# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# pylint: disable=protected-access
"""Base layer code and base model (Container) code.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import copy
import json
import os
import re
import warnings

import numpy as np
from six.moves import zip  # pylint: disable=redefined-builtin

from tensorflow.contrib.keras.python.keras import backend as K
from tensorflow.contrib.keras.python.keras.utils import conv_utils
from tensorflow.contrib.keras.python.keras.utils.io_utils import ask_to_proceed_with_overwrite
from tensorflow.contrib.keras.python.keras.utils.layer_utils import print_summary as print_layer_summary
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.layers import base as tf_base_layers
from tensorflow.python.util import tf_inspect


# pylint: disable=g-import-not-at-top
try:
  import h5py
except ImportError:
  h5py = None

try:
  import yaml
except ImportError:
  yaml = None
# pylint: enable=g-import-not-at-top


class InputSpec(object):
  """Specifies the ndim, dtype and shape of every input to a layer.

  Every layer should expose (if appropriate) an `input_spec` attribute:
  a list of instances of InputSpec (one per input tensor).

  A None entry in a shape is compatible with any dimension,
  a None shape is compatible with any shape.

  Arguments:
      dtype: Expected datatype of the input.
      shape: Shape tuple, expected shape of the input
          (may include None for unchecked axes).
      ndim: Integer, expected rank of the input.
      max_ndim: Integer, maximum rank of the input.
      min_ndim: Integer, minimum rank of the input.
      axes: Dictionary mapping integer axes to
          a specific dimension value.
  """

  def __init__(self,
               dtype=None,
               shape=None,
               ndim=None,
               max_ndim=None,
               min_ndim=None,
               axes=None):
    self.dtype = dtype
    self.shape = shape
    if shape is not None:
      self.ndim = len(shape)
    else:
      self.ndim = ndim
    self.max_ndim = max_ndim
    self.min_ndim = min_ndim
    self.axes = axes or {}


class Node(object):
  """A `Node` describes the connectivity between two layers.

  Each time a layer is connected to some new input,
  a node is added to `layer.inbound_nodes`.
  Each time the output of a layer is used by another layer,
  a node is added to `layer.outbound_nodes`.

  Arguments:
      outbound_layer: the layer that takes
          `input_tensors` and turns them into `output_tensors`
          (the node gets created when the `call`
          method of the layer was called).
      inbound_layers: a list of layers, the same length as `input_tensors`,
          the layers from where `input_tensors` originate.
      node_indices: a list of integers, the same length as `inbound_layers`.
          `node_indices[i]` is the origin node of `input_tensors[i]`
          (necessary since each inbound layer might have several nodes,
          e.g. if the layer is being shared with a different data stream).
      tensor_indices: a list of integers,
          the same length as `inbound_layers`.
          `tensor_indices[i]` is the index of `input_tensors[i]` within the
          output of the inbound layer
          (necessary since each inbound layer might
          have multiple tensor outputs, with each one being
          independently manipulable).
      input_tensors: list of input tensors.
      output_tensors: list of output tensors.
      input_masks: list of input masks (a mask can be a tensor, or None).
      output_masks: list of output masks (a mask can be a tensor, or None).
      arguments: dictionary of keyword arguments that were passed to the
          `call` method of the layer at the call that created the node.

  `node_indices` and `tensor_indices` are basically fine-grained coordinates
  describing the origin of the `input_tensors`.

  A node from layer A to layer B is added to:
      A.outbound_nodes
      B.inbound_nodes
  """

  def __init__(self,
               outbound_layer,
               inbound_layers,
               node_indices,
               tensor_indices,
               input_tensors,
               output_tensors,
               input_masks,
               output_masks,
               arguments=None):
    # Layer instance (NOT a list).
    # this is the layer that takes a list of input tensors
    # and turns them into a list of output tensors.
    # the current node will be added to
    # the inbound_nodes of outbound_layer.
    self.outbound_layer = outbound_layer

    # The following 3 properties describe where
    # the input tensors come from: which layers,
    # and for each layer, which node and which
    # tensor output of each node.

    # List of layer instances.
    self.inbound_layers = inbound_layers
    # List of integers, 1:1 mapping with inbound_layers.
    self.node_indices = node_indices
    # List of integers, 1:1 mapping with inbound_layers.
    self.tensor_indices = tensor_indices

    # Following 2 properties:
    # tensor inputs and outputs of outbound_layer.

    # List of tensors. 1:1 mapping with inbound_layers.
    self.input_tensors = input_tensors
    # List of tensors, created by outbound_layer.call().
    self.output_tensors = output_tensors

    # Following 2 properties: input and output masks.
    # List of tensors, 1:1 mapping with input_tensor.
    self.input_masks = input_masks
    # List of tensors, created by outbound_layer.compute_mask().
    self.output_masks = output_masks

    # Following 2 properties: input and output shapes.

    # List of shape tuples, shapes of input_tensors.
    self.input_shapes = [K.int_shape(x) for x in input_tensors]
    # List of shape tuples, shapes of output_tensors.
    self.output_shapes = [K.int_shape(x) for x in output_tensors]

    # Optional keyword arguments to layer's `call`.
    self.arguments = arguments

    # Add nodes to all layers involved.
    for layer in inbound_layers:
      if layer is not None:
        layer.outbound_nodes.append(self)
    outbound_layer.inbound_nodes.append(self)

  def get_config(self):
    inbound_names = []
    for layer in self.inbound_layers:
      if layer:
        inbound_names.append(layer.name)
      else:
        inbound_names.append(None)
    return {
        'outbound_layer':
            self.outbound_layer.name if self.outbound_layer else None,
        'inbound_layers':
            inbound_names,
        'node_indices':
            self.node_indices,
        'tensor_indices':
            self.tensor_indices
    }


class Layer(tf_base_layers.Layer):
  """Abstract base layer class.

  # Properties
      name: String, must be unique within a model.
      input_spec: List of InputSpec class instances
          each entry describes one required input:
              - ndim
              - dtype
          A layer with `n` input tensors must have
          an `input_spec` of length `n`.
      trainable: Boolean, whether the layer weights
          will be updated during training.
      uses_learning_phase: Whether any operation
          of the layer uses `K.in_training_phase()`
          or `K.in_test_phase()`.
      input_shape: Shape tuple. Provided for convenience,
          but note that there may be cases in which this
          attribute is ill-defined (e.g. a shared layer
          with multiple input shapes), in which case
          requesting `input_shape` will raise an Exception.
          Prefer using `layer.get_input_shape_for(input_shape)`,
          or `layer.get_input_shape_at(node_index)`.
      output_shape: Shape tuple. See above.
      inbound_nodes: List of nodes.
      outbound_nodes: List of nodes.
      input, output: Input/output tensor(s). Note that if the layer is used
          more than once (shared layer), this is ill-defined
          and will raise an exception. In such cases, use
          `layer.get_input_at(node_index)`.
      input_mask, output_mask: Same as above, for masks.
      trainable_weights: List of variables.
      non_trainable_weights: List of variables.
      weights: The concatenation of the lists trainable_weights and
          non_trainable_weights (in this order).
      constraints: Dict mapping weights to constraints.

  # Methods
      call(x, mask=None): Where the layer's logic lives.
      __call__(x, mask=None): Wrapper around the layer logic (`call`).
          If x is a Keras tensor:
              - Connect current layer with last layer from tensor:
                  `self._add_inbound_node(last_layer)`
              - Add layer to tensor history
          If layer is not built:
              - Build from inputs shape
      get_weights()
      set_weights(weights)
      get_config()
      count_params()
      _compute_output_shape(input_shape)
      compute_mask(x, mask)
      get_input_at(node_index)
      get_output_at(node_index)
      get_input_shape_at(node_index)
      get_output_shape_at(node_index)
      get_input_mask_at(node_index)
      get_output_mask_at(node_index)

  # Class Methods
      from_config(config)

  # Internal methods:
      build(input_shape)
      _add_inbound_node(layer, index=0)
  """

  def __init__(self, **kwargs):
    # These properties should be set by the user via keyword arguments.
    # note that 'dtype', 'input_shape' and 'batch_input_shape'
    # are only applicable to input layers: do not pass these keywords
    # to non-input layers.
    allowed_kwargs = {
        'input_shape',
        'batch_input_shape',
        'batch_size',
        'dtype',
        'name',
        'trainable',
        'weights',
    }
    # Validate optional keyword arguments.
    for kwarg in kwargs:
      if kwarg not in allowed_kwargs:
        raise TypeError('Keyword argument not understood:', kwarg)

    # Get layer name.
    name = kwargs.get('name')

    # Get `trainable` status.
    trainable = kwargs.get('trainable', True)

    # Get `dtype`.
    dtype = kwargs.get('dtype')
    if dtype is None:
      dtype = K.floatx()

    # Call super, which will set all properties common to Keras layers
    # and core TF layers.
    super(Layer, self).__init__(name=name, dtype=dtype, trainable=trainable)

    # Add properties that are Keras-only for now.
    self.input_spec = None
    self.supports_masking = False
    self._constraints = {}  # dict {tensor: constraint instance}

    # These lists will be filled via successive calls
    # to self._add_inbound_node().
    self.inbound_nodes = []
    self.outbound_nodes = []

    # Manage input shape information if passed.
    if 'input_shape' in kwargs or 'batch_input_shape' in kwargs:
      # In this case we will later create an input layer
      # to insert before the current layer
      if 'batch_input_shape' in kwargs:
        batch_input_shape = tuple(kwargs['batch_input_shape'])
      elif 'input_shape' in kwargs:
        if 'batch_size' in kwargs:
          batch_size = kwargs['batch_size']
        else:
          batch_size = None
        batch_input_shape = (batch_size,) + tuple(kwargs['input_shape'])
      self.batch_input_shape = batch_input_shape

    # Manage initial weight values if passed.
    if 'weights' in kwargs:
      self._initial_weights = kwargs['weights']
    else:
      self._initial_weights = None

  @property
  def constraints(self):
    return self._constraints

  @constraints.setter
  def constraints(self, constraints):
    self._constraints = constraints

  def add_weight(self,
                 name,
                 shape,
                 dtype=None,
                 initializer=None,
                 regularizer=None,
                 trainable=True,
                 constraint=None):
    """Adds a weight variable to the layer.

    Arguments:
        name: String, the name for the weight variable.
        shape: The shape tuple of the weight.
        dtype: The dtype of the weight.
        initializer: An Initializer instance (callable).
        regularizer: An optional Regularizer instance.
        trainable: A boolean, whether the weight should
            be trained via backprop or not (assuming
            that the layer itself is also trainable).
        constraint: An optional Constraint instance.

    Returns:
        The created weight variable.
    """
    if dtype is None:
      dtype = K.floatx()
    weight = self.add_variable(
        name, shape, dtype=dtype,
        initializer=initializer, regularizer=regularizer, trainable=trainable)
    if constraint is not None:
      self.constraints[weight] = constraint
    return weight

  def call(self, inputs, **kwargs):  # pylint: disable=unused-argument
    """This is where the layer's logic lives.

    Arguments:
        inputs: Input tensor, or list/tuple of input tensors.
        **kwargs: Additional keyword arguments.

    Returns:
        A tensor or list/tuple of tensors.
    """
    return inputs

  def __call__(self, inputs, **kwargs):
    """Wrapper around self.call(), for handling internal references.

    If a Keras tensor is passed:
        - We call self._add_inbound_node().
        - If necessary, we `build` the layer to match
            the shape of the input(s).
        - We update the _keras_history of the output tensor(s)
            with the current layer.
            This is done as part of _add_inbound_node().

    Arguments:
        inputs: Can be a tensor or list/tuple of tensors.
        **kwargs: Additional keyword arguments to be passed to `call()`.

    Returns:
        Output of the layer's `call` method.

    Raises:
        ValueError: in case the layer is missing shape information
            for its `build` call.
    """
    if isinstance(inputs, list):
      inputs = inputs[:]

    # Handle mask propagation.
    previous_mask = _collect_previous_mask(inputs)
    user_kwargs = copy.copy(kwargs)
    if not _is_all_none(previous_mask):
      # The previous layer generated a mask.
      if 'mask' in tf_inspect.getargspec(self.call).args:
        if 'mask' not in kwargs:
          # If mask is explicitly passed to __call__,
          # we should override the default mask.
          kwargs['mask'] = previous_mask

    # Actually call the layer (optionally building it).
    output = super(Layer, self).__call__(inputs, **kwargs)

    # Handle mask computation.
    with K.name_scope(self.name):
      output_mask = self.compute_mask(inputs, previous_mask)

    # Add an inbound node to the layer, so that it keeps track
    # of the call and of all new variables created during the call.
    # This also updates the layer history of the output tensor(s).
    # If the input tensor(s) had not previous Keras history,
    # this does nothing.
    self._add_inbound_node(
        input_tensors=inputs,
        output_tensors=output,
        input_masks=previous_mask,
        output_masks=output_mask,
        arguments=user_kwargs)

    # Optionally load weight values that were specified at layer instantiation.
    if hasattr(self, '_initial_weights') and self._initial_weights is not None:
      self.set_weights(self._initial_weights)
      del self._initial_weights
    return output

  def _add_inbound_node(self,
                        input_tensors,
                        output_tensors,
                        input_masks,
                        output_masks,
                        arguments=None):
    """Internal method to create an inbound node for the layer.

    Arguments:
        input_tensors: list of input tensors.
        output_tensors: list of output tensors.
        input_masks: list of input masks (a mask can be a tensor, or None).
        output_masks: list of output masks (a mask can be a tensor, or None).
        arguments: dictionary of keyword arguments that were passed to the
            `call` method of the layer at the call that created the node.
    """
    input_tensors = _to_list(input_tensors)
    output_tensors = _to_list(output_tensors)
    input_masks = _to_list(input_masks)
    output_masks = _to_list(output_masks)

    # Collect input tensor(s) coordinates.
    inbound_layers = []
    node_indices = []
    tensor_indices = []
    for x in input_tensors:
      if hasattr(x, '_keras_history'):
        inbound_layer, node_index, tensor_index = x._keras_history
        inbound_layers.append(inbound_layer)
        node_indices.append(node_index)
        tensor_indices.append(tensor_index)
      else:
        inbound_layers.append(None)
        node_indices.append(None)
        tensor_indices.append(None)

    # Create node, add it to inbound nodes.
    Node(
        self,
        inbound_layers=inbound_layers,
        node_indices=node_indices,
        tensor_indices=tensor_indices,
        input_tensors=input_tensors,
        output_tensors=output_tensors,
        input_masks=input_masks,
        output_masks=output_masks,
        arguments=arguments)

    # Update tensor history and `_uses_learning_phase`.
    for i in range(len(output_tensors)):
      uses_lp = any(
          [getattr(x, '_uses_learning_phase', False) for x in input_tensors])
      uses_lp = getattr(self, 'uses_learning_phase', False) or uses_lp
      output_tensors[i]._uses_learning_phase = getattr(
          output_tensors[i], '_uses_learning_phase', False) or uses_lp
      output_tensors[i]._keras_history = (self, len(self.inbound_nodes) - 1, i)

  def _compute_output_shape(self, input_shape):
    """Computes the output shape of the layer.

    Assumes that the layer will be built
    to match that input shape provided.

    Arguments:
        input_shape: Shape tuple (tuple of integers)
            or list of shape tuples (one per output tensor of the layer).
            Shape tuples can include None for free dimensions,
            instead of an integer.

    Returns:
        An input shape tuple.
    """
    if isinstance(input_shape, list):
      return [tensor_shape.TensorShape(shape) for shape in input_shape]
    else:
      return tensor_shape.TensorShape(input_shape)

  def compute_mask(self, inputs, mask=None):  # pylint: disable=unused-argument
    """Computes an output mask tensor.

    Arguments:
        inputs: Tensor or list of tensors.
        mask: Tensor or list of tensors.

    Returns:
        None or a tensor (or list of tensors,
            one per output tensor of the layer).
    """
    if not self.supports_masking:
      if mask is not None:
        if isinstance(mask, list):
          if any(m is not None for m in mask):
            raise TypeError('Layer ' + self.name + ' does not support masking, '
                            'but was passed an input_mask: ' + str(mask))
        else:
          raise TypeError('Layer ' + self.name + ' does not support masking, '
                          'but was passed an input_mask: ' + str(mask))
      # masking not explicitly supported: return None as mask
      return None
    # if masking is explicitly supported, by default
    # carry over the input mask
    return mask

  def build(self, input_shape):  # pylint: disable=unused-argument
    """Creates the layer weights.

    Must be implemented on all layers that have weights.

    Arguments:
        input_shape: Keras tensor (future input to layer)
            or list/tuple of Keras tensors to reference
            for weight shape computations.
    """
    self.built = True

  def _get_node_attribute_at_index(self, node_index, attr, attr_name):
    """Retrieves an attribute (e.g. input_tensors) from a node.

    This is used to implement the methods:
        - get_input_shape_at
        - get_output_shape_at
        - get_input_at
        etc...

    Arguments:
        node_index: Integer index of the node from which
            to retrieve the attribute.
        attr: Exact node attribute name.
        attr_name: Human-readable attribute name, for error messages.

    Returns:
        The layer's attribute `attr` at the node of index `node_index`.

    Raises:
        RuntimeError: If the layer has no inbound nodes.
        ValueError: If the index is does not match any node.
    """
    if not self.inbound_nodes:
      raise RuntimeError('The layer has never been called '
                         'and thus has no defined ' + attr_name + '.')
    if not len(self.inbound_nodes) > node_index:
      raise ValueError('Asked to get ' + attr_name + ' at node ' +
                       str(node_index) + ', but the layer has only ' +
                       str(len(self.inbound_nodes)) + ' inbound nodes.')
    values = getattr(self.inbound_nodes[node_index], attr)
    if len(values) == 1:
      return values[0]
    else:
      return values

  def get_input_shape_at(self, node_index):
    """Retrieves the input shape(s) of a layer at a given node.

    Arguments:
        node_index: Integer, index of the node
            from which to retrieve the attribute.
            E.g. `node_index=0` will correspond to the
            first time the layer was called.

    Returns:
        A shape tuple
        (or list of shape tuples if the layer has multiple inputs).
    """
    return self._get_node_attribute_at_index(node_index, 'input_shapes',
                                             'input shape')

  def get_output_shape_at(self, node_index):
    """Retrieves the output shape(s) of a layer at a given node.

    Arguments:
        node_index: Integer, index of the node
            from which to retrieve the attribute.
            E.g. `node_index=0` will correspond to the
            first time the layer was called.

    Returns:
        A shape tuple
        (or list of shape tuples if the layer has multiple outputs).
    """
    return self._get_node_attribute_at_index(node_index, 'output_shapes',
                                             'output shape')

  def get_input_at(self, node_index):
    """Retrieves the input tensor(s) of a layer at a given node.

    Arguments:
        node_index: Integer, index of the node
            from which to retrieve the attribute.
            E.g. `node_index=0` will correspond to the
            first time the layer was called.

    Returns:
        A tensor (or list of tensors if the layer has multiple inputs).
    """
    return self._get_node_attribute_at_index(node_index, 'input_tensors',
                                             'input')

  def get_output_at(self, node_index):
    """Retrieves the output tensor(s) of a layer at a given node.

    Arguments:
        node_index: Integer, index of the node
            from which to retrieve the attribute.
            E.g. `node_index=0` will correspond to the
            first time the layer was called.

    Returns:
        A tensor (or list of tensors if the layer has multiple outputs).
    """
    return self._get_node_attribute_at_index(node_index, 'output_tensors',
                                             'output')

  def get_input_mask_at(self, node_index):
    """Retrieves the input mask tensor(s) of a layer at a given node.

    Arguments:
        node_index: Integer, index of the node
            from which to retrieve the attribute.
            E.g. `node_index=0` will correspond to the
            first time the layer was called.

    Returns:
        A mask tensor
        (or list of tensors if the layer has multiple inputs).
    """
    return self._get_node_attribute_at_index(node_index, 'input_masks',
                                             'input mask')

  def get_output_mask_at(self, node_index):
    """Retrieves the output mask tensor(s) of a layer at a given node.

    Arguments:
        node_index: Integer, index of the node
            from which to retrieve the attribute.
            E.g. `node_index=0` will correspond to the
            first time the layer was called.

    Returns:
        A mask tensor
        (or list of tensors if the layer has multiple outputs).
    """
    return self._get_node_attribute_at_index(node_index, 'output_masks',
                                             'output mask')

  @property
  def input(self):
    """Retrieves the input tensor(s) of a layer.

    Only applicable if the layer has exactly one inbound node,
    i.e. if it is connected to one incoming layer.

    Returns:
        Input tensor or list of input tensors.

    Raises:
        AttributeError: if the layer is connected to
        more than one incoming layers.
    """
    if len(self.inbound_nodes) > 1:
      raise AttributeError('Layer ' + self.name +
                           ' has multiple inbound nodes, '
                           'hence the notion of "layer input" '
                           'is ill-defined. '
                           'Use `get_input_at(node_index)` instead.')
    elif not self.inbound_nodes:
      raise AttributeError('Layer ' + self.name +
                           ' is not connected, no input to return.')
    return self._get_node_attribute_at_index(0, 'input_tensors', 'input')

  @property
  def output(self):
    """Retrieves the output tensor(s) of a layer.

    Only applicable if the layer has exactly one inbound node,
    i.e. if it is connected to one incoming layer.

    Returns:
        Output tensor or list of output tensors.

    Raises:
        AttributeError: if the layer is connected to
        more than one incoming layers.
    """
    if not self.inbound_nodes:
      raise AttributeError('Layer ' + self.name + ' has no inbound nodes.')
    if len(self.inbound_nodes) > 1:
      raise AttributeError('Layer ' + self.name +
                           ' has multiple inbound nodes, '
                           'hence the notion of "layer output" '
                           'is ill-defined. '
                           'Use `get_output_at(node_index)` instead.')
    return self._get_node_attribute_at_index(0, 'output_tensors', 'output')

  @property
  def input_mask(self):
    """Retrieves the input mask tensor(s) of a layer.

    Only applicable if the layer has exactly one inbound node,
    i.e. if it is connected to one incoming layer.

    Returns:
        Input mask tensor (potentially None) or list of input
        mask tensors.

    Raises:
        AttributeError: if the layer is connected to
        more than one incoming layers.
    """
    if len(self.inbound_nodes) != 1:
      raise AttributeError('Layer ' + self.name +
                           ' has multiple inbound nodes, ' +
                           'hence the notion of "layer input mask" '
                           'is ill-defined. '
                           'Use `get_input_mask_at(node_index)` '
                           'instead.')
    return self._get_node_attribute_at_index(0, 'input_masks', 'input mask')

  @property
  def output_mask(self):
    """Retrieves the output mask tensor(s) of a layer.

    Only applicable if the layer has exactly one inbound node,
    i.e. if it is connected to one incoming layer.

    Returns:
        Output mask tensor (potentially None) or list of output
        mask tensors.

    Raises:
        AttributeError: if the layer is connected to
        more than one incoming layers.
    """
    if len(self.inbound_nodes) != 1:
      raise AttributeError('Layer ' + self.name +
                           ' has multiple inbound nodes, '
                           'hence the notion of "layer output mask" '
                           'is ill-defined. '
                           'Use `get_output_mask_at(node_index)` '
                           'instead.')
    return self._get_node_attribute_at_index(0, 'output_masks', 'output mask')

  @property
  def input_shape(self):
    """Retrieves the input shape(s) of a layer.

    Only applicable if the layer has exactly one inbound node,
    i.e. if it is connected to one incoming layer.

    Returns:
        Input shape, as `TensorShape`
        (or list of `TensorShape`, one tuple per input tensor).

    Raises:
        AttributeError: if the layer is connected to
        more than one incoming layers.
    """
    if not self.inbound_nodes:
      raise AttributeError('The layer has never been called '
                           'and thus has no defined input shape.')
    all_input_shapes = set(
        [str(node.input_shapes) for node in self.inbound_nodes])
    if len(all_input_shapes) == 1:
      input_shapes = self.inbound_nodes[0].input_shapes
      if len(input_shapes) == 1:
        return tuple(tensor_shape.TensorShape(input_shapes[0]).as_list())
      else:
        return [
            tuple(tensor_shape.TensorShape(shape).as_list())
            for shape in input_shapes
        ]
    else:
      raise AttributeError('The layer "' + str(self.name) +
                           ' has multiple inbound nodes, '
                           'with different input shapes. Hence '
                           'the notion of "input shape" is '
                           'ill-defined for the layer. '
                           'Use `get_input_shape_at(node_index)` '
                           'instead.')

  @property
  def output_shape(self):
    """Retrieves the output shape(s) of a layer.

    Only applicable if the layer has one inbound node,
    or if all inbound nodes have the same output shape.

    Returns:
        Output shape, as `TensorShape`
        (or list of `TensorShape`, one tuple per output tensor).

    Raises:
        AttributeError: if the layer is connected to
        more than one incoming layers.
    """
    if not self.inbound_nodes:
      raise AttributeError('The layer has never been called '
                           'and thus has no defined output shape.')
    all_output_shapes = set(
        [str(node.output_shapes) for node in self.inbound_nodes])
    if len(all_output_shapes) == 1:
      output_shapes = self.inbound_nodes[0].output_shapes
      if len(output_shapes) == 1:
        return tuple(tensor_shape.TensorShape(output_shapes[0]).as_list())
      else:
        return [
            tuple(tensor_shape.TensorShape(shape).as_list())
            for shape in output_shapes
        ]
    else:
      raise AttributeError('The layer "' + str(self.name) +
                           ' has multiple inbound nodes, '
                           'with different output shapes. Hence '
                           'the notion of "output shape" is '
                           'ill-defined for the layer. '
                           'Use `get_output_shape_at(node_index)` '
                           'instead.')

  def set_weights(self, weights):
    """Sets the weights of the layer, from Numpy arrays.

    Arguments:
        weights: a list of Numpy arrays. The number
            of arrays and their shape must match
            number of the dimensions of the weights
            of the layer (i.e. it should match the
            output of `get_weights`).

    Raises:
        ValueError: If the provided weights list does not match the
            layer's specifications.
    """
    params = self.weights
    if len(params) != len(weights):
      raise ValueError('You called `set_weights(weights)` on layer "' +
                       self.name + '" with a  weight list of length ' +
                       str(len(weights)) + ', but the layer was expecting ' +
                       str(len(params)) + ' weights. Provided weights: ' +
                       str(weights)[:50] + '...')
    if not params:
      return
    weight_value_tuples = []
    param_values = K.batch_get_value(params)
    for pv, p, w in zip(param_values, params, weights):
      if pv.shape != w.shape:
        raise ValueError('Layer weight shape ' + str(pv.shape) +
                         ' not compatible with '
                         'provided weight shape ' + str(w.shape))
      weight_value_tuples.append((p, w))
    K.batch_set_value(weight_value_tuples)

  def get_weights(self):
    """Returns the current weights of the layer.

    Returns:
        Weights values as a list of numpy arrays.
    """
    params = self.weights
    return K.batch_get_value(params)

  def get_config(self):
    """Returns the config of the layer.

    A layer config is a Python dictionary (serializable)
    containing the configuration of a layer.
    The same layer can be reinstantiated later
    (without its trained weights) from this configuration.

    The config of a layer does not include connectivity
    information, nor the layer class name. These are handled
    by `Container` (one layer of abstraction above).

    Returns:
        Python dictionary.
    """
    config = {'name': self.name, 'trainable': self.trainable}
    if hasattr(self, 'batch_input_shape'):
      config['batch_input_shape'] = self.batch_input_shape
    if hasattr(self, 'dtype'):
      config['dtype'] = self.dtype
    return config

  @classmethod
  def from_config(cls, config):
    """Creates a layer from its config.

    This method is the reverse of `get_config`,
    capable of instantiating the same layer from the config
    dictionary. It does not handle layer connectivity
    (handled by Container), nor weights (handled by `set_weights`).

    Arguments:
        config: A Python dictionary, typically the
            output of get_config.

    Returns:
        A layer instance.
    """
    return cls(**config)

  def count_params(self):
    """Count the total number of scalars composing the weights.

    Returns:
        An integer count.

    Raises:
        RuntimeError: if the layer isn't yet built
            (in which case its weights aren't yet defined).
    """
    if not self.built:
      if self.__class__.__name__ == 'Sequential':
        self.build()  # pylint: disable=no-value-for-parameter
      else:
        raise RuntimeError('You tried to call `count_params` on ' + self.name +
                           ', but the layer isn\'t built. '
                           'You can build it manually via: `' + self.name +
                           '.build(batch_input_shape)`.')
    return sum([K.count_params(p) for p in self.weights])


class InputLayer(Layer):
  """Layer to be used as an entry point into a graph.

  It can either wrap an existing tensor (pass an `input_tensor` argument)
  or create its a placeholder tensor (pass arguments `input_shape`
  or `batch_input_shape` as well as `dtype`).

  Arguments:
      input_shape: Shape tuple, not including the batch axis.
      batch_size: Optional input batch size (integer or None).
      batch_input_shape: Shape tuple, including the batch axis.
      dtype: Datatype of the input.
      input_tensor: Optional tensor to use as layer input
          instead of creating a placeholder.
      sparse: Boolean, whether the placeholder created
          is meant to be sparse.
      name: Name of the layer (string).
  """

  def __init__(self,
               input_shape=None,
               batch_size=None,
               batch_input_shape=None,
               dtype=None,
               input_tensor=None,
               sparse=False,
               name=None):
    if not name:
      prefix = 'input'
      name = prefix + '_' + str(K.get_uid(prefix))
    if not dtype:
      if input_tensor is None:
        dtype = K.floatx()
      else:
        dtype = K.dtype(input_tensor)
    super(InputLayer, self).__init__(dtype=dtype, name=name)
    self.built = True
    self.sparse = sparse

    if input_shape and batch_input_shape:
      raise ValueError('Only provide the input_shape OR '
                       'batch_input_shape argument to '
                       'InputLayer, not both at the same time.')
    if input_tensor is not None:
      # Attempt automatic input shape inference.
      try:
        batch_input_shape = K.int_shape(input_tensor)
      except TypeError:
        if not input_shape and not batch_input_shape:
          raise ValueError('InputLayer was provided '
                           'an input_tensor argument, '
                           'but its input shape cannot be '
                           'automatically inferred. '
                           'You should pass an input_shape or '
                           'batch_input_shape argument.')
    if not batch_input_shape:
      if not input_shape:
        raise ValueError('An Input layer should be passed either '
                         'a `batch_input_shape` or an `input_shape`.')
      else:
        batch_input_shape = (batch_size,) + tuple(input_shape)
    else:
      batch_input_shape = tuple(batch_input_shape)
    self.batch_input_shape = batch_input_shape

    if input_tensor is None:
      self.is_placeholder = True
      input_tensor = K.placeholder(
          shape=batch_input_shape,
          dtype=dtype,
          sparse=self.sparse,
          name=self.name)
    else:
      self.is_placeholder = False
    # Create an input node to add to self.outbound_node
    # and set output_tensors' _keras_history.
    input_tensor._uses_learning_phase = False
    input_tensor._keras_history = (self, 0, 0)
    Node(
        self,
        inbound_layers=[],
        node_indices=[],
        tensor_indices=[],
        input_tensors=[input_tensor],
        output_tensors=[input_tensor],
        input_masks=[None],
        output_masks=[None])

  def get_config(self):
    config = {
        'batch_input_shape': self.batch_input_shape,
        'dtype': self.dtype,
        'sparse': self.sparse,
        'name': self.name
    }
    return config


def Input(  # pylint: disable=invalid-name
    shape=None,
    batch_shape=None,
    name=None,
    dtype=K.floatx(),
    sparse=False,
    tensor=None):
  """`Input()` is used to instantiate a Keras tensor.

  A Keras tensor is a tensor object from the underlying backend
  (Theano or TensorFlow), which we augment with certain
  attributes that allow us to build a Keras model
  just by knowing the inputs and outputs of the model.

  For instance, if a, b and c and Keras tensors,
  it becomes possible to do:
  `model = Model(input=[a, b], output=c)`

  The added Keras attribute is:
      `_keras_history`: Last layer applied to the tensor.
          the entire layer graph is retrievable from that layer,
          recursively.

  Arguments:
      shape: A shape tuple (integer), not including the batch size.
          For instance, `shape=(32,)` indicates that the expected input
          will be batches of 32-dimensional vectors.
      batch_shape: A shape tuple (integer), including the batch size.
          For instance, `batch_shape=(10, 32)` indicates that
          the expected input will be batches of 10 32-dimensional vectors.
          `batch_shape=(None, 32)` indicates batches of an arbitrary number
          of 32-dimensional vectors.
      name: An optional name string for the layer.
          Should be unique in a model (do not reuse the same name twice).
          It will be autogenerated if it isn't provided.
      dtype: The data type expected by the input, as a string
          (`float32`, `float64`, `int32`...)
      sparse: A boolean specifying whether the placeholder
          to be created is sparse.
      tensor: Optional existing tensor to wrap into the `Input` layer.
          If set, the layer will not create a placeholder tensor.

  Returns:
      A tensor.

  Example:

      ```python
      # this is a logistic regression in Keras
      x = Input(shape=(32,))
      y = Dense(16, activation='softmax')(x)
      model = Model(x, y)
      ```
  """
  if not batch_shape and tensor is None:
    assert shape, ('Please provide to Input either a `shape`'
                   ' or a `batch_shape` argument. Note that '
                   '`shape` does not include the batch '
                   'dimension.')
  if shape and not batch_shape:
    batch_shape = (None,) + tuple(shape)
  input_layer = InputLayer(
      batch_input_shape=batch_shape,
      name=name,
      dtype=dtype,
      sparse=sparse,
      input_tensor=tensor)
  # Return tensor including `_keras_history`.
  # Note that in this case train_output and test_output are the same pointer.
  outputs = input_layer.inbound_nodes[0].output_tensors
  if len(outputs) == 1:
    return outputs[0]
  else:
    return outputs


class Container(Layer):
  """A Container is a directed acyclic graph of layers.

  It is the topological form of a "model". A Model
  is simply a Container with added training routines.

  # Properties
      name
      inputs
      outputs
      input_layers
      output_layers
      input_spec (list of class instances)
          each entry describes one required input:
              - ndim
              - dtype
      trainable (boolean)
      input_shape
      output_shape
      inbound_nodes: list of nodes
      outbound_nodes: list of nodes
      trainable_weights (list of variables)
      non_trainable_weights (list of variables)
      constraints (list of tuples (weight, constraint))

  # Methods
      summary
      get_layer
      get_weights
      set_weights
      get_config
      compute_output_shape

  # Class Methods
      from_config
  """

  def __init__(self, inputs, outputs, name=None):  # pylint: disable=super-init-not-called
    # Handle `name` argument.
    if not name:
      prefix = self.__class__.__name__.lower()
      name = prefix + '_' + str(K.get_uid(prefix))
    self.name = name
    self.supports_masking = False
    self.trainable = True
    self._per_input_losses = {}
    self._per_input_updates = {}

    # The following properties are not actually used by Keras;
    # they exist for compatibility with TF.
    self._updates = []
    self._scope = None
    self._reuse = None
    self._base_name = name
    self._graph = ops.get_default_graph()

    # Container-specific properties.
    if isinstance(inputs, (list, tuple)):
      self.inputs = list(inputs)  # Tensor or list of tensors.
    else:
      self.inputs = [inputs]
    if isinstance(outputs, (list, tuple)):
      self.outputs = list(outputs)
    else:
      self.outputs = [outputs]

    # Check for redundancy in inputs.
    inputs_set = set(self.inputs)
    if len(inputs_set) != len(self.inputs):
      raise ValueError('The list of inputs passed to the model '
                       'is redundant. '
                       'All inputs should only appear once.'
                       ' Found: ' + str(self.inputs))

    # List of initial layers (1 to 1 mapping with self.inputs,
    # hence the same layer might appear twice)
    self.input_layers = []
    self.input_layers_node_indices = []
    self.input_layers_tensor_indices = []
    # list of layers (1 to 1 mapping with self.inputs,
    # hence the same layer might appear twice)
    self.output_layers = []
    self.output_layers_node_indices = []
    self.output_layers_tensor_indices = []
    # all layers in order of horizontal graph traversal.
    # Entries are unique. Includes input and output layers.
    self.layers = []

    # This is for performance optimization
    # when calling the Container on new inputs.
    # every time the Container is called on a set on input tensors,
    # we compute the output tensors,
    # output masks and output shapes in one pass,
    # then cache them here. When of of these output is queried later,
    # we retrieve it from there instead of recomputing it.
    self._output_mask_cache = {}
    self._output_tensor_cache = {}
    self._output_shape_cache = {}

    # User-provided arguments validation.
    for x in self.inputs:
      # Check that x is a Keras tensor.
      if not hasattr(x, '_keras_history'):
        cls_name = self.__class__.__name__
        raise TypeError('Input tensors to a ' + cls_name + ' ' +
                        'must be Keras tensors. Found: ' + str(x) +
                        ' (missing Keras metadata).')
      # Check that x is an input tensor.
      layer, node_index, tensor_index = x._keras_history
      if len(layer.inbound_nodes) > 1 or (
          layer.inbound_nodes and layer.inbound_nodes[0].inbound_layers):
        cls_name = self.__class__.__name__
        warnings.warn(cls_name + ' inputs must come from '
                      'a Keras Input layer, '
                      'they cannot be the output of '
                      'a previous non-Input layer. '
                      'Here, a tensor specified as '
                      'input to "' + self.name + '" was not an Input tensor, '
                      'it was generated by layer ' + layer.name + '.\n'
                      'Note that input tensors are '
                      'instantiated via `tensor = Input(shape)`.\n'
                      'The tensor that caused the issue was: ' + str(x.name))
    for x in self.outputs:
      if not hasattr(x, '_keras_history'):
        cls_name = self.__class__.__name__
        raise TypeError('Output tensors to a ' + cls_name + ' must be '
                        'Keras tensors. Found: ' + str(x))
    # Build self.output_layers:
    for x in self.outputs:
      layer, node_index, tensor_index = x._keras_history
      self.output_layers.append(layer)
      self.output_layers_node_indices.append(node_index)
      self.output_layers_tensor_indices.append(tensor_index)

    # Fill in the output mask cache.
    masks = []
    for x in self.inputs:
      layer, node_index, tensor_index = x._keras_history
      node = layer.inbound_nodes[node_index]
      mask = node.output_masks[tensor_index]
      masks.append(mask)
    mask_cache_key = ','.join([str(id(x)) for x in self.inputs])
    mask_cache_key += '_' + ','.join([str(id(x)) for x in masks])
    masks = []
    for x in self.outputs:
      layer, node_index, tensor_index = x._keras_history
      node = layer.inbound_nodes[node_index]
      mask = node.output_masks[tensor_index]
      masks.append(mask)
    if len(masks) == 1:
      mask = masks[0]
    else:
      mask = masks
    self._output_mask_cache[mask_cache_key] = mask

    # Build self.input_layers:
    for x in self.inputs:
      layer, node_index, tensor_index = x._keras_history
      # It's supposed to be an input layer, so only one node
      # and one tensor output.
      assert node_index == 0
      assert tensor_index == 0
      self.input_layers.append(layer)
      self.input_layers_node_indices.append(node_index)
      self.input_layers_tensor_indices.append(tensor_index)

    # Build self.input_names and self.output_names.
    self.input_names = []
    self.output_names = []
    self._feed_input_names = []
    self._feed_inputs = []
    self._feed_input_shapes = []
    for i, layer in enumerate(self.input_layers):
      self.input_names.append(layer.name)
      if layer.is_placeholder:
        self._feed_input_names.append(layer.name)
        self._feed_inputs.append(layer.input)
        self._feed_input_shapes.append(K.int_shape(self.inputs[i]))
    for layer in self.output_layers:
      self.output_names.append(layer.name)

    self.internal_input_shapes = [K.int_shape(x) for x in self.inputs]
    self.internal_output_shapes = [K.int_shape(x) for x in self.outputs]

    # Container_nodes: set of nodes included in the graph
    # (not all nodes included in the layers
    # are relevant to the current graph).
    container_nodes = set()  # ids of all nodes relevant to the Container
    nodes_depths = {}  # dict {node: depth value}
    layers_depths = {}  # dict {layer: depth value}
    layer_indices = {}  # dict {layer: index in traversal}

    def make_node_marker(node, depth):
      return str(id(node)) + '-' + str(depth)

    def build_map_of_graph(tensor,
                           seen_nodes=None,
                           depth=0,
                           layer=None,
                           node_index=None,
                           tensor_index=None):
      """Builds a map of the graph of layers.

      This recursively updates the maps `nodes_depths`,
      `layers_depths` and the set `container_nodes`.

      Does not try to detect cycles in the graph.

      Arguments:
          tensor: Some tensor in a graph.
          seen_nodes: Set of node ids ("{layer.name}_ib-{node_index}")
              of nodes seen so far. Useful to prevent infinite loops.
          depth: Current depth in the graph (0 = last output).
          layer: Layer from which `tensor` comes from. If not provided,
              will be obtained from `tensor._keras_history`.
          node_index: Node index from which `tensor` comes from.
          tensor_index: Tensor_index from which `tensor` comes from.
      """
      seen_nodes = seen_nodes or set()
      if not layer or node_index is None or tensor_index is None:
        layer, node_index, tensor_index = tensor._keras_history
      node = layer.inbound_nodes[node_index]

      # Prevent cycles.
      seen_nodes.add(make_node_marker(node, depth))

      node_key = layer.name + '_ib-' + str(node_index)
      # Update container_nodes.
      container_nodes.add(node_key)
      # Update nodes_depths.
      node_depth = nodes_depths.get(node)
      if node_depth is None:
        nodes_depths[node] = depth
      else:
        nodes_depths[node] = max(depth, node_depth)
      # Update layers_depths.
      previously_seen_depth = layers_depths.get(layer)
      if previously_seen_depth is None:
        current_depth = depth
      else:
        current_depth = max(depth, previously_seen_depth)
      layers_depths[layer] = current_depth
      if layer not in layer_indices:
        layer_indices[layer] = len(layer_indices)

      # Propagate to all previous tensors connected to this node.
      for i in range(len(node.inbound_layers)):
        x = node.input_tensors[i]
        layer = node.inbound_layers[i]
        node_index = node.node_indices[i]
        tensor_index = node.tensor_indices[i]
        next_node = layer.inbound_nodes[node_index]
        # use node_marker to prevent cycles
        node_marker = make_node_marker(next_node, current_depth + 1)
        if node_marker not in seen_nodes:
          build_map_of_graph(x, seen_nodes, current_depth + 1, layer,
                             node_index, tensor_index)

    for x in self.outputs:
      seen_nodes = set()
      build_map_of_graph(x, seen_nodes, depth=0)

    # Build a dict {depth: list of nodes with this depth}
    nodes_by_depth = {}
    for node, depth in nodes_depths.items():
      if depth not in nodes_by_depth:
        nodes_by_depth[depth] = []
      nodes_by_depth[depth].append(node)

    # Build a dict {depth: list of layers with this depth}
    layers_by_depth = {}
    for layer, depth in layers_depths.items():
      if depth not in layers_by_depth:
        layers_by_depth[depth] = []
      layers_by_depth[depth].append(layer)

    # Get sorted list of layer depths.
    depth_keys = list(layers_by_depth.keys())
    depth_keys.sort(reverse=True)

    # Set self.layers and self.layers_by_depth.
    layers = []
    for depth in depth_keys:
      layers_for_depth = layers_by_depth[depth]
      # Container.layers needs to have a deterministic order:
      # here we order them by traversal order.
      layers_for_depth.sort(key=lambda x: layer_indices[x])
      for layer in layers_for_depth:
        layers.append(layer)
    self.layers = layers
    self.layers_by_depth = layers_by_depth

    # Get sorted list of node depths.
    depth_keys = list(nodes_by_depth.keys())
    depth_keys.sort(reverse=True)

    # Check that all tensors required are computable.
    # computable_tensors: all tensors in the graph
    # that can be computed from the inputs provided.
    computable_tensors = []
    for x in self.inputs:
      computable_tensors.append(x)

    layers_with_complete_input = []  # To provide a better error msg.
    for depth in depth_keys:
      for node in nodes_by_depth[depth]:
        layer = node.outbound_layer
        if layer:
          for x in node.input_tensors:
            if x not in computable_tensors:
              raise RuntimeError('Graph disconnected: '
                                 'cannot obtain value for tensor ' + str(x) +
                                 ' at layer "' + layer.name + '". '
                                 'The following previous layers '
                                 'were accessed without issue: ' +
                                 str(layers_with_complete_input))
          for x in node.output_tensors:
            computable_tensors.append(x)
          layers_with_complete_input.append(layer.name)

    # Set self.nodes and self.nodes_by_depth.
    self.container_nodes = container_nodes
    self.nodes_by_depth = nodes_by_depth

    # Ensure name unicity, which will be crucial for serialization
    # (since serialized nodes refer to layers by their name).
    all_names = [layer.name for layer in self.layers]
    for name in all_names:
      if all_names.count(name) != 1:
        raise RuntimeError('The name "' + name + '" is used ' +
                           str(all_names.count(name)) + ' times in the model. '
                           'All layer names should be unique.')

    # Layer parameters.
    # The new container starts with a single inbound node
    # for its inputs, and no outbound nodes.
    self.outbound_nodes = []  # Will be appended to by future calls to __call__
    self.inbound_nodes = [
    ]  # Will be appended to below, and by future calls to __call__
    # Create the node linking internal inputs to internal outputs.
    Node(
        outbound_layer=self,
        inbound_layers=[],
        node_indices=[],
        tensor_indices=[],
        input_tensors=self.inputs,
        output_tensors=self.outputs,
        # No container-level masking for now.
        input_masks=[None for _ in self.inputs],
        output_masks=[None for _ in self.outputs])
    self.built = True

    # The following are implemented as property functions:
    # self.constraints
    # self.trainable_weights
    # self.non_trainable_weights
    # self.input_spec

  def get_layer(self, name=None, index=None):
    """Retrieves a layer based on either its name (unique) or index.

    Indices are based on order of horizontal graph traversal (bottom-up).

    Arguments:
        name: String, name of layer.
        index: Integer, index of layer.

    Returns:
        A layer instance.

    Raises:
        ValueError: In case of invalid layer name or index.
    """
    # It would be unreliable to build a dictionary
    # based on layer names, because names can potentially
    # be changed at any point by the user
    # without the container being notified of it.
    if index is not None:
      if len(self.layers) <= index:
        raise ValueError('Was asked to retrieve layer at index ' + str(index) +
                         ' but model only has ' + str(len(self.layers)) +
                         ' layers.')
      else:
        return self.layers[index]
    else:
      if not name:
        raise ValueError('Provide either a layer name or layer index.')
    layer = None
    for layer in self.layers:
      if layer.name == name:
        return layer
    if not layer:
      raise ValueError('No such layer: ' + name)

  @property
  def updates(self):
    """Retrieve the model's updates.

    Will only include updates that are either
    inconditional, or conditional on inputs to this model
    (e.g. will not include updates that depend on tensors
    that aren't inputs to this model).

    Returns:
        A list of update ops.
    """
    updates = []
    for layer in self.layers:
      if hasattr(layer, 'updates'):
        # Collect updates that are dependent on inputs
        # that are part of the model.
        for node_index, node in enumerate(layer.inbound_nodes):
          node_key = layer.name + '_ib-' + str(node_index)
          if node_key in self.container_nodes:
            # The model owns this layer node.
            inputs = node.input_tensors
            updates += layer.get_updates_for(inputs)
        # Collect unconditional updates.
        updates += layer.get_updates_for(None)
    return updates

  @property
  def losses(self):
    """Retrieve the model's losses.

    Will only include losses that are either
    inconditional, or conditional on inputs to this model
    (e.g. will not include losses that depend on tensors
    that aren't inputs to this model).

    Returns:
        A list of loss tensors.
    """
    losses = []
    # Retrieve losses for all internal layers.
    for layer in self.layers:
      if hasattr(layer, 'losses'):
        # Collect losses that are dependent on inputs
        # that are part of the model.
        for node_index, node in enumerate(layer.inbound_nodes):
          node_key = layer.name + '_ib-' + str(node_index)
          if node_key in self.container_nodes:
            # The model owns this layer node.
            inputs = node.input_tensors
            losses += layer.get_losses_for(inputs)
        # Collect unconditional losses.
        losses += layer.get_losses_for(None)
    # Add any potential unconditional model-level loss.
    losses += self.get_losses_for(None)
    return losses

  @property
  def uses_learning_phase(self):
    return any([x._uses_learning_phase for x in self.outputs])

  @property
  def stateful(self):
    return any([(hasattr(layer, 'stateful') and layer.stateful)
                for layer in self.layers])

  def reset_states(self):
    for layer in self.layers:
      if hasattr(layer, 'reset_states') and getattr(layer, 'stateful', False):
        layer.reset_states()

  @property
  def state_updates(self):
    """Returns the `updates` from all layers that are stateful.

    This is useful for separating training updates and
    state updates, e.g. when we need to update a layer's internal state
    during prediction.

    Returns:
        A list of update ops.
    """
    state_updates = []
    for layer in self.layers:
      if getattr(layer, 'stateful', False):
        if hasattr(layer, 'updates'):
          state_updates += layer.updates
    return state_updates

  @property
  def constraints(self):
    cons = {}
    for layer in self.layers:
      for key, value in layer.constraints.items():
        if key in cons and cons[key] != value:
          raise ValueError('Received multiple constraints '
                           'for one weight tensor: ' + str(key))
        cons[key] = value
    return cons

  @property
  def trainable_weights(self):
    if not self.trainable:
      return []
    weights = []
    for layer in self.layers:
      weights += layer.trainable_weights
    return weights

  @property
  def non_trainable_weights(self):
    weights = []
    for layer in self.layers:
      weights += layer.non_trainable_weights
    if not self.trainable:
      trainable_weights = []
      for layer in self.layers:
        trainable_weights += layer.trainable_weights
      return trainable_weights + weights
    return weights

  def get_weights(self):
    """Retrieves the weights of the model.

    Returns:
        A flat list of Numpy arrays.
    """
    weights = []
    for layer in self.layers:
      weights += layer.weights
    return K.batch_get_value(weights)

  def set_weights(self, weights):
    """Sets the weights of the model.

    Arguments:
        weights: A list of Numpy arrays with shapes and types matching
            the output of `model.get_weights()`.
    """
    tuples = []
    for layer in self.layers:
      num_param = len(layer.weights)
      layer_weights = weights[:num_param]
      for sw, w in zip(layer.weights, layer_weights):
        tuples.append((sw, w))
      weights = weights[num_param:]
    K.batch_set_value(tuples)

  @property
  def input_spec(self):
    """Gets the model's input specs.

    Returns:
        A list of `InputSpec` instances (one per input to the model)
            or a single instance if the model has only one input.
    """
    specs = []
    for layer in getattr(self, 'input_layers', []):
      if layer.input_spec is None:
        specs.append(None)
      else:
        if not isinstance(layer.input_spec, list):
          raise TypeError('Layer ' + layer.name +
                          ' has an input_spec attribute that '
                          'is not a list. We expect a list. '
                          'Found input_spec = ' + str(layer.input_spec))
        specs += layer.input_spec
    if len(specs) == 1:
      return specs[0]
    return specs

  def call(self, inputs, mask=None):
    """Call the model on new inputs.

    In this case `call` just reapplies
    all ops in the graph to the new inputs
    (e.g. build a new computational graph from the provided inputs).

    A model is callable on non-Keras tensors.

    Arguments:
        inputs: A tensor or list of tensors.
        mask: A mask or list of masks. A mask can be
            either a tensor or None (no mask).

    Returns:
        A tensor if there is a single output, or
        a list of tensors if there are more than one outputs.
    """
    inputs = _to_list(inputs)
    if mask is None:
      masks = [None for _ in range(len(inputs))]
    else:
      masks = _to_list(mask)
    cache_key = ','.join([str(id(x)) for x in inputs])
    cache_key += '_' + ','.join([str(id(x)) for x in masks])
    if cache_key in self._output_tensor_cache:
      return self._output_tensor_cache[cache_key]
    else:
      output_tensors, _, _ = self.run_internal_graph(inputs, masks)
      return output_tensors

  def compute_mask(self, inputs, mask):
    inputs = _to_list(inputs)
    if mask is None:
      masks = [None for _ in range(len(inputs))]
    else:
      masks = _to_list(mask)
    cache_key = ','.join([str(id(x)) for x in inputs])
    cache_key += '_' + ','.join([str(id(x)) for x in masks])
    if cache_key in self._output_mask_cache:
      return self._output_mask_cache[cache_key]
    else:
      _, output_masks, _ = self.run_internal_graph(inputs, masks)
      return output_masks

  def _compute_output_shape(self, input_shape):
    if isinstance(input_shape, list):
      input_shapes = []
      for shape in input_shape:
        if shape is not None:
          input_shapes.append(tuple(tensor_shape.TensorShape(shape).as_list()))
        else:
          input_shapes.append(None)
    else:
      if input_shape is not None:
        input_shapes = [tuple(tensor_shape.TensorShape(input_shape).as_list())]
      else:
        input_shapes = [None]

    if len(input_shapes) != len(self.input_layers):
      raise ValueError('Invalid input_shape argument ' + str(input_shape) +
                       ': model has ' + str(len(self.input_layers)) +
                       ' tensor inputs.')

    cache_key = ','.join([str(x) for x in input_shapes])
    if cache_key in self._output_shape_cache:
      output_shapes = self._output_shape_cache[cache_key]
      if isinstance(output_shapes, list):
        if len(output_shapes) == 1:
          return tensor_shape.TensorShape(output_shapes[0])
        else:
          return [tensor_shape.TensorShape(shape) for shape in output_shapes]
      else:
        return tensor_shape.TensorShape(output_shapes)
    else:
      # Bad luck, we have to run the graph manually.
      layers_to_output_shapes = {}
      for i in range(len(input_shapes)):
        layer = self.input_layers[i]
        input_shape = input_shapes[i]
        # It's an input layer: compute_output_shape is identity,
        # and there is only one node and one tensor output.
        shape_key = layer.name + '_0_0'
        layers_to_output_shapes[shape_key] = input_shape

      depth_keys = list(self.nodes_by_depth.keys())
      depth_keys.sort(reverse=True)
      # Iterate over nodes, by depth level.
      if len(depth_keys) > 1:
        for depth in depth_keys:
          nodes = self.nodes_by_depth[depth]
          for node in nodes:
            # This is always a single layer, never a list.
            layer = node.outbound_layer
            if layer in self.input_layers:
              # We've already covered the input layers
              # a few lines above.
              continue
            # Potentially redundant list,
            # same size of node.input_tensors.
            input_shapes = []
            for j in range(len(node.inbound_layers)):
              inbound_layer = node.inbound_layers[j]
              node_index = node.node_indices[j]
              tensor_index = node.tensor_indices[j]
              shape_key = inbound_layer.name + '_%s_%s' % (node_index,
                                                           tensor_index)
              input_shape = layers_to_output_shapes[shape_key]
              input_shapes.append(input_shape)

            if len(input_shapes) == 1:
              output_shape = layer._compute_output_shape(input_shapes[0])
            else:
              output_shape = layer._compute_output_shape(input_shapes)
            if isinstance(output_shape, list):
              output_shapes = [
                  tuple(tensor_shape.TensorShape(shape).as_list())
                  for shape in output_shape
              ]
            else:
              output_shapes = [
                  tuple(tensor_shape.TensorShape(output_shape).as_list())
              ]

            node_index = layer.inbound_nodes.index(node)
            for j in range(len(output_shapes)):
              shape_key = layer.name + '_%s_%s' % (node_index, j)
              layers_to_output_shapes[shape_key] = output_shapes[j]

      # Read final output shapes from layers_to_output_shapes.
      output_shapes = []
      output_shape_keys = []
      for i in range(len(self.output_layers)):
        layer = self.output_layers[i]
        node_index = self.output_layers_node_indices[i]
        tensor_index = self.output_layers_tensor_indices[i]
        shape_key = layer.name + '_%s_%s' % (node_index, tensor_index)
        output_shape_keys.append(shape_key)

      for i, key in enumerate(output_shape_keys):
        assert key in layers_to_output_shapes
        output_shapes.append(layers_to_output_shapes[key])
      # Store in cache.
      self._output_shape_cache[cache_key] = output_shapes
      if isinstance(output_shapes, list):
        if len(output_shapes) == 1:
          return tensor_shape.TensorShape(output_shapes[0])
        else:
          return [tensor_shape.TensorShape(shape) for shape in output_shapes]
      else:
        return tensor_shape.TensorShape(output_shapes)

  def run_internal_graph(self, inputs, masks=None):
    """Computes output tensors for new inputs.

    # Note:
        - Expects `inputs` to be a list (potentially with 1 element).
        - Can be run on non-Keras tensors.

    Arguments:
        inputs: List of tensors
        masks: List of masks (tensors or None).

    Returns:
        Three lists: output_tensors, output_masks, output_shapes
    """
    if masks is None:
      masks = [None for _ in range(len(inputs))]

    # Dictionary mapping reference tensors to tuples
    # (computed tensor, compute mask)
    # we assume a 1:1 mapping from tensor to mask
    # TODO(fchollet): raise exception when a `.compute_mask()` call
    # does not return a list the same size as `call`
    tensor_map = {}
    for x, y, mask in zip(self.inputs, inputs, masks):
      tensor_map[str(id(x))] = (y, mask)

    depth_keys = list(self.nodes_by_depth.keys())
    depth_keys.sort(reverse=True)
    for depth in depth_keys:
      nodes = self.nodes_by_depth[depth]
      for node in nodes:
        # This is always a single layer, never a list.
        layer = node.outbound_layer

        reference_input_tensors = node.input_tensors
        reference_output_tensors = node.output_tensors

        # If all previous input tensors are available in tensor_map,
        # then call node.inbound_layer on them.
        computed_data = []  # List of tuples (input, mask).
        for x in reference_input_tensors:
          if str(id(x)) in tensor_map:
            computed_data.append(tensor_map[str(id(x))])

        if len(computed_data) == len(reference_input_tensors):
          # call layer
          with K.name_scope(layer.name):
            if node.arguments:
              kwargs = node.arguments
            else:
              kwargs = {}
            if len(computed_data) == 1:
              computed_tensor, computed_mask = computed_data[0]
              if 'mask' in tf_inspect.getargspec(layer.call).args:
                if 'mask' not in kwargs:
                  kwargs['mask'] = computed_mask
              output_tensors = _to_list(layer.call(computed_tensor, **kwargs))
              output_masks = _to_list(
                  layer.compute_mask(computed_tensor, computed_mask))
              computed_tensors = [computed_tensor]
              computed_masks = [computed_mask]
            else:
              computed_tensors = [x[0] for x in computed_data]
              computed_masks = [x[1] for x in computed_data]
              if 'mask' in tf_inspect.getargspec(layer.call).args:
                if 'mask' not in kwargs:
                  kwargs['mask'] = computed_masks
              output_tensors = _to_list(layer.call(computed_tensors, **kwargs))
              output_masks = _to_list(
                  layer.compute_mask(computed_tensors, computed_masks))

            # Apply activity regularizer if any:
            if hasattr(layer, 'activity_regularizer'
                      ) and layer.activity_regularizer is not None:
              regularization_losses = [
                  layer.activity_regularizer(x) for x in computed_tensors
              ]
              layer.add_loss(regularization_losses, computed_tensors)

          # Update model updates and losses:
          # Keep track of updates that depend on the inputs
          # (e.g. BN updates).
          self.add_update(layer.get_updates_for(computed_tensors), inputs)
          # Keep track of unconditional updates (e.g. a counter).
          self.add_update(layer.get_updates_for(None), None)
          # Keep track of losses that depend on the inputs
          # (e.g. activity regularizers).
          self.add_loss(layer.get_losses_for(computed_tensors), inputs)
          # Keep track of unconditional losses
          # (e.g. weight regularizers).
          self.add_loss(layer.get_losses_for(None), None)

          # Update `_uses_learning_phase`.
          if len(computed_tensors) == 1:
            uses_learning_phase = getattr(computed_tensors[0],
                                          '_uses_learning_phase', False)
          else:
            uses_learning_phase = any([
                getattr(x, '_uses_learning_phase', False)
                for x in computed_tensors
            ])
          for x in output_tensors:
            x._uses_learning_phase = getattr(x, '_uses_learning_phase',
                                             False) or uses_learning_phase

          # Update tensor_map.
          for x, y, mask in zip(reference_output_tensors, output_tensors,
                                output_masks):
            tensor_map[str(id(x))] = (y, mask)

    output_tensors = []
    output_masks = []
    output_shapes = []
    for x in self.outputs:
      assert str(id(x)) in tensor_map, 'Could not compute output ' + str(x)
      tensor, mask = tensor_map[str(id(x))]
      output_shapes.append(K.int_shape(x))
      output_tensors.append(tensor)
      output_masks.append(mask)

    # Update cache;
    # keys are based on ids on input tensors and inputs masks.
    cache_key = ','.join([str(id(x)) for x in inputs])
    cache_key += '_' + ','.join([str(id(x)) for x in masks])

    if len(output_tensors) == 1:
      output_tensors = output_tensors[0]
      self._output_tensor_cache[cache_key] = output_tensors
    else:
      self._output_tensor_cache[cache_key] = output_tensors

    if len(output_masks) == 1:
      output_masks = output_masks[0]
      self._output_mask_cache[cache_key] = output_masks
    else:
      self._output_mask_cache[cache_key] = output_masks

    if output_shapes is not None:
      input_shapes = [K.int_shape(x) for x in inputs]
      cache_key = ','.join([str(x) for x in input_shapes])
      if len(output_shapes) == 1:
        output_shapes = output_shapes[0]
        self._output_shape_cache[cache_key] = output_shapes
      else:
        self._output_shape_cache[cache_key] = output_shapes
    return output_tensors, output_masks, output_shapes

  def get_config(self):
    config = {
        'name': self.name,
    }
    node_conversion_map = {}
    for layer in self.layers:
      if issubclass(layer.__class__, Container):
        # Containers start with a pre-existing node
        # linking their input to output.
        kept_nodes = 1
      else:
        kept_nodes = 0
      for original_node_index, node in enumerate(layer.inbound_nodes):
        node_key = layer.name + '_ib-' + str(original_node_index)
        if node_key in self.container_nodes:
          node_conversion_map[node_key] = kept_nodes
          kept_nodes += 1
    layer_configs = []
    for layer in self.layers:  # From the earliest layers on.
      layer_class_name = layer.__class__.__name__
      layer_config = layer.get_config()
      filtered_inbound_nodes = []
      for original_node_index, node in enumerate(layer.inbound_nodes):
        node_key = layer.name + '_ib-' + str(original_node_index)
        if node_key in self.container_nodes:
          # The node is relevant to the model:
          # add to filtered_inbound_nodes.
          if node.arguments:
            try:
              json.dumps(node.arguments)
              kwargs = node.arguments
            except TypeError:
              warnings.warn('Layer ' + layer.name +
                            ' was passed non-serializable keyword arguments: ' +
                            str(node.arguments) + '. They will not be included '
                            'in the serialized model (and thus will be missing '
                            'at deserialization time).')
              kwargs = {}
          else:
            kwargs = {}
          if node.inbound_layers:
            node_data = []
            for i in range(len(node.inbound_layers)):
              inbound_layer = node.inbound_layers[i]
              node_index = node.node_indices[i]
              tensor_index = node.tensor_indices[i]
              node_key = inbound_layer.name + '_ib-' + str(node_index)
              new_node_index = node_conversion_map.get(node_key, 0)
              node_data.append(
                  [inbound_layer.name, new_node_index, tensor_index, kwargs])
            filtered_inbound_nodes.append(node_data)
      layer_configs.append({
          'name': layer.name,
          'class_name': layer_class_name,
          'config': layer_config,
          'inbound_nodes': filtered_inbound_nodes,
      })
    config['layers'] = layer_configs

    # Gather info about inputs and outputs.
    model_inputs = []
    for i in range(len(self.input_layers)):
      layer = self.input_layers[i]
      node_index = self.input_layers_node_indices[i]
      node_key = layer.name + '_ib-' + str(node_index)
      new_node_index = node_conversion_map[node_key]
      tensor_index = self.input_layers_tensor_indices[i]
      model_inputs.append([layer.name, new_node_index, tensor_index])
    config['input_layers'] = model_inputs
    model_outputs = []
    for i in range(len(self.output_layers)):
      layer = self.output_layers[i]
      node_index = self.output_layers_node_indices[i]
      node_key = layer.name + '_ib-' + str(node_index)
      new_node_index = node_conversion_map[node_key]
      tensor_index = self.output_layers_tensor_indices[i]
      model_outputs.append([layer.name, new_node_index, tensor_index])
    config['output_layers'] = model_outputs
    return copy.deepcopy(config)

  @classmethod
  def from_config(cls, config, custom_objects=None):
    """Instantiates a Model from its config (output of `get_config()`).

    Arguments:
        config: Model config dictionary.
        custom_objects: Optional dictionary mapping names
            (strings) to custom classes or functions to be
            considered during deserialization.

    Returns:
        A model instance.

    Raises:
        ValueError: In case of improperly formatted config dict.
    """
    # layer instances created during
    # the graph reconstruction process
    created_layers = {}

    def process_layer(layer_data):
      """Deserialize a layer, then call it on appropriate inputs.

      Arguments:
          layer_data: layer config dict.

      Raises:
          ValueError: In case of improperly formatted `layer_data` dict.
      """
      layer_name = layer_data['name']

      # Instantiate layer.
      from tensorflow.contrib.keras.python.keras.layers import deserialize as deserialize_layer  # pylint: disable=g-import-not-at-top
      layer = deserialize_layer(layer_data, custom_objects=custom_objects)
      created_layers[layer_name] = layer

      # Gather layer inputs.
      inbound_nodes_data = layer_data['inbound_nodes']
      for node_data in inbound_nodes_data:
        input_tensors = []
        for input_data in node_data:
          inbound_layer_name = input_data[0]
          inbound_node_index = input_data[1]
          inbound_tensor_index = input_data[2]
          if len(input_data) == 3:
            kwargs = {}
          elif len(input_data) == 4:
            kwargs = input_data[3]
          else:
            raise ValueError('Improperly formatted model config.')
          if inbound_layer_name not in created_layers:
            raise ValueError('Missing layer: ' + inbound_layer_name)
          inbound_layer = created_layers[inbound_layer_name]
          inbound_node = inbound_layer.inbound_nodes[inbound_node_index]
          input_tensors.append(
              inbound_node.output_tensors[inbound_tensor_index])
        # Call layer on its inputs, thus creating the node
        # and building the layer if needed.
        if input_tensors:
          if len(input_tensors) == 1:
            layer(input_tensors[0], **kwargs)
          else:
            layer(input_tensors, **kwargs)

    for layer_data in config['layers']:
      process_layer(layer_data)

    name = config.get('name')
    input_tensors = []
    output_tensors = []
    for layer_data in config['input_layers']:
      layer_name, node_index, tensor_index = layer_data
      assert layer_name in created_layers
      layer = created_layers[layer_name]
      layer_output_tensors = layer.inbound_nodes[node_index].output_tensors
      input_tensors.append(layer_output_tensors[tensor_index])
    for layer_data in config['output_layers']:
      layer_name, node_index, tensor_index = layer_data
      assert layer_name in created_layers
      layer = created_layers[layer_name]
      layer_output_tensors = layer.inbound_nodes[node_index].output_tensors
      output_tensors.append(layer_output_tensors[tensor_index])
    return cls(inputs=input_tensors, outputs=output_tensors, name=name)

  def save(self, filepath, overwrite=True, include_optimizer=True):
    """Save the model to a single HDF5 file.

    The savefile includes:
        - The model architecture, allowing to re-instantiate the model.
        - The model weights.
        - The state of the optimizer, allowing to resume training
            exactly where you left off.

    This allows you to save the entirety of the state of a model
    in a single file.

    Saved models can be reinstantiated via `keras.models.load_model`.
    The model returned by `load_model`
    is a compiled model ready to be used (unless the saved model
    was never compiled in the first place).

    Arguments:
        filepath: String, path to the file to save the weights to.
        overwrite: Whether to silently overwrite any existing file at the
            target location, or provide the user with a manual prompt.
        include_optimizer: If True, save optimizer's state together.

    Example:

    ```python
    from keras.models import load_model

    model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'
    del model  # deletes the existing model

    # returns a compiled model
    # identical to the previous one
    model = load_model('my_model.h5')
    ```
    """
    from tensorflow.contrib.keras.python.keras.models import save_model  # pylint: disable=g-import-not-at-top
    save_model(self, filepath, overwrite, include_optimizer)

  def save_weights(self, filepath, overwrite=True):
    """Dumps all layer weights to a HDF5 file.

    The weight file has:
        - `layer_names` (attribute), a list of strings
            (ordered names of model layers).
        - For every layer, a `group` named `layer.name`
            - For every such layer group, a group attribute `weight_names`,
                a list of strings
                (ordered names of weights tensor of the layer).
            - For every weight in the layer, a dataset
                storing the weight value, named after the weight tensor.

    Arguments:
        filepath: String, path to the file to save the weights to.
        overwrite: Whether to silently overwrite any existing file at the
            target location, or provide the user with a manual prompt.

    Raises:
        ImportError: If h5py is not available.
    """
    if h5py is None:
      raise ImportError('`save_weights` requires h5py.')
    # If file exists and should not be overwritten:
    if not overwrite and os.path.isfile(filepath):
      proceed = ask_to_proceed_with_overwrite(filepath)
      if not proceed:
        return
    f = h5py.File(filepath, 'w')
    save_weights_to_hdf5_group(f, self.layers)
    f.flush()
    f.close()

  def load_weights(self, filepath, by_name=False):
    """Loads all layer weights from a HDF5 save file.

    If `by_name` is False (default) weights are loaded
    based on the network's topology, meaning the architecture
    should be the same as when the weights were saved.
    Note that layers that don't have weights are not taken
    into account in the topological ordering, so adding or
    removing layers is fine as long as they don't have weights.

    If `by_name` is True, weights are loaded into layers
    only if they share the same name. This is useful
    for fine-tuning or transfer-learning models where
    some of the layers have changed.

    Arguments:
        filepath: String, path to the weights file to load.
        by_name: Boolean, whether to load weights by name
            or by topological order.

    Raises:
        ImportError: If h5py is not available.
    """
    if h5py is None:
      raise ImportError('`load_weights` requires h5py.')
    f = h5py.File(filepath, mode='r')
    if 'layer_names' not in f.attrs and 'model_weights' in f:
      f = f['model_weights']
    if by_name:
      load_weights_from_hdf5_group_by_name(f, self.layers)
    else:
      load_weights_from_hdf5_group(f, self.layers)

    if hasattr(f, 'close'):
      f.close()

  def _updated_config(self):
    """Util hared between different serialization methods.

    Returns:
        Model config with Keras version information added.
    """
    from tensorflow.contrib.keras.python.keras import __version__ as keras_version  # pylint: disable=g-import-not-at-top

    config = self.get_config()
    model_config = {
        'class_name': self.__class__.__name__,
        'config': config,
        'keras_version': keras_version,
        'backend': K.backend()
    }
    return model_config

  def to_json(self, **kwargs):
    """Returns a JSON string containing the network configuration.

    To load a network from a JSON save file, use
    `keras.models.model_from_json(json_string, custom_objects={})`.

    Arguments:
        **kwargs: Additional keyword arguments
            to be passed to `json.dumps()`.

    Returns:
        A JSON string.
    """

    def get_json_type(obj):
      # If obj is any numpy type
      if type(obj).__module__ == np.__name__:
        return obj.item()

      # If obj is a python 'type'
      if type(obj).__name__ == type.__name__:
        return obj.__name__

      raise TypeError('Not JSON Serializable:', obj)

    model_config = self._updated_config()
    return json.dumps(model_config, default=get_json_type, **kwargs)

  def to_yaml(self, **kwargs):
    """Returns a yaml string containing the network configuration.

    To load a network from a yaml save file, use
    `keras.models.model_from_yaml(yaml_string, custom_objects={})`.

    `custom_objects` should be a dictionary mapping
    the names of custom losses / layers / etc to the corresponding
    functions / classes.

    Arguments:
        **kwargs: Additional keyword arguments
            to be passed to `yaml.dump()`.

    Returns:
        A YAML string.

    Raises:
        ImportError: if yaml module is not found.
    """
    if yaml is None:
      raise ImportError('Requires yaml module installed.')
    return yaml.dump(self._updated_config(), **kwargs)

  def summary(self, line_length=None, positions=None):
    print_layer_summary(self, line_length=line_length, positions=positions)


def get_source_inputs(tensor, layer=None, node_index=None):
  """Returns the list of input tensors necessary to compute `tensor`.

  Output will always be a list of tensors
  (potentially with 1 element).

  Arguments:
      tensor: The tensor to start from.
      layer: Origin layer of the tensor. Will be
          determined via tensor._keras_history if not provided.
      node_index: Origin node index of the tensor.

  Returns:
      List of input tensors.
  """
  if not hasattr(tensor, '_keras_history'):
    return tensor

  if layer is None or node_index:
    layer, node_index, _ = tensor._keras_history
  if not layer.inbound_nodes:
    return [tensor]
  else:
    node = layer.inbound_nodes[node_index]
    if not node.inbound_layers:
      # Reached an Input layer, stop recursion.
      return node.input_tensors
    else:
      source_tensors = []
      for i in range(len(node.inbound_layers)):
        x = node.input_tensors[i]
        layer = node.inbound_layers[i]
        node_index = node.node_indices[i]
        previous_sources = get_source_inputs(x, layer, node_index)
        # Avoid input redundancy.
        for x in previous_sources:
          if x not in source_tensors:
            source_tensors.append(x)
      return source_tensors


def _to_list(x):
  """Normalizes a list/tensor into a list.

  If a tensor is passed, we return
  a list of size 1 containing the tensor.

  Arguments:
      x: target object to be normalized.

  Returns:
      A list.
  """
  if isinstance(x, list):
    return x
  return [x]


def _object_list_uid(object_list):
  object_list = _to_list(object_list)
  return ', '.join([str(abs(id(x))) for x in object_list])


def _is_all_none(iterable_or_element):
  if not isinstance(iterable_or_element, (list, tuple)):
    iterable = [iterable_or_element]
  else:
    iterable = iterable_or_element
  for element in iterable:
    if element is not None:
      return False
  return True


def _collect_previous_mask(input_tensors):
  """Retrieves the output mask(s) of the previous node.

  Arguments:
      input_tensors: A tensor or list of tensors.

  Returns:
      A mask tensor or list of mask tensors.
  """
  input_tensors = _to_list(input_tensors)
  masks = []
  for x in input_tensors:
    if hasattr(x, '_keras_history'):
      inbound_layer, node_index, tensor_index = x._keras_history
      node = inbound_layer.inbound_nodes[node_index]
      mask = node.output_masks[tensor_index]
      masks.append(mask)
    else:
      masks.append(None)
  if len(masks) == 1:
    return masks[0]
  return masks


def _to_snake_case(name):
  intermediate = re.sub('(.)([A-Z][a-z0-9]+)', r'\1_\2', name)
  insecure = re.sub('([a-z])([A-Z])', r'\1_\2', intermediate).lower()
  # If the class is private the name starts with "_" which is not secure
  # for creating scopes. We prefix the name with "private" in this case.
  if insecure[0] != '_':
    return insecure
  return 'private' + insecure


def _collect_input_shape(input_tensors):
  """Collects the output shape(s) of a list of Keras tensors.

  Arguments:
      input_tensors: list of input tensors (or single input tensor).

  Returns:
      List of shape tuples (or single tuple), one tuple per input.
  """
  input_tensors = _to_list(input_tensors)
  shapes = []
  for x in input_tensors:
    shapes.append(K.int_shape(x))
  if len(shapes) == 1:
    return shapes[0]
  return shapes


def save_weights_to_hdf5_group(f, layers):
  from tensorflow.contrib.keras.python.keras import __version__ as keras_version  # pylint: disable=g-import-not-at-top

  f.attrs['layer_names'] = [layer.name.encode('utf8') for layer in layers]
  f.attrs['backend'] = K.backend().encode('utf8')
  f.attrs['keras_version'] = str(keras_version).encode('utf8')

  for layer in layers:
    g = f.create_group(layer.name)
    symbolic_weights = layer.weights
    weight_values = K.batch_get_value(symbolic_weights)
    weight_names = []
    for i, (w, val) in enumerate(zip(symbolic_weights, weight_values)):
      if hasattr(w, 'name') and w.name:
        name = str(w.name)
      else:
        name = 'param_' + str(i)
      weight_names.append(name.encode('utf8'))
    g.attrs['weight_names'] = weight_names
    for name, val in zip(weight_names, weight_values):
      param_dset = g.create_dataset(name, val.shape, dtype=val.dtype)
      if not val.shape:
        # scalar
        param_dset[()] = val
      else:
        param_dset[:] = val


def preprocess_weights_for_loading(layer,
                                   weights,
                                   original_keras_version=None,
                                   original_backend=None):
  """Converts layers weights from Keras 1 format to Keras 2.

  Arguments:
      layer: Layer instance.
      weights: List of weights values (Numpy arrays).
      original_keras_version: Keras version for the weights, as a string.
      original_backend: Keras backend the weights were trained with,
          as a string.

  Returns:
      A list of weights values (Numpy arrays).
  """
  if original_keras_version == '1':
    if layer.__class__.__name__ == 'Conv1D':
      shape = weights[0].shape
      # Handle Keras 1.1 format
      if shape[:2] != (layer.kernel_size[0], 1) or shape[3] != layer.filters:
        # Legacy shape:
        # (filters, input_dim, filter_length, 1)
        assert shape[0] == layer.filters and shape[2:] == (layer.kernel_size[0],
                                                           1)
        weights[0] = np.transpose(weights[0], (2, 3, 1, 0))
      weights[0] = weights[0][:, 0, :, :]

    if layer.__class__.__name__ == 'Conv2D':
      if layer.data_format == 'channels_first':
        # old: (filters, stack_size, kernel_rows, kernel_cols)
        # new: (kernel_rows, kernel_cols, stack_size, filters)
        weights[0] = np.transpose(weights[0], (2, 3, 1, 0))

    if layer.__class__.__name__ == 'Conv2DTranspose':
      if layer.data_format == 'channels_last':
        # old: (kernel_rows, kernel_cols, stack_size, filters)
        # new: (kernel_rows, kernel_cols, filters, stack_size)
        weights[0] = np.transpose(weights[0], (0, 1, 3, 2))
      if layer.data_format == 'channels_first':
        # old: (filters, stack_size, kernel_rows, kernel_cols)
        # new: (kernel_rows, kernel_cols, filters, stack_size)
        weights[0] = np.transpose(weights[0], (2, 3, 0, 1))

    if layer.__class__.__name__ == 'Conv3D':
      if layer.data_format == 'channels_first':
        # old: (filters, stack_size, ...)
        # new: (..., stack_size, filters)
        weights[0] = np.transpose(weights[0], (2, 3, 4, 1, 0))

    if layer.__class__.__name__ == 'GRU':
      if len(weights) == 9:
        kernel = np.concatenate([weights[0], weights[3], weights[6]], axis=-1)
        recurrent_kernel = np.concatenate(
            [weights[1], weights[4], weights[7]], axis=-1)
        bias = np.concatenate([weights[2], weights[5], weights[8]], axis=-1)
        weights = [kernel, recurrent_kernel, bias]

    if layer.__class__.__name__ == 'LSTM':
      if len(weights) == 12:
        # old: i, c, f, o
        # new: i, f, c, o
        kernel = np.concatenate(
            [weights[0], weights[6], weights[3], weights[9]], axis=-1)
        recurrent_kernel = np.concatenate(
            [weights[1], weights[7], weights[4], weights[10]], axis=-1)
        bias = np.concatenate(
            [weights[2], weights[8], weights[5], weights[11]], axis=-1)
        weights = [kernel, recurrent_kernel, bias]

    if layer.__class__.__name__ == 'ConvLSTM2D':
      if len(weights) == 12:
        kernel = np.concatenate(
            [weights[0], weights[6], weights[3], weights[9]], axis=-1)
        recurrent_kernel = np.concatenate(
            [weights[1], weights[7], weights[4], weights[10]], axis=-1)
        bias = np.concatenate(
            [weights[2], weights[8], weights[5], weights[11]], axis=-1)
        if layer.data_format == 'channels_first':
          # old: (filters, stack_size, kernel_rows, kernel_cols)
          # new: (kernel_rows, kernel_cols, stack_size, filters)
          kernel = np.transpose(kernel, (2, 3, 1, 0))
          recurrent_kernel = np.transpose(recurrent_kernel, (2, 3, 1, 0))
        weights = [kernel, recurrent_kernel, bias]

  if original_backend and K.backend() != original_backend:
    conv_layers = ['Conv1D', 'Conv2D', 'Conv3D', 'Conv2DTranspose']
    if layer.__class__.__name__ in conv_layers:
      weights[0] = conv_utils.convert_kernel(weights[0])
    if layer.__class__.__name__ == 'ConvLSTM2D':
      weights[0] = conv_utils.convert_kernel(weights[0])
      weights[1] = conv_utils.convert_kernel(weights[1])
  return weights


def load_weights_from_hdf5_group(f, layers):
  """Implements topological (order-based) weight loading.

  Arguments:
      f: A pointer to a HDF5 group.
      layers: a list of target layers.

  Raises:
      ValueError: in case of mismatch between provided layers
          and weights file.
  """
  if 'keras_version' in f.attrs:
    original_keras_version = f.attrs['keras_version'].decode('utf8')
  else:
    original_keras_version = '1'
  if 'backend' in f.attrs:
    original_backend = f.attrs['backend'].decode('utf8')
  else:
    original_backend = None

  filtered_layers = []
  for layer in layers:
    weights = layer.weights
    if weights:
      filtered_layers.append(layer)

  layer_names = [n.decode('utf8') for n in f.attrs['layer_names']]
  filtered_layer_names = []
  for name in layer_names:
    g = f[name]
    weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]
    if weight_names:
      filtered_layer_names.append(name)
  layer_names = filtered_layer_names
  if len(layer_names) != len(filtered_layers):
    raise ValueError('You are trying to load a weight file '
                     'containing ' + str(len(layer_names)) +
                     ' layers into a model with ' + str(len(filtered_layers)) +
                     ' layers.')

  # We batch weight value assignments in a single backend call
  # which provides a speedup in TensorFlow.
  weight_value_tuples = []
  for k, name in enumerate(layer_names):
    g = f[name]
    weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]
    weight_values = [g[weight_name] for weight_name in weight_names]
    layer = filtered_layers[k]
    symbolic_weights = layer.weights
    weight_values = preprocess_weights_for_loading(
        layer, weight_values, original_keras_version, original_backend)
    if len(weight_values) != len(symbolic_weights):
      raise ValueError('Layer #' + str(k) + ' (named "' + layer.name +
                       '" in the current model) was found to '
                       'correspond to layer ' + name + ' in the save file. '
                       'However the new layer ' + layer.name + ' expects ' +
                       str(len(symbolic_weights)) +
                       ' weights, but the saved weights have ' +
                       str(len(weight_values)) + ' elements.')
    weight_value_tuples += zip(symbolic_weights, weight_values)
  K.batch_set_value(weight_value_tuples)


def load_weights_from_hdf5_group_by_name(f, layers):
  """Implements name-based weight loading.

  (instead of topological weight loading).

  Layers that have no matching name are skipped.

  Arguments:
      f: A pointer to a HDF5 group.
      layers: a list of target layers.

  Raises:
      ValueError: in case of mismatch between provided layers
          and weights file.
  """
  if 'keras_version' in f.attrs:
    original_keras_version = f.attrs['keras_version'].decode('utf8')
  else:
    original_keras_version = '1'
  if 'backend' in f.attrs:
    original_backend = f.attrs['backend'].decode('utf8')
  else:
    original_backend = None

  # New file format.
  layer_names = [n.decode('utf8') for n in f.attrs['layer_names']]

  # Reverse index of layer name to list of layers with name.
  index = {}
  for layer in layers:
    if layer.name:
      index.setdefault(layer.name, []).append(layer)

  # We batch weight value assignments in a single backend call
  # which provides a speedup in TensorFlow.
  weight_value_tuples = []
  for k, name in enumerate(layer_names):
    g = f[name]
    weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]
    weight_values = [g[weight_name] for weight_name in weight_names]

    for layer in index.get(name, []):
      symbolic_weights = layer.weights
      weight_values = preprocess_weights_for_loading(
          layer, weight_values, original_keras_version, original_backend)
      if len(weight_values) != len(symbolic_weights):
        raise ValueError('Layer #' + str(k) + ' (named "' + layer.name +
                         '") expects ' + str(len(symbolic_weights)) +
                         ' weight(s), but the saved weights' + ' have ' +
                         str(len(weight_values)) + ' element(s).')
      # Set values.
      for i in range(len(weight_values)):
        weight_value_tuples.append((symbolic_weights[i], weight_values[i]))
  K.batch_set_value(weight_value_tuples)
