callbacks.

        # Arguments
            logs: dict, currently no data is passed to this argument for this method
                but that may change in the future.
        """
        for callback in self.callbacks:
            callback.on_train_begin(logs)

    def on_train_end(self, logs=None):
        """Calls the `on_train_end` methods of its callbacks.

        # Arguments
            logs: dict, currently no data is passed to this argument for this method
                but that may change in the future.
        """
        for callback in self.callbacks:
            callback.on_train_end(logs)

    def on_test_begin(self, logs=None):
        """Calls the `on_test_begin` methods of its callbacks.

        # Arguments
            logs: dict, currently no data is passed to this argument for this method
                but that may change in the future.
        """
        for callback in self.callbacks:
            callback.on_test_begin(logs)

    def on_test_end(self, logs=None):
        """Calls the `on_test_end` methods of its callbacks.

        # Arguments
            logs: dict, currently no data is passed to this argument for this method
                but that may change in the future.
        """
        for callback in self.callbacks:
            callback.on_test_end(logs)

    def on_predict_begin(self, logs=None):
        """Calls the `on_predict_begin` methods of its callbacks.

        # Arguments
            logs: dict, currently no data is passed to this argument for this method
                but that may change in the future.
        """
        for callback in self.callbacks:
            callback.on_predict_begin(logs)

    def on_predict_end(self, logs=None):
        """Calls the `on_predict_end` methods of its callbacks.

        # Arguments
            logs: dict, currently no data is passed to this argument for this method
                but that may change in the future.
        """
        for callback in self.callbacks:
            callback.on_predict_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
        self.model = None

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

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

    def on_batch_begin(self, batch, logs=None):
        """A backwards compatibility alias for `on_train_batch_begin`."""

    def on_batch_end(self, batch, logs=None):
        """A backwards compatibility alias for `on_train_batch_end`."""

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

        Subclasses should override for any actions to run. This function should only
        be called during train mode.

        # Arguments
            epoch: integer, index of epoch.
            logs: dict, currently no data is passed to this argument for this method
                but that may change in the future.
        """

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

        Subclasses should override for any actions to run. This function should only
        be called during train mode.

        # Arguments
            epoch: integer, index of epoch.
            logs: dict, metric results for this training epoch, and for the
                validation epoch if validation is performed. Validation result keys
                are prefixed with `val_`.
        """

    def on_train_batch_begin(self, batch, logs=None):
        """Called at the beginning of a training batch in `fit` methods.

        Subclasses should override for any actions to run.

        # Arguments
            batch: integer, index of batch within the current epoch.
            logs: dict, has keys `batch` and `size` representing the current
                batch number and the size of the batch.
        """
        # For backwards compatibility
        self.on_batch_begin(batch, logs=logs)

    def on_train_batch_end(self, batch, logs=None):
        """Called at the end of a training batch in `fit` methods.

        Subclasses should override for any actions to run.

        # Arguments
            batch: integer, index of batch within the current epoch.
            logs: dict, metric results for this batch.
        """
        # For backwards compatibility
        self.on_batch_end(batch, logs=logs)

    def on_test_batch_begin(self, batch, logs=None):
        """Called at the beginning of a batch in `evaluate` methods.

        Also called at the beginning of a validation batch in the `fit` methods,
        if validation data is provided.

        Subclasses should override for any actions to run.

        # Arguments
            batch: integer, index of batch within the current epoch.
            logs: dict, has keys `batch` and `size` representing the current
                batch number and the size of the batch.
        """

    def on_test_batch_end(self, batch, logs=None):
        """Called at the end of a batch in `evaluate` methods.

        Also called at the end of a validation batch in the `fit` methods,
        if validation data is provided.

        Subclasses should override for any actions to run.

        # Arguments
            batch: integer, index of batch within the current epoch.
            logs: dict, metric results for this batch.
        """

    def on_predict_batch_begin(self, batch, logs=None):
        """Called at the beginning of a batch in `predict` methods.

        Subclasses should override for any actions to run.

        # Arguments
            batch: integer, index of batch within the current epoch.
            logs: dict, has keys `batch` and `size` representing the current
                batch number and the size of the batch.
        """

    def on_predict_batch_end(self, batch, logs=None):
        """Called at the end of a batch in `predict` methods.

        Subclasses should override for any actions to run.

        # Arguments
            batch: integer, index of batch within the current epoch.
            logs: dict, metric results for this batch.
        """

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

        Subclasses should override for any actions to run.

        # Arguments
            logs: dict, currently no data is passed to this argument for this method
                but that may change in the future.
        """

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

        Subclasses should override for any actions to run.

        # Arguments
            logs: dict, currently no data is passed to this argument for this method
                but that may change in the future.
        """

    def on_test_begin(self, logs=None):
        """Called at the beginning of evaluation or validation.

        Subclasses should override for any actions to run.

        # Arguments
            logs: dict, currently no data is passed to this argument for this method
                but that may change in the future.
        """

    def on_test_end(self, logs=None):
        """Called at the end of evaluation or validation.

        Subclasses should override for any actions to run.

        # Arguments
            logs: dict, currently no data is passed to this argument for this method
                but that may change in the future.
        """

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

        Subclasses should override for any actions to run.

        # Arguments
            logs: dict, currently no data is passed to this argument for this method
                but that may change in the future.
        """

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

        Subclasses should override for any actions to run.

        # Arguments
            logs: dict, currently no data is passed to this argument for this method
                but that may change in the future.
        """


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

    This callback is automatically applied to every Keras model.

    # Arguments
        stateful_metrics: Iterable of string names of metrics that
            should *not* be averaged over an epoch.
            Metrics in this list will be logged as-is in `on_epoch_end`.
            All others will be averaged in `on_epoch_end`.
    """

    def __init__(self, stateful_metrics=None):
        if stateful_metrics:
            self.stateful_metrics = set(stateful_metrics)
        else:
            self.stateful_metrics = set()

    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.stateful_metrics:
                self.totals[k] = v
            else:
                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.
                    if k in self.stateful_metrics:
                        logs[k] = self.totals[k]
                    else:
                        logs[k] = self.totals[k] / self.seen


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

    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 seen or steps (batches) seen.
        stateful_metrics: Iterable of string names of metrics that
            should *not* be averaged over an epoch.
            Metrics in this list will be logged as-is.
            All others will be averaged over time (e.g. loss, etc).

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

    def __init__(self, count_mode='samples',
                 stateful_metrics=None):
        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))
        if stateful_metrics:
            self.stateful_metrics = set(stateful_metrics)
        else:
            self.stateful_metrics = set()

    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,
                                   stateful_metrics=self.stateful_metrics)
        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)


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 with the values 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.
        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)`).
        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.
        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 + 1, **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('\nEpoch %05d: %s improved from %0.5f to %0.5f,'
                                  ' saving model to %s'
                                  % (epoch + 1, 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('\nEpoch %05d: %s did not improve from %0.5f' %
                                  (epoch + 1, self.monitor, self.best))
            else:
                if self.verbose > 0:
                    print('\nEpoch %05d: saving model to %s' % (epoch + 1, 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.
        baseline: Baseline value for the monitored quantity to reach.
            Training will stop if the model doesn't show improvement
            over the baseline.
        restore_best_weights: whether to restore model weights from
            the epoch with the best value of the monitored quantity.
            If False, the model weights obtained at the last step of
            training are used.
    """

    def __init__(self,
                 monitor='val_loss',
                 min_delta=0,
                 patience=0,
                 verbose=0,
                 mode='auto',
                 baseline=None,
                 restore_best_weights=False):
        super(EarlyStopping, self).__init__()

        self.monitor = monitor
        self.baseline = baseline
        self.patience = patience
        self.verbose = verbose
        self.min_delta = min_delta
        self.wait = 0
        self.stopped_epoch = 0
        self.restore_best_weights = restore_best_weights
        self.best_weights = None

        if mode not in ['auto', 'min', 'max']:
            warnings.warn('EarlyStopping mode %s is unknown, '
                          'fallback to auto mode.' % 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:
                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
        if self.baseline is not None:
            self.best = self.baseline
        else:
            self.best = np.Inf if self.monitor_op == np.less else -np.Inf

    def on_epoch_end(self, epoch, logs=None):
        current = self.get_monitor_value(logs)
        if current is None:
            return

        if self.monitor_op(current - self.min_delta, self.best):
            self.best = current
            self.wait = 0
            if self.restore_best_weights:
                self.best_weights = self.model.get_weights()
        else:
            self.wait += 1
            if self.wait >= self.patience:
                self.stopped_epoch = epoch
                self.model.stop_training = True
                if self.restore_best_weights:
                    if self.verbose > 0:
                        print('Restoring model weights from the end of '
                              'the best epoch')
                    self.model.set_weights(self.best_weights)

    def on_train_end(self, logs=None):
        if self.stopped_epoch > 0 and self.verbose > 0:
            print('Epoch %05d: early stopping' % (self.stopped_epoch + 1))

    def get_monitor_value(self, logs):
        monitor_value = logs.get(self.monitor)
        if monitor_value is None:
            warnings.warn(
                'Early stopping conditioned on metric `%s` '
                'which is not available. Available metrics are: %s' %
                (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning
            )
        return monitor_value


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.
    If send_as_json is set to True, the content type of the request will be
    application/json. Otherwise the serialized JSON will be send within a form

    # 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.
            The field is used only if the payload is sent within a form
            (i.e. send_as_json is set to False).
        headers: Dictionary; optional custom HTTP headers.
        send_as_json: Boolean; whether the request should be send as
            application/json.
    """

    def __init__(self,
                 root='http://localhost:9000',
                 path='/publish/epoch/end/',
                 field='data',
                 headers=None,
                 send_as_json=False):
        super(RemoteMonitor, self).__init__()

        self.root = root
        self.path = path
        self.field = field
        self.headers = headers
        self.send_as_json = send_as_json

    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():
            if isinstance(v, (np.ndarray, np.generic)):
                send[k] = v.item()
            else:
                send[k] = v
        try:
            if self.send_as_json:
                requests.post(self.root + self.path, json=send, headers=self.headers)
            else:
                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 current learning rate
            and returns a new learning rate as output (float).
        verbose: int. 0: quiet, 1: update messages.
    """

    def __init__(self, schedule, verbose=0):
        super(LearningRateScheduler, self).__init__()
        self.schedule = schedule
        self.verbose = verbose

    def on_epoch_begin(self, epoch, logs=None):
        if not hasattr(self.model.optimizer, 'lr'):
            raise ValueError('Optimizer must have a "lr" attribute.')
        lr = float(K.get_value(self.model.optimizer.lr))
        try:  # new API
            lr = self.schedule(epoch, lr)
        except TypeError:  # old API for backward compatibility
            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)
        if self.verbose > 0:
            print('\nEpoch %05d: LearningRateScheduler setting learning '
                  'rate to %s.' % (epoch + 1, lr))

    def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}
        logs['lr'] = K.get_value(self.model.optimizer.lr)


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

    [TensorBoard](https://www.tensorflow.org/guide/summaries_and_tensorboard)
    is a visualization tool provided with TensorFlow.

    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.

    If you have installed TensorFlow with pip, you should be able
    to launch TensorBoard from the command line:
    ```sh
    tensorboard --logdir=/full_path_to_your_logs
    ```

    When using a backend other than TensorFlow, TensorBoard will still work
    (if you have TensorFlow installed), but the only feature available will
    be the display of the losses and metrics plots.

    # 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.
        batch_size: size of batch of inputs to feed to the network
            for histograms computation.
        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.
        write_images: whether to write model weights to visualize as
            image in TensorBoard.
        embeddings_freq: frequency (in epochs) at which selected embedding
            layers will be saved. If set to 0, embeddings won't be computed.
            Data to be visualized in TensorBoard's Embedding tab must be passed
            as `embeddings_data`.
        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/guide/embedding#metadata)
            about metadata files format. In case if the same metadata file is
            used for all embedding layers, string can be passed.
        embeddings_data: data to be embedded at layers specified in
            `embeddings_layer_names`. Numpy array (if the model has a single
            input) or list of Numpy arrays (if the model has multiple inputs).
            Learn [more about embeddings](
            https://www.tensorflow.org/guide/embedding).
        update_freq: `'batch'` or `'epoch'` or integer. When using `'batch'`, writes
            the losses and metrics to TensorBoard after each batch. The same
            applies for `'epoch'`. If using an integer, let's say `10000`,
            the callback will write the metrics and losses to TensorBoard every
            10000 samples. Note that writing too frequently to TensorBoard
            can slow down your training.
    """

    def __init__(self, log_dir='./logs',
                 histogram_freq=0,
                 batch_size=32,
                 write_graph=True,
                 write_grads=False,
                 write_images=False,
                 embeddings_freq=0,
                 embeddings_layer_names=None,
                 embeddings_metadata=None,
                 embeddings_data=None,
                 update_freq='epoch'):
        super(TensorBoard, self).__init__()
        global tf, projector
        try:
            import tensorflow as tf
            from tensorflow.contrib.tensorboard.plugins import projector
        except ImportError:
            raise ImportError('You need the TensorFlow module installed to '
                              'use TensorBoard.')

        if K.backend() != 'tensorflow':
            if histogram_freq != 0:
                warnings.warn('You are not using the TensorFlow backend. '
                              'histogram_freq was set to 0')
                histogram_freq = 0
            if write_graph:
                warnings.warn('You are not using the TensorFlow backend. '
                              'write_graph was set to False')
                write_graph = False
            if write_images:
                warnings.warn('You are not using the TensorFlow backend. '
                              'write_images was set to False')
                write_images = False
            if embeddings_freq != 0:
                warnings.warn('You are not using the TensorFlow backend. '
                              'embeddings_freq was set to 0')
                embeddings_freq = 0

        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.embeddings_freq = embeddings_freq
        self.embeddings_layer_names = embeddings_layer_names
        self.embeddings_metadata = embeddings_metadata or {}
        self.batch_size = batch_size
        self.embeddings_data = embeddings_data
        if update_freq == 'batch':
            # It is the same as writing as frequently as possible.
            self.update_freq = 1
        else:
            self.update_freq = update_freq
        self.samples_seen = 0
        self.samples_seen_at_last_write = 0

    def set_model(self, model):
        self.model = model
        if K.backend() == 'tensorflow':
            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:
                    mapped_weight_name = weight.name.replace(':', '_')
                    tf.summary.histogram(mapped_weight_name, weight)
                    if self.write_grads and weight in layer.trainable_weights:
                        grads = model.optimizer.get_gradients(model.total_loss,
                                                              weight)

                        def is_indexed_slices(grad):
                            return type(grad).__name__ == 'IndexedSlices'
                        grads = [
                            grad.values if is_indexed_slices(grad) else grad
                            for grad in grads]
                        tf.summary.histogram('{}_grad'.format(mapped_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(mapped_weight_name, w_img)

                if hasattr(layer, 'output'):
                    if isinstance(layer.output, list):
                        for i, output in enumerate(layer.output):
                            tf.summary.histogram('{}_out_{}'.format(layer.name, i),
                                                 output)
                    else:
                        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 and self.embeddings_data is not None:
            self.embeddings_data = standardize_input_data(self.embeddings_data,
                                                          model.input_names)

            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']
            self.assign_embeddings = []
            embeddings_vars = {}

            self.batch_id = batch_id = tf.placeholder(tf.int32)
            self.step = step = tf.placeholder(tf.int32)

            for layer in self.model.layers:
                if layer.name in embeddings_layer_names:
                    embedding_input = self.model.get_layer(layer.name).output
                    embedding_size = np.prod(embedding_input.shape[1:])
                    embedding_input = tf.reshape(embedding_input,
                                                 (step, int(embedding_size)))
                    shape = (self.embeddings_data[0].shape[0], int(embedding_size))
                    embedding = tf.Variable(tf.zeros(shape),
                                            name=layer.name + '_embedding')
                    embeddings_vars[layer.name] = embedding
                    batch = tf.assign(embedding[batch_id:batch_id + step],
                                      embedding_input)
                    self.assign_embeddings.append(batch)

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

            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_vars.keys()}

            config = projector.ProjectorConfig()

            for layer_name, tensor in embeddings_vars.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 not self.validation_data and self.histogram_freq:
            raise ValueError("If printing histograms, validation_data must be "
                             "provided, and cannot be a generator.")
        if self.embeddings_data is None and self.embeddings_freq:
            raise ValueError("To visualize embeddings, embeddings_data must "
                             "be provided.")
        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)
                    if self.model.uses_learning_phase:
                        # do not slice the learning phase
                        batch_val = [x[i:i + step] for x in val_data[:-1]]
                        batch_val.append(val_data[-1])
                    else:
                        batch_val = [x[i:i + step] for x in val_data]
                    assert len(batch_val) == len(tensors)
                    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, epoch)
                    i += self.batch_size

        if self.embeddings_freq and self.embeddings_data is not None:
            if epoch % self.embeddings_freq == 0:
                # We need a second forward-pass here because we're passing
                # the `embeddings_data` explicitly. This design allows to pass
                # arbitrary data as `embeddings_data` and results from the fact
                # that we need to know the size of the `tf.Variable`s which
                # hold the embeddings in `set_model`. At this point, however,
                # the `validation_data` is not yet set.

                # More details in this discussion:
                # https://github.com/keras-team/keras/pull/7766#issuecomment-329195622

                embeddings_data = self.embeddings_data
                n_samples = embeddings_data[0].shape[0]

                i = 0
                while i < n_samples:
                    step = min(self.batch_size, n_samples - i)
                    batch = slice(i, i + step)

                    if type(self.model.input) == list:
                        feed_dict = {_input: embeddings_data[idx][batch]
                                     for idx, _input in enumerate(self.model.input)}
                    else:
                        feed_dict = {self.model.input: embeddings_data[0][batch]}

                    feed_dict.update({self.batch_id: i, self.step: step})

                    if self.model.uses_learning_phase:
                        feed_dict[K.learning_phase()] = False

                    self.sess.run(self.assign_embeddings, feed_dict=feed_dict)
                    self.saver.save(self.sess,
                                    os.path.join(self.log_dir,
                                                 'keras_embedding.ckpt'),
                                    epoch)

                    i += self.batch_size

        if self.update_freq == 'epoch':
            index = epoch
        else:
            index = self.samples_seen
        self._write_logs(logs, index)

    def _write_logs(self, logs, index):
        for name, value in logs.items():
            if name in ['batch', 'size']:
                continue
            summary = tf.Summary()
            summary_value = summary.value.add()
            if isinstance(value, np.ndarray):
                summary_value.simple_value = value.item()
            else:
                summary_value.simple_value = value
            summary_value.tag = name
            self.writer.add_summary(summary, index)
        self.writer.flush()

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

    def on_batch_end(self, batch, logs=None):
        if self.update_freq != 'epoch':
            self.samples_seen += logs['size']
            samples_seen_since = self.samples_seen - self.samples_seen_at_last_write
            if samples_seen_since >= self.update_freq:
                self._write_logs(logs, self.samples_seen)
                self.samples_seen_at_last_write = self.samples_seen


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.
        min_delta: 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', min_delta=1e-4, cooldown=0, min_lr=0,
                 **kwargs):
        super(ReduceLROnPlateau, self).__init__()

        self.monitor = monitor
        if factor >= 1.0:
            raise ValueError('ReduceLROnPlateau '
                             'does not support a factor >= 1.0.')
        if 'epsilon' in kwargs:
            min_delta = kwargs.pop('epsilon')
            warnings.warn('`epsilon` argument is deprecated and '
                          'will be removed, use `min_delta` instead.')
        self.factor = factor
        self.min_lr = min_lr
        self.min_delta = min_delta
        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.min_delta)
            self.best = np.Inf
        else:
            self.monitor_op = lambda a, b: np.greater(a, b + self.min_delta)
            self.best = -np.Inf
        self.cooldown_counter = 0
        self.wait = 0

    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(
                'Reduce LR on plateau conditioned on metric `%s` '
                'which is not available. Available metrics are: %s' %
                (self.monitor, ','.join(list(logs.keys()))), 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():
                self.wait += 1
                if self.wait >= self.patience:
                    old_lr = float(K.get_value(self.model.optimizer.lr))
                    if old_lr > self.min_lr:
                        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: ReduceLROnPlateau reducing '
                                  'learning rate to %s.' % (epoch + 1, new_lr))
                        self.cooldown_counter = self.cooldown
                        self.wait = 0

    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
        if six.PY2:
            self.file_flags = 'b'
            self._open_args = {}
        else:
            self.file_flags = ''
            self._open_args = {'newline': '\n'}
        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()))
            mode = 'a'
        else:
            mode = 'w'
        self.csv_file = io.open(self.filename,
                                mode + self.file_flags,
                                **self._open_args)

    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 self.keys is None:
            self.keys = sorted(logs.keys())

        if self.model.stop_training:
            # We set NA so that csv parsers do not fail for this last epoch.
            logs = dict([(k, logs[k] if k in logs else 'NA') for k in self.keys])

        if not self.writer:
            class CustomDialect(csv.excel):
                delimiter = self.sep
            fieldnames = ['epoch'] + self.keys
            if six.PY2:
                fieldnames = [unicode(x) for x in fieldnames]
            self.writer = csv.DictWriter(self.csv_file,
                                         fieldnames=fieldnames,
                                         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):
    r"""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))

    # Stream the epoch loss to a file in JSON format. The file content
    # is not well-formed JSON but rather has a JSON object per line.
    import json
    json_log = open('loss_log.json', mode='wt', buffering=1)
    json_logging_callback = LambdaCallback(
        on_epoch_end=lambda epoch, logs: json_log.write(
            json.dumps({'epoch': epoch, 'loss': logs['loss']}) + '\n'),
        on_train_end=lambda logs: json_log.close()
    )

    # 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,
                         json_logging_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


class MakeLRGreatAgain(Callback):
    """Increase learning rate when the loss function seems to be converged.

    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.
    
    Reducing the learning rate by a factor as in ReduceLROnPlateau seems to 
    be the way to stagnate the learning. However, this callback pushes the
    Learning Rate up high again, to make sure we don't get more improvements
    on our training. Making Learning Rate great again. 

    # Example

    ```python
    increase_lr = MakeLRGreatAgain(monitor='val_loss', factor_min=10, factor_max=1000,
                                  patience=10, min_lr=0.001)
    model.fit(X_train, Y_train, callbacks=[increase_lr])
    ```

    # Arguments
        monitor: quantity to be monitored.
        
        factor_min: minimum int to give the numpy.random.randint function.
            this throws a random factor by which the learning rate will
            be increased. new_lr = lr * factor
        factor_max: maximum int to give the numpy.random.randint function.
            this throws a random factor by which the learning rate will
            be increased. 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.
        min_delta: 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. When learning rate is less
            than min_lr, this callback pushes the Learning Rate up again.
    """

    def __init__(self, monitor='val_loss', factor_min=10, factor_max=1000, patience=10,
                 verbose=0, mode='auto', min_delta=1e-4, cooldown=0, min_lr=0, # min_delta might need to be increased (!)
                 **kwargs):
        super(MakeLRGreatAgain, self).__init__()

        self.monitor = monitor
        if factor_min <= 1.0:
            raise ValueError('MakeLRGreatAgain '
                             'does not support a factor_min <= 1.0 as you\'re not increasing the Learning Rate!')
        if factor_max <= 2.0:
            raise ValueError('MakeLRGreatAgain '
                             'does not support a factor_max <= 2.0 as you\'re not increasing the Learning Rate!')
        if 'epsilon' in kwargs:
            min_delta = kwargs.pop('epsilon')
            warnings.warn('`epsilon` argument is deprecated and '
                          'will be removed, use `min_delta` instead.')


        self.factor_min = factor_min
        self.factor_max = factor_max
        self.min_lr = min_lr
        self.min_delta = min_delta
        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 increasing 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.min_delta)
            self.best = np.Inf
        else:
            self.monitor_op = lambda a, b: np.greater(a, b + self.min_delta)
            self.best = -np.Inf
        self.cooldown_counter = 0
        self.wait = 0

    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(
                'Increase LR conditioned on metric `%s` '
                'which is not available. Available metrics are: %s' %
                (self.monitor, ','.join(list(logs.keys()))), 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():
                self.wait += 1
                if self.wait >= self.patience:
                    old_lr = float(K.get_value(self.model.optimizer.lr))
                    if old_lr <= self.min_lr:
                        new_lr = old_lr * np.random.randint(low=self.factor_min, high=self.factor_max, size=None, dtype='l')
                        new_lr = max(new_lr, self.min_lr)
                        K.set_value(self.model.optimizer.lr, new_lr)
                        if self.verbose > 0:
                            print('\nEpoch %05d: MakeLRGreatAgain increasing '
                                  'learning rate to %s.' % (epoch + 1, new_lr))
                        self.cooldown_counter = self.cooldown
                        self.wait = 0

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


class BetterCSVLogger(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
        if six.PY2:
            self.file_flags = 'b'
            self._open_args = {}
        else:
            self.file_flags = ''
            self._open_args = {'newline': '\n'}
        super(BetterCSVLogger, 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()))
            mode = 'a'
        else:
            mode = 'w'
        self.csv_file = io.open(self.filename,
                                mode + self.file_flags,
                                **self._open_args)

    def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}
        logs['date'] = str(datetime.datetime.now())

        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 self.keys is None:
            self.keys = sorted(logs.keys())

        if self.model.stop_training:
            # We set NA so that csv parsers do not fail for this last epoch.
            logs = dict([(k, logs[k] if k in logs else 'NA') for k in self.keys])

        if not self.writer:
            class CustomDialect(csv.excel):
                delimiter = self.sep
            fieldnames = ['epoch'] + self.keys
            if six.PY2:
                fieldnames = [unicode(x) for x in fieldnames]
            self.writer = csv.DictWriter(self.csv_file,
                                         fieldnames=fieldnames,
                                         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