# -*- coding: utf-8 -*-
"""Training-related part of the Keras engine.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import warnings
import copy
import numpy as np
from scipy.sparse import issparse

from .topology import Container
from .. import backend as K
from .. import optimizers
from .. import losses
from .. import metrics as metrics_module
from ..utils.data_utils import Sequence
from ..utils.data_utils import GeneratorEnqueuer
from ..utils.data_utils import OrderedEnqueuer
from ..utils.generic_utils import Progbar
from .. import callbacks as cbks
from ..legacy import interfaces


def _standardize_input_data(data, names, shapes=None,
                            check_batch_axis=True,
                            exception_prefix=''):
    """Normalizes inputs and targets provided by users.

    Users may pass data as a list of arrays, dictionary of arrays,
    or as a single array. We normalize this to an ordered list of
    arrays (same order as `names`), while checking that the provided
    arrays have shapes that match the network's expectations.

    # Arguments
        data: User-provided input data (polymorphic).
        names: List of expected array names.
        shapes: Optional list of expected array shapes.
        check_batch_axis: Boolean; whether to check that
            the batch axis of the arrays matches the expected
            value found in `shapes`.
        exception_prefix: String prefix used for exception formatting.

    # Returns
        List of standardized input arrays (one array per model input).

    # Raises
        ValueError: in case of improperly formatted user-provided data.
    """
    if not names:
        if data is not None and hasattr(data, '__len__') and len(data):
            raise ValueError('Error when checking model ' +
                             exception_prefix + ': '
                             'expected no data, but got:', data)
        return []
    if data is None:
        return [None for _ in range(len(names))]

    if isinstance(data, dict):
        try:
            data = [data[x].values if data[x].__class__.__name__ == 'DataFrame' else data[x] for x in names]
            data = [np.expand_dims(x, 1) if x.ndim == 1 else x for x in data]
        except KeyError as e:
            raise ValueError(
                'No data provided for "' + e.args[0] + '". Need data '
                'for each key in: ' + str(names))
    elif isinstance(data, list):
        data = [x.values if x.__class__.__name__ == 'DataFrame' else x for x in data]
        data = [np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data]
    else:
        data = data.values if data.__class__.__name__ == 'DataFrame' else data
        data = [np.expand_dims(data, 1)] if data.ndim == 1 else [data]

    if len(data) != len(names):
        if data and hasattr(data[0], 'shape'):
            raise ValueError(
                'Error when checking model ' + exception_prefix +
                ': the list of Numpy arrays that you are passing to '
                'your model is not the size the model expected. '
                'Expected to see ' + str(len(names)) + ' array(s), '
                'but instead got the following list of ' +
                str(len(data)) + ' arrays: ' + str(data)[:200] + '...')
        elif len(names) > 1:
            raise ValueError(
                'Error when checking model ' + exception_prefix +
                ': you are passing a list as input to your model, '
                'but the model expects a list of ' + str(len(names)) +
                ' Numpy arrays instead. The list you passed was: ' +
                str(data)[:200])
        elif len(data) == 1 and not hasattr(data[0], 'shape'):
            raise TypeError(
                'Error when checking model ' + exception_prefix +
                ': data should be a Numpy array, or list/dict of '
                'Numpy arrays. Found: ' + str(data)[:200] + '...')
        elif len(names) == 1:
            data = [np.asarray(data)]

    # Check shapes compatibility.
    if shapes:
        for i in range(len(names)):
            if shapes[i] is not None:
                data_shape = data[i].shape
                shape = shapes[i]
                if data[i].ndim != len(shape):
                    raise ValueError(
                        'Error when checking ' + exception_prefix +
                        ': expected ' + names[i] + ' to have ' +
                        str(len(shape)) + ' dimensions, but got array '
                        'with shape ' + str(data_shape))
                if not check_batch_axis:
                    data_shape = data_shape[1:]
                    shape = shape[1:]
                for dim, ref_dim in zip(data_shape, shape):
                    if ref_dim != dim and ref_dim:
                        raise ValueError(
                            'Error when checking ' + exception_prefix +
                            ': expected ' + names[i] + ' to have shape ' +
                            str(shape) + ' but got array with shape ' +
                            str(data_shape))
    return data


def _standardize_sample_or_class_weights(x_weight, output_names, weight_type):
    """Maps `sample_weight` or `class_weight` to model outputs.

    # Arguments
        x_weight: User-provided `sample_weight` or `class_weight` argument.
        output_names: List of output names (strings) in the model.
        weight_type: A string used purely for exception printing.

    # Returns
        A list of `sample_weight` or `class_weight` where there are exactly
            one element per model output.

    # Raises
        ValueError: In case of invalid user-provided argument.
    """
    if x_weight is None or len(x_weight) == 0:
        return [None for _ in output_names]
    if len(output_names) == 1:
        if isinstance(x_weight, list) and len(x_weight) == 1:
            return x_weight
        if isinstance(x_weight, dict) and output_names[0] in x_weight:
            return [x_weight[output_names[0]]]
        else:
            return [x_weight]
    if isinstance(x_weight, list):
        if len(x_weight) != len(output_names):
            raise ValueError('Provided `' + weight_type + '` was a list of ' +
                             str(len(x_weight)) +
                             ' elements, but the model has ' +
                             str(len(output_names)) + ' outputs. '
                             'You should provide one `' + weight_type + '`'
                             'array per model output.')
        return x_weight
    if isinstance(x_weight, dict):
        x_weights = []
        for name in output_names:
            x_weights.append(x_weight.get(name))
        return x_weights
    else:
        raise TypeError('The model has multiple outputs, so `' +
                        weight_type + '` '
                        'should be either a list or a dict. '
                        'Provided `' + weight_type +
                        '` type not understood: ' +
                        str(x_weight))


def _standardize_class_weights(class_weight, output_names):
    return _standardize_sample_or_class_weights(class_weight,
                                                output_names,
                                                'class_weight')


def _standardize_sample_weights(sample_weight, output_names):
    return _standardize_sample_or_class_weights(sample_weight,
                                                output_names,
                                                'sample_weight')


def _check_array_lengths(inputs, targets, weights=None):
    """Does user input validation for numpy arrays.

    # Arguments
        inputs: list of Numpy arrays of inputs.
        targets: list of Numpy arrays of targets.
        weights: list of Numpy arrays of sample weights.

    # Raises
        ValueError: in case of incorrectly formatted data.
    """
    def set_of_lengths(x):
        # return a set with the variation between
        # different shapes, with None => 0
        if x is None:
            return {0}
        else:
            return set([0 if y is None else y.shape[0] for y in x])

    set_x = set_of_lengths(inputs)
    set_y = set_of_lengths(targets)
    set_w = set_of_lengths(weights)
    if len(set_x) > 1:
        raise ValueError('All input arrays (x) should have '
                         'the same number of samples. Got array shapes: ' +
                         str([x.shape for x in inputs]))
    if len(set_y) > 1:
        raise ValueError('All target arrays (y) should have '
                         'the same number of samples. Got array shapes: ' +
                         str([y.shape for y in targets]))
    if set_x and set_y and list(set_x)[0] != list(set_y)[0]:
        raise ValueError('Input arrays should have '
                         'the same number of samples as target arrays. '
                         'Found ' + str(list(set_x)[0]) + ' input samples '
                         'and ' + str(list(set_y)[0]) + ' target samples.')
    if len(set_w) > 1:
        raise ValueError('All sample_weight arrays should have '
                         'the same number of samples. Got array shapes: ' +
                         str([w.shape for w in weights]))
    if set_y and set_w and list(set_y)[0] != list(set_w)[0]:
        raise ValueError('Sample_weight arrays should have '
                         'the same number of samples as target arrays. Got ' +
                         str(list(set_y)[0]) + ' input samples and ' +
                         str(list(set_w)[0]) + ' target samples.')


def _check_loss_and_target_compatibility(targets, loss_fns, output_shapes):
    """Does validation on the compatibility of targets and loss functions.

    This helps prevent users from using loss functions incorrectly.

    # Arguments
        targets: list of Numpy arrays of targets.
        loss_fns: list of loss functions.
        output_shapes: list of shapes of model outputs.

    # Raises
        ValueError: if a loss function or target array
            is incompatible with an output.
    """
    key_losses = {losses.mean_squared_error,
                  losses.binary_crossentropy,
                  losses.categorical_crossentropy}
    for y, loss, shape in zip(targets, loss_fns, output_shapes):
        if loss is None:
            continue
        if loss is losses.categorical_crossentropy:
            if y.shape[-1] == 1:
                raise ValueError(
                    'You are passing a target array of shape ' + str(y.shape) +
                    ' while using as loss `categorical_crossentropy`. '
                    '`categorical_crossentropy` expects '
                    'targets to be binary matrices (1s and 0s) '
                    'of shape (samples, classes). '
                    'If your targets are integer classes, '
                    'you can convert them to the expected format via:\n'
                    '```\n'
                    'from keras.utils import to_categorical\n'
                    'y_binary = to_categorical(y_int)\n'
                    '```\n'
                    '\n'
                    'Alternatively, you can use the loss function '
                    '`sparse_categorical_crossentropy` instead, '
                    'which does expect integer targets.')
        if loss in key_losses:
            for target_dim, out_dim in zip(y.shape[1:], shape[1:]):
                if out_dim is not None and target_dim != out_dim:
                    raise ValueError(
                        'A target array with shape ' + str(y.shape) +
                        ' was passed for an output of shape ' + str(shape) +
                        ' while using as loss `' + loss.__name__ + '`. '
                        'This loss expects '
                        'targets to have the same shape '
                        'as the output.')


def _collect_metrics(metrics, output_names):
    """Maps metric functions to model outputs.

    # Arguments
        metrics: a list or dict of metric functions.
        output_names: a list of the names (strings) of model outputs.

    # Returns
        A list (one entry per model output) of lists of metric functions.
        For instance, if the model has 2 outputs, and for the first output
        we want to compute "binary_accuracy" and "binary_crossentropy",
        and just "binary_accuracy" for the second output,
        the list would look like:
            `[[binary_accuracy, binary_crossentropy], [binary_accuracy]]`

    # Raises
        TypeError: if an incorrect type is passed for the `metrics` argument.
    """
    if not metrics:
        return [[] for _ in output_names]
    if isinstance(metrics, list):
        # we then apply all metrics to all outputs.
        return [copy.copy(metrics) for _ in output_names]
    elif isinstance(metrics, dict):
        nested_metrics = []
        for name in output_names:
            output_metrics = metrics.get(name, [])
            if not isinstance(output_metrics, list):
                output_metrics = [output_metrics]
            nested_metrics.append(output_metrics)
        return nested_metrics
    else:
        raise TypeError('Type of `metrics` argument not understood. '
                        'Expected a list or dictionary, found: ' +
                        str(metrics))


def _batch_shuffle(index_array, batch_size):
    """Shuffles an array in a batch-wise fashion.

    Useful for shuffling HDF5 arrays
    (where one cannot access arbitrary indices).

    # Arguments
        index_array: array of indices to be shuffled.
        batch_size: integer.

    # Returns
        The `index_array` array, shuffled in a batch-wise fashion.
    """
    batch_count = int(len(index_array) / batch_size)
    # to reshape we need to be cleanly divisible by batch size
    # we stash extra items and reappend them after shuffling
    last_batch = index_array[batch_count * batch_size:]
    index_array = index_array[:batch_count * batch_size]
    index_array = index_array.reshape((batch_count, batch_size))
    np.random.shuffle(index_array)
    index_array = index_array.flatten()
    return np.append(index_array, last_batch)


def _make_batches(size, batch_size):
    """Returns a list of batch indices (tuples of indices).

    # Arguments
        size: Integer, total size of the data to slice into batches.
        batch_size: Integer, batch size.

    # Returns
        A list of tuples of array indices.
    """
    num_batches = (size + batch_size - 1) // batch_size  # round up
    return [(i * batch_size, min(size, (i + 1) * batch_size))
            for i in range(num_batches)]


def _slice_arrays(arrays, start=None, stop=None):
    """Slice an array or list of arrays.

    This takes an array-like, or a list of
    array-likes, and outputs:
        - arrays[start:stop] if `arrays` is an array-like
        - [x[start:stop] for x in arrays] if `arrays` is a list

    Can also work on list/array of indices: `_slice_arrays(x, indices)`

    # Arguments
        arrays: Single array or list of arrays.
        start: can be an integer index (start index)
            or a list/array of indices
        stop: integer (stop index); should be None if
            `start` was a list.

    # Returns
        A slice of the array(s).
    """
    if arrays is None:
        return [None]
    elif isinstance(arrays, list):
        if hasattr(start, '__len__'):
            # hdf5 datasets only support list objects as indices
            if hasattr(start, 'shape'):
                start = start.tolist()
            return [None if x is None else x[start] for x in arrays]
        else:
            return [None if x is None else x[start:stop] for x in arrays]
    else:
        if hasattr(start, '__len__'):
            if hasattr(start, 'shape'):
                start = start.tolist()
            return arrays[start]
        elif hasattr(start, '__getitem__'):
            return arrays[start:stop]
        else:
            return [None]


def _weighted_masked_objective(fn):
    """Adds support for masking and sample-weighting to an objective function.

    It transforms an objective function `fn(y_true, y_pred)`
    into a sample-weighted, cost-masked objective function
    `fn(y_true, y_pred, weights, mask)`.

    # Arguments
        fn: The objective function to wrap,
            with signature `fn(y_true, y_pred)`.

    # Returns
        A function with signature `fn(y_true, y_pred, weights, mask)`.
    """
    if fn is None:
        return None

    def weighted(y_true, y_pred, weights, mask=None):
        """Wrapper function.

        # Arguments
            y_true: `y_true` argument of `fn`.
            y_pred: `y_pred` argument of `fn`.
            weights: Weights tensor.
            mask: Mask tensor.

        # Returns
            Scalar tensor.
        """
        # score_array has ndim >= 2
        score_array = fn(y_true, y_pred)
        if mask is not None:
            # Cast the mask to floatX to avoid float64 upcasting in theano
            mask = K.cast(mask, K.floatx())
            # mask should have the same shape as score_array
            score_array *= mask
            #  the loss per batch should be proportional
            #  to the number of unmasked samples.
            score_array /= K.mean(mask)

        # apply sample weighting
        if weights is not None:
            # reduce score_array to same ndim as weight array
            ndim = K.ndim(score_array)
            weight_ndim = K.ndim(weights)
            score_array = K.mean(score_array, axis=list(range(weight_ndim, ndim)))
            score_array *= weights
            score_array /= K.mean(K.cast(K.not_equal(weights, 0), K.floatx()))
        return K.mean(score_array)
    return weighted


def _standardize_weights(y, sample_weight=None, class_weight=None,
                         sample_weight_mode=None):
    """Performs sample weight validation and standardization.

    Everything gets normalized to a single sample-wise (or timestep-wise)
    weight array.

    # Arguments
        y: Numpy array of model targets to be weighted.
        sample_weight: User-provided `sample_weight` argument.
        class_weight: User-provided `class_weight` argument.
        sample_weight_mode: One of `None` or `"temporal"`.
            `"temporal"` indicated that we expect 2D weight data
            that will be applied to the last 2 dimensions of
            the targets (i.e. we are weighting timesteps, not samples).

    # Returns
        A numpy array of target weights, one entry per sample to weight.

    # Raises
        ValueError: In case of invalid user-provided arguments.
    """
    if sample_weight_mode is not None:
        if sample_weight_mode != 'temporal':
            raise ValueError('"sample_weight_mode '
                             'should be None or "temporal". '
                             'Found: ' + str(sample_weight_mode))
        if len(y.shape) < 3:
            raise ValueError('Found a sample_weight array for '
                             'an input with shape ' +
                             str(y.shape) + '. '
                             'Timestep-wise sample weighting (use of '
                             'sample_weight_mode="temporal") is restricted to '
                             'outputs that are at least 3D, i.e. that have '
                             'a time dimension.')
        if sample_weight is not None and len(sample_weight.shape) != 2:
            raise ValueError('Found a sample_weight array with shape ' +
                             str(sample_weight.shape) + '. '
                             'In order to use timestep-wise sample weighting, '
                             'you should pass a 2D sample_weight array.')
    else:
        if sample_weight is not None and len(sample_weight.shape) != 1:
            raise ValueError('Found a sample_weight array with shape ' +
                             str(sample_weight.shape) + '. '
                             'In order to use timestep-wise sample weights, '
                             'you should specify '
                             'sample_weight_mode="temporal" '
                             'in compile(). If you just mean to use '
                             'sample-wise weights, make sure your '
                             'sample_weight array is 1D.')

    if sample_weight is not None:
        if len(sample_weight.shape) > len(y.shape):
            raise ValueError('Found a sample_weight with shape' +
                             str(sample_weight.shape) + '.'
                             'Expected sample_weight with rank '
                             'less than or equal to ' + str(len(y.shape)))

        if y.shape[:sample_weight.ndim] != sample_weight.shape:
            raise ValueError('Found a sample_weight array with shape ' +
                             str(sample_weight.shape) + ' for an input with shape ' +
                             str(y.shape) + '. '
                             'sample_weight cannot be broadcast.')
        return sample_weight
    elif isinstance(class_weight, dict):
        if len(y.shape) > 2:
            raise ValueError('`class_weight` not supported for '
                             '3+ dimensional targets.')
        if y.shape[1] > 1:
            y_classes = y.argmax(axis=1)
        elif y.shape[1] == 1:
            y_classes = np.reshape(y, y.shape[0])
        else:
            y_classes = y

        weights = np.asarray([class_weight[cls] for cls in y_classes
                              if cls in class_weight])

        if len(weights) != len(y_classes):
            # subtract the sets to pick all missing classes
            existing_classes = set(y_classes)
            existing_class_weight = set(class_weight.keys())
            raise ValueError('`class_weight` must contain all classes in the data.'
                             ' The classes %s exist in the data but not in '
                             '`class_weight`.'
                             % (existing_classes - existing_class_weight))
        return weights
    else:
        if sample_weight_mode is None:
            return np.ones((y.shape[0],), dtype=K.floatx())
        else:
            return np.ones((y.shape[0], y.shape[1]), dtype=K.floatx())


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

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

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

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

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

        # Prepare output masks.
        masks = self.compute_mask(self.inputs, mask=None)
        if masks is None:
            masks = [None for _ in self.outputs]
        if not isinstance(masks, list):
            masks = [masks]

        # Prepare loss weights.
        if loss_weights is None:
            loss_weights_list = [1. for _ in range(len(self.outputs))]
        elif isinstance(loss_weights, dict):
            for name in loss_weights:
                if name not in self.output_names:
                    raise ValueError('Unknown entry in loss_weights '
                                     'dictionary: "' + name + '". '
                                     'Only expected the following keys: ' +
                                     str(self.output_names))
            loss_weights_list = []
            for name in self.output_names:
                loss_weights_list.append(loss_weights.get(name, 1.))
        elif isinstance(loss_weights, list):
            if len(loss_weights) != len(self.outputs):
                raise ValueError('When passing a list as loss_weights, '
                                 'it should have one entry per model output. '
                                 'The model has ' + str(len(self.outputs)) +
                                 ' outputs, but you passed loss_weights=' +
                                 str(loss_weights))
            loss_weights_list = loss_weights
        else:
            raise TypeError('Could not interpret loss_weights argument: ' +
                            str(loss_weights) +
                            ' - expected a list of dicts.')

        # Prepare targets of model.
        self.targets = []
        self._feed_targets = []
        if target_tensors is not None:
            if isinstance(target_tensors, list):
                if len(target_tensors) != len(self.outputs):
                    raise ValueError(
                        'When passing a list as `target_tensors`, '
                        'it should have one entry per model output. '
                        'The model has ' + str(len(self.outputs)) +
                        ' outputs, but you passed target_tensors=' +
                        str(target_tensors))
            elif isinstance(target_tensors, dict):
                for name in target_tensors:
                    if name not in self.output_names:
                        raise ValueError('Unknown entry in `target_tensors` '
                                         'dictionary: "' + name + '". '
                                         'Only expected the following keys: ' +
                                         str(self.output_names))
                tmp_target_tensors = []
                for name in self.output_names:
                    tmp_target_tensors.append(target_tensors.get(name, None))
                target_tensors = tmp_target_tensors
            else:
                raise TypeError('Expected `target_tensors` to be '
                                'a list or dict, but got:', target_tensors)
        for i in range(len(self.outputs)):
            if i in skip_target_indices:
                self.targets.append(None)
            else:
                shape = self._internal_output_shapes[i]
                name = self.output_names[i]
                if target_tensors is not None:
                    target = target_tensors[i]
                else:
                    target = None
                if target is None or K.is_placeholder(target):
                    if target is None:
                        target = K.placeholder(ndim=len(shape),
                                               name=name + '_target',
                                               sparse=K.is_sparse(self.outputs[i]),
                                               dtype=K.dtype(self.outputs[i]))
                    self._feed_targets.append(target)
                    self._feed_outputs.append(self.outputs[i])
                    self._feed_output_names.append(name)
                    self._feed_output_shapes.append(shape)
                    self._feed_loss_fns.append(self.loss_functions[i])
                else:
                    skip_target_weighing_indices.append(i)
                self.targets.append(target)

        # Prepare sample weights.
        sample_weights = []
        sample_weight_modes = []
        if isinstance(sample_weight_mode, dict):
            for name in sample_weight_mode:
                if name not in self.output_names:
                    raise ValueError('Unknown entry in '
                                     'sample_weight_mode dictionary: "' +
                                     name + '". '
                                     'Only expected the following keys: ' +
                                     str(self.output_names))
            for i, name in enumerate(self.output_names):
                if i in skip_target_weighing_indices:
                    weight = None
                    sample_weight_modes.append(None)
                else:
                    if name not in sample_weight_mode:
                        raise ValueError('Output "' + name +
                                         '" missing from sample_weight_modes '
                                         'dictionary')
                    if sample_weight_mode.get(name) == 'temporal':
                        weight = K.placeholder(ndim=2,
                                               name=name + '_sample_weights')
                        sample_weight_modes.append('temporal')
                    else:
                        weight = K.placeholder(ndim=1,
                                               name=name + '_sample_weights')
                        sample_weight_modes.append(None)
                sample_weights.append(weight)
        elif isinstance(sample_weight_mode, list):
            if len(sample_weight_mode) != len(self.outputs):
                raise ValueError('When passing a list as sample_weight_mode, '
                                 'it should have one entry per model output. '
                                 'The model has ' + str(len(self.outputs)) +
                                 ' outputs, but you passed '
                                 'sample_weight_mode=' +
                                 str(sample_weight_mode))
            for i in range(len(self.output_names)):
                if i in skip_target_weighing_indices:
                    weight = None
                    sample_weight_modes.append(None)
                else:
                    mode = sample_weight_mode[i]
                    name = self.output_names[i]
                    if mode == 'temporal':
                        weight = K.placeholder(ndim=2,
                                               name=name + '_sample_weights')
                        sample_weight_modes.append('temporal')
                    else:
                        weight = K.placeholder(ndim=1,
                                               name=name + '_sample_weights')
                        sample_weight_modes.append(None)
                sample_weights.append(weight)
        else:
            for i, name in enumerate(self.output_names):
                if i in skip_target_weighing_indices:
                    sample_weight_modes.append(None)
                    sample_weights.append(None)
                else:
                    if sample_weight_mode == 'temporal':
                        sample_weights.append(
                            K.placeholder(ndim=2,
                                          name=name + '_sample_weights'))
                        sample_weight_modes.append('temporal')
                    else:
                        sample_weights.append(
                            K.placeholder(ndim=1,
                                          name=name + '_sample_weights'))
                        sample_weight_modes.append(None)
        self.sample_weight_modes = sample_weight_modes
        self._feed_sample_weight_modes = []
        for i in range(len(self.outputs)):
            if i not in skip_target_weighing_indices:
                self._feed_sample_weight_modes.append(self.sample_weight_modes[i])

        # Prepare metrics.
        self.metrics = metrics
        self.weighted_metrics = weighted_metrics
        self.metrics_names = ['loss']
        self.metrics_tensors = []

        # Compute total loss.
        total_loss = None
        with K.name_scope('loss'):
            for i in range(len(self.outputs)):
                if i in skip_target_indices:
                    continue
                y_true = self.targets[i]
                y_pred = self.outputs[i]
                weighted_loss = weighted_losses[i]
                sample_weight = sample_weights[i]
                mask = masks[i]
                loss_weight = loss_weights_list[i]
                with K.name_scope(self.output_names[i] + '_loss'):
                    output_loss = weighted_loss(y_true, y_pred,
                                                sample_weight, mask)
                if len(self.outputs) > 1:
                    self.metrics_tensors.append(output_loss)
                    self.metrics_names.append(self.output_names[i] + '_loss')
                if total_loss is None:
                    total_loss = loss_weight * output_loss
                else:
                    total_loss += loss_weight * output_loss
            if total_loss is None:
                if not self.losses:
                    raise ValueError('The model cannot be compiled '
                                     'because it has no loss to optimize.')
                else:
                    total_loss = 0.

            # Add regularization penalties
            # and other layer-specific losses.
            for loss_tensor in self.losses:
                total_loss += loss_tensor

        # List of same size as output_names.
        # contains tuples (metrics for output, names of metrics).
        nested_metrics = _collect_metrics(metrics, self.output_names)
        nested_weighted_metrics = _collect_metrics(weighted_metrics, self.output_names)

        def append_metric(layer_index, metric_name, metric_tensor):
            """Helper function used in loop below."""
            if len(self.output_names) > 1:
                metric_name = self.output_names[layer_index] + '_' + metric_name
            self.metrics_names.append(metric_name)
            self.metrics_tensors.append(metric_tensor)

        with K.name_scope('metrics'):
            for i in range(len(self.outputs)):
                if i in skip_target_indices:
                    continue

                y_true = self.targets[i]
                y_pred = self.outputs[i]
                weights = sample_weights[i]
                output_metrics = nested_metrics[i]
                output_weighted_metrics = nested_weighted_metrics[i]

                def handle_metrics(metrics, weights=None):
                    metric_name_prefix = 'weighted_' if weights is not None else ''

                    for metric in metrics:
                        if metric in ('accuracy', 'acc', 'crossentropy', 'ce'):
                            # custom handling of accuracy/crossentropy
                            # (because of class mode duality)
                            output_shape = self._internal_output_shapes[i]
                            if (output_shape[-1] == 1 or
                               self.loss_functions[i] == losses.binary_crossentropy):
                                # case: binary accuracy/crossentropy
                                if metric in ('accuracy', 'acc'):
                                    acc_fn = metrics_module.binary_accuracy
                                elif metric in ('crossentropy', 'ce'):
                                    acc_fn = metrics_module.binary_crossentropy
                            elif self.loss_functions[i] == losses.sparse_categorical_crossentropy:
                                # case: categorical accuracy/crossentropy with sparse targets
                                if metric in ('accuracy', 'acc'):
                                    acc_fn = metrics_module.sparse_categorical_accuracy
                                elif metric in ('crossentropy', 'ce'):
                                    acc_fn = metrics_module.sparse_categorical_crossentropy
                            else:
                                # case: categorical accuracy/crossentropy
                                if metric in ('accuracy', 'acc'):
                                    acc_fn = metrics_module.categorical_accuracy
                                elif metric in ('crossentropy', 'ce'):
                                    acc_fn = metrics_module.categorical_crossentropy
                            if metric in ('accuracy', 'acc'):
                                    suffix = 'acc'
                            elif metric in ('crossentropy', 'ce'):
                                    suffix = 'ce'
                            weighted_metric_fn = _weighted_masked_objective(acc_fn)
                            metric_name = metric_name_prefix + suffix
                        else:
                            metric_fn = metrics_module.get(metric)
                            weighted_metric_fn = _weighted_masked_objective(metric_fn)
                            metric_name = metric_name_prefix + metric_fn.__name__

                        with K.name_scope(metric_name):
                            metric_result = weighted_metric_fn(y_true, y_pred,
                                                               weights=weights,
                                                               mask=masks[i])
                        append_metric(i, metric_name, metric_result)

                handle_metrics(output_metrics)
                handle_metrics(output_weighted_metrics, weights=weights)

        # Prepare gradient updates and state updates.
        self.total_loss = total_loss
        self.sample_weights = sample_weights
        self._feed_sample_weights = []
        for i in range(len(self.sample_weights)):
            if i not in skip_target_weighing_indices:
                self._feed_sample_weights.append(sample_weights[i])

        # Functions for train, test and predict will
        # be compiled lazily when required.
        # This saves time when the user is not using all functions.
        self._function_kwargs = kwargs

        self.train_function = None
        self.test_function = None
        self.predict_function = None

        # Collected trainable weights, sorted in topological order.
        trainable_weights = self.trainable_weights
        self._collected_trainable_weights = trainable_weights

    def _check_trainable_weights_consistency(self):
        """Check trainable weights count consistency.

        This will raise a warning if `trainable_weights` and
        `_collected_trainable_weights` are inconsistent (i.e. have different
        number of parameters).
        Inconsistency will typically arise when one modifies `model.trainable`
        without calling `model.compile` again.
        """
        if not hasattr(self, '_collected_trainable_weights'):
            return

        if (len(self.trainable_weights) !=
                len(self._collected_trainable_weights)):
            warnings.warn(UserWarning(
                'Discrepancy between trainable weights and collected trainable'
                ' weights, did you set `model.trainable` without calling'
                ' `model.compile` after ?'))

    def _make_train_function(self):
        if not hasattr(self, 'train_function'):
            raise RuntimeError('You must compile your model before using it.')
        self._check_trainable_weights_consistency()
        if self.train_function is None:
            inputs = self._feed_inputs + self._feed_targets + self._feed_sample_weights
            if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
                inputs += [K.learning_phase()]

            with K.name_scope('training'):
                with K.name_scope(self.optimizer.__class__.__name__):
                    training_updates = self.optimizer.get_updates(
                        params=self._collected_trainable_weights,
                        loss=self.total_loss)
                updates = self.updates + training_updates
                # Gets loss and metrics. Updates weights at each call.
                self.train_function = K.function(inputs,
                                                 [self.total_loss] + self.metrics_tensors,
                                                 updates=updates,
                                                 name='train_function',
                                                 **self._function_kwargs)

    def _make_test_function(self):
        if not hasattr(self, 'test_function'):
            raise RuntimeError('You must compile your model before using it.')
        if self.test_function is None:
            inputs = self._feed_inputs + self._feed_targets + self._feed_sample_weights
            if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
                inputs += [K.learning_phase()]
            # Return loss and metrics, no gradient updates.
            # Does update the network states.
            self.test_function = K.function(inputs,
                                            [self.total_loss] + self.metrics_tensors,
                                            updates=self.state_updates,
                                            name='test_function',
                                            **self._function_kwargs)

    def _make_predict_function(self):
        if not hasattr(self, 'predict_function'):
            self.predict_function = None
        if self.predict_function is None:
            if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
                inputs = self._feed_inputs + [K.learning_phase()]
            else:
                inputs = self._feed_inputs
            # Gets network outputs. Does not update weights.
            # Does update the network states.
            kwargs = getattr(self, '_function_kwargs', {})
            self.predict_function = K.function(inputs,
                                               self.outputs,
                                               updates=self.state_updates,
                                               name='predict_function',
                                               **kwargs)

    def _check_num_samples(self, ins, batch_size=None, steps=None, steps_name='steps'):
        """Determine the number of samples provided for training and evaluation.

        The number of samples is not defined when running with `steps`,
        in which case the number of samples is set to `None`.

        # Arguments
            ins: List of tensors to be fed to the Keras function.
            batch_size: Integer batch size or `None` if not defined.
            steps: Total number of steps (batches of samples)
                before declaring `_predict_loop` finished.
                Ignored with the default value of `None`.
            steps_name: The public API's parameter name for `steps`.

        # Raises
            ValueError: when `steps` is `None` and the attribute `ins.shape`
            does not exist. Also raises ValueError when `steps` is not `None`
            and `batch_size` is not `None` because they are mutually
            exclusive.

        # Returns
            When steps is `None`, returns the number of samples to be
            processed based on the size of the first dimension of the
            first input numpy array. When steps is not `None` and
            `batch_size` is `None`, returns `None`.

        # Raises
            ValueError: In case of invalid arguments.
        """
        if steps is not None:
            num_samples = None
            if batch_size is not None:
                raise ValueError('If ' + steps_name +
                                 ' is set, the `batch_size` must be None.')
        elif ins and hasattr(ins[0], 'shape'):
            num_samples = ins[0].shape[0]
        else:
            raise ValueError('Either the input data should have '
                             'a defined shape, or ' + steps_name +
                             ' should be specified.')
        return num_samples

    def _fit_loop(self, f, ins, out_labels=None, batch_size=None,
                  epochs=100, verbose=1, callbacks=None,
                  val_f=None, val_ins=None, shuffle=True,
                  callback_metrics=None, initial_epoch=0,
                  steps_per_epoch=None, validation_steps=None):
        """Abstract fit function for `f(ins)`.

        Assume that f returns a list, labeled by out_labels.

        # Arguments
            f: Keras function returning a list of tensors
            ins: List of tensors to be fed to `f`
            out_labels: List of strings, display names of
                the outputs of `f`
            batch_size: Integer batch size or None if unknown.
            epochs: Number of times to iterate over the data
            verbose: Verbosity mode, 0, 1 or 2
            callbacks: List of callbacks to be called during training
            val_f: Keras function to call for validation
            val_ins: List of tensors to be fed to `val_f`
            shuffle: Whether to shuffle the data at the beginning of each epoch
            callback_metrics: List of strings, the display names of the metrics
                passed to the callbacks. They should be the
                concatenation of list the display names of the outputs of
                 `f` and the list of display names of the outputs of `f_val`.
            initial_epoch: Epoch at which to start training
                (useful for resuming a previous training run)
            steps_per_epoch: Total number of steps (batches of samples)
                before declaring one epoch finished and starting the
                next epoch. Ignored with the default value of `None`.
            validation_steps: Number of steps to run validation for
                (only if doing validation from data tensors).
                Ignored with the default value of `None`.

        # Returns
            `History` object.
        """
        do_validation = False
        if val_f and val_ins:
            do_validation = True
            if verbose and ins and hasattr(ins[0], 'shape') and hasattr(val_ins[0], 'shape'):
                print('Train on %d samples, validate on %d samples' %
                      (ins[0].shape[0], val_ins[0].shape[0]))
        if validation_steps:
            do_validation = True
            if steps_per_epoch is None:
                raise ValueError('Can only use `validation_steps` '
                                 'when doing step-wise '
                                 'training, i.e. `steps_per_epoch` '
                                 'must be set.')

        num_train_samples = self._check_num_samples(ins, batch_size,
                                                    steps_per_epoch,
                                                    'steps_per_epoch')
        if num_train_samples is not None:
            index_array = np.arange(num_train_samples)

        self.history = cbks.History()
        callbacks = [cbks.BaseLogger()] + (callbacks or []) + [self.history]
        if verbose:
            if steps_per_epoch is not None:
                count_mode = 'steps'
            else:
                count_mode = 'samples'
            callbacks += [cbks.ProgbarLogger(count_mode)]
        callbacks = cbks.CallbackList(callbacks)
        out_labels = out_labels or []

        # it's possible to callback a different model than self
        # (used by Sequential models)
        if hasattr(self, 'callback_model') and self.callback_model:
            callback_model = self.callback_model
        else:
            callback_model = self

        callbacks.set_model(callback_model)
        callbacks.set_params({
            'batch_size': batch_size,
            'epochs': epochs,
            'steps': steps_per_epoch,
            'samples': num_train_samples,
            'verbose': verbose,
            'do_validation': do_validation,
            'metrics': callback_metrics or [],
        })
        callbacks.on_train_begin()
        callback_model.stop_training = False
        for cbk in callbacks:
            cbk.validation_data = val_ins

        # To prevent a slowdown, we find beforehand the arrays that need conversion.
        feed = self._feed_inputs + self._feed_targets + self._feed_sample_weights
        indices_for_conversion_to_dense = []
        for i in range(len(feed)):
            if issparse(ins[i]) and not K.is_sparse(feed[i]):
                indices_for_conversion_to_dense.append(i)

        for epoch in range(initial_epoch, epochs):
            callbacks.on_epoch_begin(epoch)
            epoch_logs = {}
            if steps_per_epoch is not None:
                for step_index in range(steps_per_epoch):
                    batch_logs = {}
                    batch_logs['batch'] = step_index
                    batch_logs['size'] = 1
                    callbacks.on_batch_begin(step_index, batch_logs)
                    outs = f(ins)

                    if not isinstance(outs, list):
                        outs = [outs]
                    for l, o in zip(out_labels, outs):
                        batch_logs[l] = o

                    callbacks.on_batch_end(step_index, batch_logs)
                    if callback_model.stop_training:
                        break

                if do_validation:
                    val_outs = self._test_loop(val_f, val_ins,
                                               batch_size=batch_size,
                                               steps=validation_steps,
                                               verbose=0)
                    if not isinstance(val_outs, list):
                        val_outs = [val_outs]
                    # Same labels assumed.
                    for l, o in zip(out_labels, val_outs):
                        epoch_logs['val_' + l] = o
            else:
                if shuffle == 'batch':
                    index_array = _batch_shuffle(index_array, batch_size)
                elif shuffle:
                    np.random.shuffle(index_array)

                batches = _make_batches(num_train_samples, batch_size)
                for batch_index, (batch_start, batch_end) in enumerate(batches):
                    batch_ids = index_array[batch_start:batch_end]
                    try:
                        if isinstance(ins[-1], float):
                            # Do not slice the training phase flag.
                            ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
                        else:
                            ins_batch = _slice_arrays(ins, batch_ids)
                    except TypeError:
                        raise TypeError('TypeError while preparing batch. '
                                        'If using HDF5 input data, '
                                        'pass shuffle="batch".')
                    batch_logs = {}
                    batch_logs['batch'] = batch_index
                    batch_logs['size'] = len(batch_ids)
                    callbacks.on_batch_begin(batch_index, batch_logs)
                    for i in indices_for_conversion_to_dense:
                        ins_batch[i] = ins_batch[i].toarray()

                    outs = f(ins_batch)
                    if not isinstance(outs, list):
                        outs = [outs]
                    for l, o in zip(out_labels, outs):
                        batch_logs[l] = o

                    callbacks.on_batch_end(batch_index, batch_logs)
                    if callback_model.stop_training:
                        break

                    if batch_index == len(batches) - 1:  # Last batch.
                        if do_validation:
                            val_outs = self._test_loop(val_f, val_ins,
                                                       batch_size=batch_size,
                                                       verbose=0)
                            if not isinstance(val_outs, list):
                                val_outs = [val_outs]
                            # Same labels assumed.
                            for l, o in zip(out_labels, val_outs):
                                epoch_logs['val_' + l] = o
            callbacks.on_epoch_end(epoch, epoch_logs)
            if callback_model.stop_training:
                break
        callbacks.on_train_end()
        return self.history

    def _predict_loop(self, f, ins, batch_size=32, verbose=0, steps=None):
        """Abstract method to loop over some data in batches.

        # Arguments
            f: Keras function returning a list of tensors.
            ins: list of tensors to be fed to `f`.
            batch_size: integer batch size.
            verbose: verbosity mode.
            steps: Total number of steps (batches of samples)
                before declaring `_predict_loop` finished.
                Ignored with the default value of `None`.

        # Returns
            Array of predictions (if the model has a single output)
            or list of arrays of predictions
            (if the model has multiple outputs).
        """
        num_samples = self._check_num_samples(ins, batch_size,
                                              steps,
                                              'steps')
        if verbose == 1:
            if steps is not None:
                progbar = Progbar(target=steps)
            else:
                progbar = Progbar(target=num_samples)

        indices_for_conversion_to_dense = []
        for i in range(len(self._feed_inputs)):
            if issparse(ins[i]) and not K.is_sparse(self._feed_inputs[i]):
                indices_for_conversion_to_dense.append(i)

        if steps is not None:
            # Step-based predictions.
            # Since we do not know how many samples
            # we will see, we cannot pre-allocate
            # the returned Numpy arrays.
            # Instead, we store one array per batch seen
            # and concatenate them upon returning.
            unconcatenated_outs = []
            for step in range(steps):
                batch_outs = f(ins)
                if not isinstance(batch_outs, list):
                    batch_outs = [batch_outs]
                if step == 0:
                    for batch_out in batch_outs:
                        unconcatenated_outs.append([])
                for i, batch_out in enumerate(batch_outs):
                    unconcatenated_outs[i].append(batch_out)
                if verbose == 1:
                    progbar.update(step + 1)
            if len(unconcatenated_outs) == 1:
                return np.concatenate(unconcatenated_outs[0], axis=0)
            return [np.concatenate(unconcatenated_outs[i], axis=0)
                    for i in range(len(unconcatenated_outs))]
        else:
            # Sample-based predictions.
            outs = []
            batches = _make_batches(num_samples, batch_size)
            index_array = np.arange(num_samples)
            for batch_index, (batch_start, batch_end) in enumerate(batches):
                batch_ids = index_array[batch_start:batch_end]
                if ins and isinstance(ins[-1], float):
                    # Do not slice the training phase flag.
                    ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
                else:
                    ins_batch = _slice_arrays(ins, batch_ids)
                for i in indices_for_conversion_to_dense:
                    ins_batch[i] = ins_batch[i].toarray()

                batch_outs = f(ins_batch)
                if not isinstance(batch_outs, list):
                    batch_outs = [batch_outs]
                if batch_index == 0:
                    # Pre-allocate the results arrays.
                    for batch_out in batch_outs:
                        shape = (num_samples,) + batch_out.shape[1:]
                        outs.append(np.zeros(shape, dtype=batch_out.dtype))
                for i, batch_out in enumerate(batch_outs):
                    outs[i][batch_start:batch_end] = batch_out
                if verbose == 1:
                    progbar.update(batch_end)
            if len(outs) == 1:
                return outs[0]
            return outs

    def _test_loop(self, f, ins, batch_size=None, verbose=0, steps=None):
        """Abstract method to loop over some data in batches.

        # Arguments
            f: Keras function returning a list of tensors.
            ins: list of tensors to be fed to `f`.
            batch_size: integer batch size or `None`.
            verbose: verbosity mode.
            steps: Total number of steps (batches of samples)
                before declaring predictions finished.
                Ignored with the default value of `None`.

        # Returns
            Scalar loss (if the model has a single output and no metrics)
            or list of scalars (if the model has multiple outputs
            and/or metrics). The attribute `model.metrics_names` will give you
            the display labels for the scalar outputs.
        """
        num_samples = self._check_num_samples(ins, batch_size,
                                              steps,
                                              'steps')
        outs = []
        if verbose == 1:
            if steps is not None:
                progbar = Progbar(target=steps)
            else:
                progbar = Progbar(target=num_samples)

        # To prevent a slowdown, we find beforehand the arrays that need conversion.
        feed = self._feed_inputs + self._feed_targets + self._feed_sample_weights
        indices_for_conversion_to_dense = []
        for i in range(len(feed)):
            if issparse(ins[i]) and not K.is_sparse(feed[i]):
                indices_for_conversion_to_dense.append(i)

        if steps is not None:
            for step in range(steps):
                batch_outs = f(ins)
                if isinstance(batch_outs, list):
                    if step == 0:
                        for _ in enumerate(batch_outs):
                            outs.append(0.)
                    for i, batch_out in enumerate(batch_outs):
                        outs[i] += batch_out
                else:
                    if step == 0:
                        outs.append(0.)
                    outs[0] += batch_outs
                if verbose == 1:
                    progbar.update(step + 1)
            for i in range(len(outs)):
                outs[i] /= steps
        else:
            batches = _make_batches(num_samples, batch_size)
            index_array = np.arange(num_samples)
            for batch_index, (batch_start, batch_end) in enumerate(batches):
                batch_ids = index_array[batch_start:batch_end]
                if isinstance(ins[-1], float):
                    # Do not slice the training phase flag.
                    ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
                else:
                    ins_batch = _slice_arrays(ins, batch_ids)
                for i in indices_for_conversion_to_dense:
                    ins_batch[i] = ins_batch[i].toarray()

                batch_outs = f(ins_batch)
                if isinstance(batch_outs, list):
                    if batch_index == 0:
                        for batch_out in enumerate(batch_outs):
                            outs.append(0.)
                    for i, batch_out in enumerate(batch_outs):
                        outs[i] += batch_out * len(batch_ids)
                else:
                    if batch_index == 0:
                        outs.append(0.)
                    outs[0] += batch_outs * len(batch_ids)

                if verbose == 1:
                    progbar.update(batch_end)
            for i in range(len(outs)):
                outs[i] /= num_samples
        if len(outs) == 1:
            return outs[0]
        return outs

    def _standardize_user_data(self, x, y,
                               sample_weight=None, class_weight=None,
                               check_batch_axis=True, batch_size=None):
        if not hasattr(self, 'optimizer'):
            raise RuntimeError('You must compile a model before '
                               'training/testing. '
                               'Use `model.compile(optimizer, loss)`.')

        output_shapes = []
        for output_shape, loss_fn in zip(self._feed_output_shapes, self._feed_loss_fns):
            if loss_fn is losses.sparse_categorical_crossentropy:
                output_shapes.append(output_shape[:-1] + (1,))
            elif (not hasattr(loss_fn, '__name__') or
                  getattr(losses, loss_fn.__name__, None) is None):
                # If `loss_fn` is not a function (e.g. callable class)
                # or if it not in the `losses` module, then
                # it is a user-defined loss and we make no assumptions
                # about it.
                output_shapes.append(None)
            else:
                output_shapes.append(output_shape)
        x = _standardize_input_data(x, self._feed_input_names,
                                    self._feed_input_shapes,
                                    check_batch_axis=False,
                                    exception_prefix='input')
        y = _standardize_input_data(y, self._feed_output_names,
                                    output_shapes,
                                    check_batch_axis=False,
                                    exception_prefix='target')
        sample_weights = _standardize_sample_weights(sample_weight,
                                                     self._feed_output_names)
        class_weights = _standardize_class_weights(class_weight,
                                                   self._feed_output_names)
        sample_weights = [_standardize_weights(ref, sw, cw, mode)
                          for (ref, sw, cw, mode)
                          in zip(y, sample_weights, class_weights, self._feed_sample_weight_modes)]
        _check_array_lengths(x, y, sample_weights)
        _check_loss_and_target_compatibility(y,
                                             self._feed_loss_fns,
                                             self._feed_output_shapes)
        if self.stateful and batch_size:
            if x[0].shape[0] % batch_size != 0:
                raise ValueError('In a stateful network, '
                                 'you should only pass inputs with '
                                 'a number of samples that can be '
                                 'divided by the batch size. Found: ' +
                                 str(x[0].shape[0]) + ' samples')
        return x, y, sample_weights

    def _get_deduped_metrics_names(self):
        out_labels = self.metrics_names

        # Rename duplicated metrics name
        # (can happen with an output layer shared among multiple dataflows).
        deduped_out_labels = []
        for i, label in enumerate(out_labels):
            new_label = label
            if out_labels.count(label) > 1:
                dup_idx = out_labels[:i].count(label)
                new_label += '_' + str(dup_idx + 1)
            deduped_out_labels.append(new_label)
        return deduped_out_labels

    def fit(self,
            x=None,
            y=None,
            batch_size=None,
            epochs=1,
            verbose=1,
            callbacks=None,
            validation_split=0.,
            validation_data=None,
            shuffle=True,
            class_weight=None,
            sample_weight=None,
            initial_epoch=0,
            steps_per_epoch=None,
            validation_steps=None,
            **kwargs):
        """Trains the model for a fixed number of epochs (iterations on a dataset).

        # Arguments
            x: Numpy array of training data (if the model has a single input),
                or list of Numpy arrays (if the model has multiple inputs).
                If input layers in the model are named, you can also pass a
                dictionary mapping input names to Numpy arrays.
                `x` can be `None` (default) if feeding from
                framework-native tensors (e.g. TensorFlow data tensors).
            y: Numpy array of target (label) data
                (if the model has a single output),
                or list of Numpy arrays (if the model has multiple outputs).
                If output layers in the model are named, you can also pass a
                dictionary mapping output names to Numpy arrays.
                `y` can be `None` (default) if feeding from
                framework-native tensors (e.g. TensorFlow data tensors).
            batch_size: Integer or `None`.
                Number of samples per gradient update.
                If unspecified, `batch_size` will default to 32.
            epochs: Integer. Number of epochs to train the model.
                An epoch is an iteration over the entire `x` and `y`
                data provided.
                Note that in conjunction with `initial_epoch`,
                `epochs` is to be understood as "final epoch".
                The model is not trained for a number of iterations
                given by `epochs`, but merely until the epoch
                of index `epochs` is reached.
            verbose: Integer. 0, 1, or 2. Verbosity mode.
                0 = silent, 1 = progress bar, 2 = one line per epoch.
            callbacks: List of `keras.callbacks.Callback` instances.
                List of callbacks to apply during training.
                See [callbacks](/callbacks).
            validation_split: Float between 0 and 1.
                Fraction of the training data to be used as validation data.
                The model will set apart this fraction of the training data,
                will not train on it, and will evaluate
                the loss and any model metrics
                on this data at the end of each epoch.
                The validation data is selected from the last samples
                in the `x` and `y` data provided, before shuffling.
            validation_data: tuple `(x_val, y_val)` or tuple
                `(x_val, y_val, val_sample_weights)` on which to evaluate
                the loss and any model metrics at the end of each epoch.
                The model will not be trained on this data.
                `validation_data` will override `validation_split`.
            shuffle: Boolean (whether to shuffle the training data
                before each epoch) or str (for 'batch').
                'batch' is a special option for dealing with the
                limitations of HDF5 data; it shuffles in batch-sized chunks.
                Has no effect when `steps_per_epoch` is not `None`.
            class_weight: Optional dictionary mapping class indices (integers)
                to a weight (float) value, used for weighting the loss function
                (during training only).
                This can be useful to tell the model to
                "pay more attention" to samples from
                an under-represented class.
            sample_weight: Optional Numpy array of weights for
                the training samples, used for weighting the loss function
                (during training only). You can either pass a flat (1D)
                Numpy array with the same length as the input samples
                (1:1 mapping between weights and samples),
                or in the case of temporal data,
                you can pass a 2D array with shape
                `(samples, sequence_length)`,
                to apply a different weight to every timestep of every sample.
                In this case you should make sure to specify
                `sample_weight_mode="temporal"` in `compile()`.
            initial_epoch: Integer.
                Epoch at which to start training
                (useful for resuming a previous training run).
            steps_per_epoch: Integer or `None`.
                Total number of steps (batches of samples)
                before declaring one epoch finished and starting the
                next epoch. When training with input tensors such as
                TensorFlow data tensors, the default `None` is equal to
                the number of samples in your dataset divided by
                the batch size, or 1 if that cannot be determined.
            validation_steps: Only relevant if `steps_per_epoch`
                is specified. Total number of steps (batches of samples)
                to validate before stopping.

        # Returns
            A `History` object. Its `History.history` attribute is
            a record of training loss values and metrics values
            at successive epochs, as well as validation loss values
            and validation metrics values (if applicable).

        # Raises
            RuntimeError: If the model was never compiled.
            ValueError: In case of mismatch between the provided input data
                and what the model expects.
        """
        # Backwards compatibility
        if batch_size is None and steps_per_epoch is None:
            batch_size = 32
        # Legacy support
        if 'nb_epoch' in kwargs:
            warnings.warn('The `nb_epoch` argument in `fit` '
                          'has been renamed `epochs`.', stacklevel=2)
            epochs = kwargs.pop('nb_epoch')
        if kwargs:
            raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))
        if x is None and y is None and steps_per_epoch is None:
            raise ValueError('If fitting from data tensors, '
                             'you should specify the `steps_per_epoch` '
                             'argument.')
        # Validate user data.
        x, y, sample_weights = self._standardize_user_data(
            x, y,
            sample_weight=sample_weight,
            class_weight=class_weight,
            check_batch_axis=False,
            batch_size=batch_size)
        # Prepare validation data.
        do_validation = False
        if validation_data:
            do_validation = True
            if len(validation_data) == 2:
                val_x, val_y = validation_data
                val_sample_weight = None
            elif len(validation_data) == 3:
                val_x, val_y, val_sample_weight = validation_data
            else:
                raise ValueError('When passing validation_data, '
                                 'it must contain 2 (x_val, y_val) '
                                 'or 3 (x_val, y_val, val_sample_weights) '
                                 'items, however it contains %d items' %
                                 len(validation_data))

            val_x, val_y, val_sample_weights = self._standardize_user_data(
                val_x, val_y,
                sample_weight=val_sample_weight,
                check_batch_axis=False,
                batch_size=batch_size)
            if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
                val_ins = val_x + val_y + val_sample_weights + [0.]
            else:
                val_ins = val_x + val_y + val_sample_weights

        elif validation_split and 0. < validation_split < 1.:
            do_validation = True
            if hasattr(x[0], 'shape'):
                split_at = int(x[0].shape[0] * (1. - validation_split))
            else:
                split_at = int(len(x[0]) * (1. - validation_split))
            x, val_x = (_slice_arrays(x, 0, split_at), _slice_arrays(x, split_at))
            y, val_y = (_slice_arrays(y, 0, split_at), _slice_arrays(y, split_at))
            sample_weights, val_sample_weights = (
                _slice_arrays(sample_weights, 0, split_at),
                _slice_arrays(sample_weights, split_at))
            if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
                val_ins = val_x + val_y + val_sample_weights + [0.]
            else:
                val_ins = val_x + val_y + val_sample_weights

        elif validation_steps:
            do_validation = True
            if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
                val_ins = [0.]

        # Prepare input arrays and training function.
        if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
            ins = x + y + sample_weights + [1.]
        else:
            ins = x + y + sample_weights
        self._make_train_function()
        f = self.train_function

        # Prepare display labels.
        out_labels = self._get_deduped_metrics_names()

        if do_validation:
            self._make_test_function()
            val_f = self.test_function
            callback_metrics = copy.copy(out_labels) + ['val_' + n for n in out_labels]
        else:
            callback_metrics = copy.copy(out_labels)
            val_f = None
            val_ins = []

        # Delegate logic to `_fit_loop`.
        return self._fit_loop(f, ins, out_labels=out_labels,
                              batch_size=batch_size, epochs=epochs,
                              verbose=verbose, callbacks=callbacks,
                              val_f=val_f, val_ins=val_ins, shuffle=shuffle,
                              callback_metrics=callback_metrics,
                              initial_epoch=initial_epoch,
                              steps_per_epoch=steps_per_epoch,
                              validation_steps=validation_steps)

    def evaluate(self, x=None, y=None,
                 batch_size=None,
                 verbose=1,
                 sample_weight=None,
                 steps=None):
        """Returns the loss value & metrics values for the model in test mode.

        Computation is done in batches.

        # Arguments
            x: Numpy array of test data (if the model has a single input),
                or list of Numpy arrays (if the model has multiple inputs).
                If input layers in the model are named, you can also pass a
                dictionary mapping input names to Numpy arrays.
                `x` can be `None` (default) if feeding from
                framework-native tensors (e.g. TensorFlow data tensors).
            y: Numpy array of target (label) data
                (if the model has a single output),
                or list of Numpy arrays (if the model has multiple outputs).
                If output layers in the model are named, you can also pass a
                dictionary mapping output names to Numpy arrays.
                `y` can be `None` (default) if feeding from
                framework-native tensors (e.g. TensorFlow data tensors).
            batch_size: Integer or `None`.
                Number of samples per evaluation step.
                If unspecified, `batch_size` will default to 32.
            verbose: 0 or 1. Verbosity mode.
                0 = silent, 1 = progress bar.
            sample_weight: Optional Numpy array of weights for
                the test samples, used for weighting the loss function.
                You can either pass a flat (1D)
                Numpy array with the same length as the input samples
                (1:1 mapping between weights and samples),
                or in the case of temporal data,
                you can pass a 2D array with shape
                `(samples, sequence_length)`,
                to apply a different weight to every timestep of every sample.
                In this case you should make sure to specify
                `sample_weight_mode="temporal"` in `compile()`.
            steps: Integer or `None`.
                Total number of steps (batches of samples)
                before declaring the evaluation round finished.
                Ignored with the default value of `None`.

        # Returns
            Scalar test loss (if the model has a single output and no metrics)
            or list of scalars (if the model has multiple outputs
            and/or metrics). The attribute `model.metrics_names` will give you
            the display labels for the scalar outputs.
        """
        # Backwards compatibility.
        if batch_size is None and steps is None:
            batch_size = 32
        if x is None and y is None and steps is None:
            raise ValueError('If evaluating from data tensors, '
                             'you should specify the `steps` '
                             'argument.')
        # Validate user data.
        x, y, sample_weights = self._standardize_user_data(
            x, y,
            sample_weight=sample_weight,
            check_batch_axis=False,
            batch_size=batch_size)
        # Prepare inputs, delegate logic to `_test_loop`.
        if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
            ins = x + y + sample_weights + [0.]
        else:
            ins = x + y + sample_weights
        self._make_test_function()
        f = self.test_function
        return self._test_loop(f, ins,
                               batch_size=batch_size,
                               verbose=verbose,
                               steps=steps)

    def predict(self, x,
                batch_size=None,
                verbose=0,
                steps=None):
        """Generates output predictions for the input samples.

        Computation is done in batches.

        # Arguments
            x: The input data, as a Numpy array
                (or list of Numpy arrays if the model has multiple outputs).
            batch_size: Integer. If unspecified, it will default to 32.
            verbose: Verbosity mode, 0 or 1.
            steps: Total number of steps (batches of samples)
                before declaring the prediction round finished.
                Ignored with the default value of `None`.

        # Returns
            Numpy array(s) of predictions.

        # Raises
            ValueError: In case of mismatch between the provided
                input data and the model's expectations,
                or in case a stateful model receives a number of samples
                that is not a multiple of the batch size.
        """
        # Backwards compatibility.
        if batch_size is None and steps is None:
            batch_size = 32
        if x is None and steps is None:
            raise ValueError('If predicting from data tensors, '
                             'you should specify the `steps` '
                             'argument.')
        # Validate user data.
        x = _standardize_input_data(x, self._feed_input_names,
                                    self._feed_input_shapes,
                                    check_batch_axis=False)
        if self.stateful:
            if x[0].shape[0] > batch_size and x[0].shape[0] % batch_size != 0:
                raise ValueError('In a stateful network, '
                                 'you should only pass inputs with '
                                 'a number of samples that can be '
                                 'divided by the batch size. Found: ' +
                                 str(x[0].shape[0]) + ' samples. '
                                 'Batch size: ' + str(batch_size) + '.')

        # Prepare inputs, delegate logic to `_predict_loop`.
        if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
            ins = x + [0.]
        else:
            ins = x
        self._make_predict_function()
        f = self.predict_function
        return self._predict_loop(f, ins, batch_size=batch_size,
                                  verbose=verbose, steps=steps)

    def train_on_batch(self, x, y,
                       sample_weight=None,
                       class_weight=None):
        """Runs a single gradient update on a single batch of data.

        # Arguments
            x: Numpy array of training data,
                or list of Numpy arrays if the model has multiple inputs.
                If all inputs in the model are named,
                you can also pass a dictionary
                mapping input names to Numpy arrays.
            y: Numpy array of target data,
                or list of Numpy arrays if the model has multiple outputs.
                If all outputs in the model are named,
                you can also pass a dictionary
                mapping output names to Numpy arrays.
            sample_weight: Optional array of the same length as x, containing
                weights to apply to the model's loss for each sample.
                In the case of temporal data, you can pass a 2D array
                with shape (samples, sequence_length),
                to apply a different weight to every timestep of every sample.
                In this case you should make sure to specify
                sample_weight_mode="temporal" in compile().
            class_weight: Optional dictionary mapping
                class indices (integers) to
                a weight (float) to apply to the model's loss for the samples
                from this class during training.
                This can be useful to tell the model to "pay more attention" to
                samples from an under-represented class.

        # Returns
            Scalar training loss
            (if the model has a single output and no metrics)
            or list of scalars (if the model has multiple outputs
            and/or metrics). The attribute `model.metrics_names` will give you
            the display labels for the scalar outputs.
        """
        x, y, sample_weights = self._standardize_user_data(
            x, y,
            sample_weight=sample_weight,
            class_weight=class_weight,
            check_batch_axis=True)
        if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
            ins = x + y + sample_weights + [1.]
        else:
            ins = x + y + sample_weights
        self._make_train_function()
        outputs = self.train_function(ins)
        if len(outputs) == 1:
            return outputs[0]
        return outputs

    def test_on_batch(self, x, y, sample_weight=None):
        """Test the model on a single batch of samples.

        # Arguments
            x: Numpy array of test data,
                or list of Numpy arrays if the model has multiple inputs.
                If all inputs in the model are named,
                you can also pass a dictionary
                mapping input names to Numpy arrays.
            y: Numpy array of target data,
                or list of Numpy arrays if the model has multiple outputs.
                If all outputs in the model are named,
                you can also pass a dictionary
                mapping output names to Numpy arrays.
            sample_weight: Optional array of the same length as x, containing
                weights to apply to the model's loss for each sample.
                In the case of temporal data, you can pass a 2D array
                with shape (samples, sequence_length),
                to apply a different weight to every timestep of every sample.
                In this case you should make sure to specify
                sample_weight_mode="temporal" in compile().

        # Returns
            Scalar test loss (if the model has a single output and no metrics)
            or list of scalars (if the model has multiple outputs
            and/or metrics). The attribute `model.metrics_names` will give you
            the display labels for the scalar outputs.
        """
        x, y, sample_weights = self._standardize_user_data(
            x, y,
            sample_weight=sample_weight,
            check_batch_axis=True)
        if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
            ins = x + y + sample_weights + [0.]
        else:
            ins = x + y + sample_weights
        self._make_test_function()
        outputs = self.test_function(ins)
        if len(outputs) == 1:
            return outputs[0]
        return outputs

    def predict_on_batch(self, x):
        """Returns predictions for a single batch of samples.

        # Arguments
            x: Input samples, as a Numpy array.

        # Returns
            Numpy array(s) of predictions.
        """
        x = _standardize_input_data(x, self._feed_input_names,
                                    self._feed_input_shapes)
        if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
            ins = x + [0.]
        else:
            ins = x
        self._make_predict_function()
        outputs = self.predict_function(ins)
        if len(outputs) == 1:
            return outputs[0]
        return outputs

    @interfaces.legacy_generator_methods_support
    def fit_generator(self,
                      generator,
                      steps_per_epoch=None,
                      epochs=1,
                      verbose=1,
                      callbacks=None,
                      validation_data=None,
                      validation_steps=None,
                      class_weight=None,
                      max_queue_size=10,
                      workers=1,
                      use_multiprocessing=False,
                      shuffle=True,
                      initial_epoch=0):
        """Fits the model on data yielded batch-by-batch by a Python generator.

        The generator is run in parallel to the model, for efficiency.
        For instance, this allows you to do real-time data augmentation
        on images on CPU in parallel to training your model on GPU.

        The use of `keras.utils.Sequence` guarantees the ordering
        and guarantees the single use of every input per epoch when
        using `use_multiprocessing=True`.

        # Arguments
            generator: A generator or an instance of `Sequence` (`keras.utils.Sequence`)
                object in order to avoid duplicate data
                when using multiprocessing.
                The output of the generator must be either
                - a tuple `(inputs, targets)`
                - a tuple `(inputs, targets, sample_weights)`.
                This tuple (a single output of the generator) makes a single batch.
                Therefore, all arrays in this tuple must have the same length (equal
                to the size of this batch). Different batches may have different sizes.
                For example, the last batch of the epoch is commonly smaller than the
                others, if the size of the dataset is not divisible by the batch size.
                The generator is expected to loop over its data
                indefinitely. An epoch finishes when `steps_per_epoch`
                batches have been seen by the model.
            steps_per_epoch: Total number of steps (batches of samples)
                to yield from `generator` before declaring one epoch
                finished and starting the next epoch. It should typically
                be equal to the number of samples of your dataset
                divided by the batch size.
                Optional for `Sequence`: if unspecified, will use
                the `len(generator)` as a number of steps.
            epochs: Integer, total number of iterations on the data.
            verbose: Verbosity mode, 0, 1, or 2.
            callbacks: List of callbacks to be called during training.
            validation_data: This can be either
                - a generator for the validation data
                - a tuple (inputs, targets)
                - a tuple (inputs, targets, sample_weights).
            validation_steps: Only relevant if `validation_data`
                is a generator. Total number of steps (batches of samples)
                to yield from `generator` before stopping.
                Optional for `Sequence`: if unspecified, will use
                the `len(validation_data)` as a number of steps.
            class_weight: Dictionary mapping class indices to a weight
                for the class.
            max_queue_size: Integer. Maximum size for the generator queue.
                If unspecified, `max_queue_size` will default to 10.
            workers: Integer. Maximum number of processes to spin up
                when using process based threading.
                If unspecified, `workers` will default to 1. If 0, will
                execute the generator on the main thread.
            use_multiprocessing: Boolean. If True, use process based threading.
                If unspecified, `workers` will default to False.
                Note that because
                this implementation relies on multiprocessing,
                you should not pass
                non picklable arguments to the generator
                as they can't be passed
                easily to children processes.
            shuffle: Whether to shuffle the order of the batches at
                the beginning of each epoch. Only used with instances
                of `Sequence` (keras.utils.Sequence).
            initial_epoch: Epoch at which to start training
                (useful for resuming a previous training run)

        # Returns
            A `History` object.

        # Example

        ```python
            def generate_arrays_from_file(path):
                while 1:
                    f = open(path)
                    for line in f:
                        # create numpy arrays of input data
                        # and labels, from each line in the file
                        x1, x2, y = process_line(line)
                        yield ({'input_1': x1, 'input_2': x2}, {'output': y})
                    f.close()

            model.fit_generator(generate_arrays_from_file('/my_file.txt'),
                                steps_per_epoch=10000, epochs=10)
        ```

        # Raises
            ValueError: In case the generator yields
                data in an invalid format.
        """
        wait_time = 0.01  # in seconds
        epoch = initial_epoch

        do_validation = bool(validation_data)
        self._make_train_function()
        if do_validation:
            self._make_test_function()

        is_sequence = isinstance(generator, Sequence)
        if not is_sequence and use_multiprocessing and workers > 1:
            warnings.warn(
                UserWarning('Using a generator with `use_multiprocessing=True`'
                            ' and multiple workers may duplicate your data.'
                            ' Please consider using the`keras.utils.Sequence'
                            ' class.'))
        if steps_per_epoch is None:
            if is_sequence:
                steps_per_epoch = len(generator)
            else:
                raise ValueError('`steps_per_epoch=None` is only valid for a'
                                 ' generator based on the `keras.utils.Sequence`'
                                 ' class. Please specify `steps_per_epoch` or use'
                                 ' the `keras.utils.Sequence` class.')

        # python 2 has 'next', 3 has '__next__'
        # avoid any explicit version checks
        val_gen = (hasattr(validation_data, 'next') or
                   hasattr(validation_data, '__next__') or
                   isinstance(validation_data, Sequence))
        if (val_gen and not isinstance(validation_data, Sequence) and
                not validation_steps):
            raise ValueError('`validation_steps=None` is only valid for a'
                             ' generator based on the `keras.utils.Sequence`'
                             ' class. Please specify `validation_steps` or use'
                             ' the `keras.utils.Sequence` class.')

        # Prepare display labels.
        out_labels = self._get_deduped_metrics_names()
        callback_metrics = out_labels + ['val_' + n for n in out_labels]

        # prepare callbacks
        self.history = cbks.History()
        callbacks = [cbks.BaseLogger()] + (callbacks or []) + [self.history]
        if verbose:
            callbacks += [cbks.ProgbarLogger(count_mode='steps')]
        callbacks = cbks.CallbackList(callbacks)

        # it's possible to callback a different model than self:
        if hasattr(self, 'callback_model') and self.callback_model:
            callback_model = self.callback_model
        else:
            callback_model = self
        callbacks.set_model(callback_model)
        callbacks.set_params({
            'epochs': epochs,
            'steps': steps_per_epoch,
            'verbose': verbose,
            'do_validation': do_validation,
            'metrics': callback_metrics,
        })
        callbacks.on_train_begin()

        enqueuer = None
        val_enqueuer = None

        try:
            if do_validation:
                if val_gen:
                    if workers > 0:
                        if isinstance(validation_data, Sequence):
                            val_enqueuer = OrderedEnqueuer(validation_data,
                                                           use_multiprocessing=use_multiprocessing)
                            if validation_steps is None:
                                validation_steps = len(validation_data)
                        else:
                            val_enqueuer = GeneratorEnqueuer(validation_data,
                                                             use_multiprocessing=use_multiprocessing,
                                                             wait_time=wait_time)
                        val_enqueuer.start(workers=workers, max_queue_size=max_queue_size)
                        validation_generator = val_enqueuer.get()
                    else:
                        validation_generator = validation_data
                else:
                    if len(validation_data) == 2:
                        val_x, val_y = validation_data
                        val_sample_weight = None
                    elif len(validation_data) == 3:
                        val_x, val_y, val_sample_weight = validation_data
                    else:
                        raise ValueError('`validation_data` should be a tuple '
                                         '`(val_x, val_y, val_sample_weight)` '
                                         'or `(val_x, val_y)`. Found: ' +
                                         str(validation_data))
                    val_x, val_y, val_sample_weights = self._standardize_user_data(
                        val_x, val_y, val_sample_weight)
                    val_data = val_x + val_y + val_sample_weights
                    if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
                        val_data += [0.]
                    for cbk in callbacks:
                        cbk.validation_data = val_data

            if workers > 0:
                if is_sequence:
                    enqueuer = OrderedEnqueuer(generator,
                                               use_multiprocessing=use_multiprocessing,
                                               shuffle=shuffle)
                else:
                    enqueuer = GeneratorEnqueuer(generator,
                                                 use_multiprocessing=use_multiprocessing,
                                                 wait_time=wait_time)
                enqueuer.start(workers=workers, max_queue_size=max_queue_size)
                output_generator = enqueuer.get()
            else:
                output_generator = generator

            callback_model.stop_training = False
            # Construct epoch logs.
            epoch_logs = {}
            while epoch < epochs:
                callbacks.on_epoch_begin(epoch)
                steps_done = 0
                batch_index = 0
                while steps_done < steps_per_epoch:
                    generator_output = next(output_generator)

                    if not hasattr(generator_output, '__len__'):
                        raise ValueError('Output of generator should be '
                                         'a tuple `(x, y, sample_weight)` '
                                         'or `(x, y)`. Found: ' +
                                         str(generator_output))

                    if len(generator_output) == 2:
                        x, y = generator_output
                        sample_weight = None
                    elif len(generator_output) == 3:
                        x, y, sample_weight = generator_output
                    else:
                        raise ValueError('Output of generator should be '
                                         'a tuple `(x, y, sample_weight)` '
                                         'or `(x, y)`. Found: ' +
                                         str(generator_output))
                    # build batch logs
                    batch_logs = {}
                    if isinstance(x, list):
                        batch_size = x[0].shape[0]
                    elif isinstance(x, dict):
                        batch_size = list(x.values())[0].shape[0]
                    else:
                        batch_size = x.shape[0]
                    batch_logs['batch'] = batch_index
                    batch_logs['size'] = batch_size
                    callbacks.on_batch_begin(batch_index, batch_logs)

                    outs = self.train_on_batch(x, y,
                                               sample_weight=sample_weight,
                                               class_weight=class_weight)

                    if not isinstance(outs, list):
                        outs = [outs]
                    for l, o in zip(out_labels, outs):
                        batch_logs[l] = o

                    callbacks.on_batch_end(batch_index, batch_logs)

                    batch_index += 1
                    steps_done += 1

                    # Epoch finished.
                    if steps_done >= steps_per_epoch and do_validation:
                        if val_gen:
                            val_outs = self.evaluate_generator(
                                validation_generator,
                                validation_steps,
                                workers=0)
                        else:
                            # No need for try/except because
                            # data has already been validated.
                            val_outs = self.evaluate(
                                val_x, val_y,
                                batch_size=batch_size,
                                sample_weight=val_sample_weights,
                                verbose=0)
                        if not isinstance(val_outs, list):
                            val_outs = [val_outs]
                        # Same labels assumed.
                        for l, o in zip(out_labels, val_outs):
                            epoch_logs['val_' + l] = o

                    if callback_model.stop_training:
                        break

                callbacks.on_epoch_end(epoch, epoch_logs)
                epoch += 1
                if callback_model.stop_training:
                    break

        finally:
            try:
                if enqueuer is not None:
                    enqueuer.stop()
            finally:
                if val_enqueuer is not None:
                    val_enqueuer.stop()

        callbacks.on_train_end()
        return self.history

    @interfaces.legacy_generator_methods_support
    def evaluate_generator(self, generator, steps=None,
                           max_queue_size=10,
                           workers=1,
                           use_multiprocessing=False):
        """Evaluates the model on a data generator.

        The generator should return the same kind of data
        as accepted by `test_on_batch`.

        # Arguments
            generator: Generator yielding tuples (inputs, targets)
                or (inputs, targets, sample_weights)
                or an instance of Sequence (keras.utils.Sequence)
                object in order to avoid duplicate data
                when using multiprocessing.
            steps: Total number of steps (batches of samples)
                to yield from `generator` before stopping.
                Optional for `Sequence`: if unspecified, will use
                the `len(generator)` as a number of steps.
            max_queue_size: maximum size for the generator queue
            workers: Integer. Maximum number of processes to spin up
                when using process based threading.
                If unspecified, `workers` will default to 1. If 0, will
                execute the generator on the main thread.
            use_multiprocessing: if True, use process based threading.
                Note that because
                this implementation relies on multiprocessing,
                you should not pass
                non picklable arguments to the generator
                as they can't be passed
                easily to children processes.

        # Returns
            Scalar test loss (if the model has a single output and no metrics)
            or list of scalars (if the model has multiple outputs
            and/or metrics). The attribute `model.metrics_names` will give you
            the display labels for the scalar outputs.

        # Raises
            ValueError: In case the generator yields
                data in an invalid format.
        """
        self._make_test_function()

        steps_done = 0
        wait_time = 0.01
        all_outs = []
        batch_sizes = []
        is_sequence = isinstance(generator, Sequence)
        if not is_sequence and use_multiprocessing and workers > 1:
            warnings.warn(
                UserWarning('Using a generator with `use_multiprocessing=True`'
                            ' and multiple workers may duplicate your data.'
                            ' Please consider using the`keras.utils.Sequence'
                            ' class.'))
        if steps is None:
            if is_sequence:
                steps = len(generator)
            else:
                raise ValueError('`steps=None` is only valid for a generator'
                                 ' based on the `keras.utils.Sequence` class.'
                                 ' Please specify `steps` or use the'
                                 ' `keras.utils.Sequence` class.')
        enqueuer = None

        try:
            if workers > 0:
                if is_sequence:
                    enqueuer = OrderedEnqueuer(generator,
                                               use_multiprocessing=use_multiprocessing)
                else:
                    enqueuer = GeneratorEnqueuer(generator,
                                                 use_multiprocessing=use_multiprocessing,
                                                 wait_time=wait_time)
                enqueuer.start(workers=workers, max_queue_size=max_queue_size)
                output_generator = enqueuer.get()
            else:
                output_generator = generator

            while steps_done < steps:
                generator_output = next(output_generator)
                if not hasattr(generator_output, '__len__'):
                    raise ValueError('Output of generator should be a tuple '
                                     '(x, y, sample_weight) '
                                     'or (x, y). Found: ' +
                                     str(generator_output))
                if len(generator_output) == 2:
                    x, y = generator_output
                    sample_weight = None
                elif len(generator_output) == 3:
                    x, y, sample_weight = generator_output
                else:
                    raise ValueError('Output of generator should be a tuple '
                                     '(x, y, sample_weight) '
                                     'or (x, y). Found: ' +
                                     str(generator_output))
                outs = self.test_on_batch(x, y, sample_weight=sample_weight)

                if isinstance(x, list):
                    batch_size = x[0].shape[0]
                elif isinstance(x, dict):
                    batch_size = list(x.values())[0].shape[0]
                else:
                    batch_size = x.shape[0]
                if batch_size == 0:
                    raise ValueError('Received an empty batch. '
                                     'Batches should at least contain one item.')
                all_outs.append(outs)

                steps_done += 1
                batch_sizes.append(batch_size)

        finally:
            if enqueuer is not None:
                enqueuer.stop()

        if not isinstance(outs, list):
            return np.average(np.asarray(all_outs),
                              weights=batch_sizes)
        else:
            averages = []
            for i in range(len(outs)):
                averages.append(np.average([out[i] for out in all_outs],
                                           weights=batch_sizes))
            return averages

    @interfaces.legacy_generator_methods_support
    def predict_generator(self, generator, steps=None,
                          max_queue_size=10,
                          workers=1,
                          use_multiprocessing=False,
                          verbose=0):
        """Generates predictions for the input samples from a data generator.

        The generator should return the same kind of data as accepted by
        `predict_on_batch`.

        # Arguments
            generator: Generator yielding batches of input samples
                or an instance of Sequence (keras.utils.Sequence)
                object in order to avoid duplicate data
                when using multiprocessing.
            steps: Total number of steps (batches of samples)
                to yield from `generator` before stopping.
                Optional for `Sequence`: if unspecified, will use
                the `len(generator)` as a number of steps.
            max_queue_size: Maximum size for the generator queue.
            workers: Integer. Maximum number of processes to spin up
                when using process based threading.
                If unspecified, `workers` will default to 1. If 0, will
                execute the generator on the main thread.
            use_multiprocessing: If `True`, use process based threading.
                Note that because
                this implementation relies on multiprocessing,
                you should not pass
                non picklable arguments to the generator
                as they can't be passed
                easily to children processes.
            verbose: verbosity mode, 0 or 1.

        # Returns
            Numpy array(s) of predictions.

        # Raises
            ValueError: In case the generator yields
                data in an invalid format.
        """
        self._make_predict_function()

        steps_done = 0
        wait_time = 0.01
        all_outs = []
        is_sequence = isinstance(generator, Sequence)
        if not is_sequence and use_multiprocessing and workers > 1:
            warnings.warn(
                UserWarning('Using a generator with `use_multiprocessing=True`'
                            ' and multiple workers may duplicate your data.'
                            ' Please consider using the`keras.utils.Sequence'
                            ' class.'))
        if steps is None:
            if is_sequence:
                steps = len(generator)
            else:
                raise ValueError('`steps=None` is only valid for a generator'
                                 ' based on the `keras.utils.Sequence` class.'
                                 ' Please specify `steps` or use the'
                                 ' `keras.utils.Sequence` class.')
        enqueuer = None

        try:
            if workers > 0:
                if is_sequence:
                    enqueuer = OrderedEnqueuer(generator,
                                               use_multiprocessing=use_multiprocessing)
                else:
                    enqueuer = GeneratorEnqueuer(generator,
                                                 use_multiprocessing=use_multiprocessing,
                                                 wait_time=wait_time)
                enqueuer.start(workers=workers, max_queue_size=max_queue_size)
                output_generator = enqueuer.get()
            else:
                output_generator = generator

            if verbose == 1:
                progbar = Progbar(target=steps)

            while steps_done < steps:
                generator_output = next(output_generator)
                if isinstance(generator_output, tuple):
                    # Compatibility with the generators
                    # used for training.
                    if len(generator_output) == 2:
                        x, _ = generator_output
                    elif len(generator_output) == 3:
                        x, _, _ = generator_output
                    else:
                        raise ValueError('Output of generator should be '
                                         'a tuple `(x, y, sample_weight)` '
                                         'or `(x, y)`. Found: ' +
                                         str(generator_output))
                else:
                    # Assumes a generator that only
                    # yields inputs (not targets and sample weights).
                    x = generator_output

                outs = self.predict_on_batch(x)
                if not isinstance(outs, list):
                    outs = [outs]

                if not all_outs:
                    for out in outs:
                        all_outs.append([])

                for i, out in enumerate(outs):
                    all_outs[i].append(out)
                steps_done += 1
                if verbose == 1:
                    progbar.update(steps_done)

        finally:
            if enqueuer is not None:
                enqueuer.stop()

        if len(all_outs) == 1:
            if steps_done == 1:
                return all_outs[0][0]
            else:
                return np.concatenate(all_outs[0])
        if steps_done == 1:
            return [out[0] for out in all_outs]
        else:
            return [np.concatenate(out) for out in all_outs]
