def test_normalize_data_format(self):
    self.assertEqual('channels_last',
                     conv_utils.normalize_data_format('Channels_Last'))
    self.assertEqual('channels_first',
                     conv_utils.normalize_data_format('CHANNELS_FIRST'))

    with self.assertRaises(ValueError):
      conv_utils.normalize_data_format('invalid')

 def __init__(self,
              filters,
              kernel_size,
              strides=1,
              padding='valid',
              data_format=None,
              activation=None,
              use_bias=True,
              kernel_initializer='glorot_uniform',
              bias_initializer='zeros',
              kernel_regularizer=None,
              bias_regularizer=None,
              activity_regularizer=None,
              kernel_constraint=None,
              bias_constraint=None,
              **kwargs):
   super(LocallyConnected1D, self).__init__(**kwargs)
   self.filters = filters
   self.kernel_size = conv_utils.normalize_tuple(kernel_size, 1, 'kernel_size')
   self.strides = conv_utils.normalize_tuple(strides, 1, 'strides')
   self.padding = conv_utils.normalize_padding(padding)
   if self.padding != 'valid':
     raise ValueError('Invalid border mode for LocallyConnected1D '
                      '(only "valid" is supported): ' + padding)
   self.data_format = conv_utils.normalize_data_format(data_format)
   self.activation = activations.get(activation)
   self.use_bias = use_bias
   self.kernel_initializer = initializers.get(kernel_initializer)
   self.bias_initializer = initializers.get(bias_initializer)
   self.kernel_regularizer = regularizers.get(kernel_regularizer)
   self.bias_regularizer = regularizers.get(bias_regularizer)
   self.activity_regularizer = regularizers.get(activity_regularizer)
   self.kernel_constraint = constraints.get(kernel_constraint)
   self.bias_constraint = constraints.get(bias_constraint)
   self.input_spec = InputSpec(ndim=3)

  def __init__(self,
               filters,
               kernel_size,
               strides=(1, 1),
               padding='valid',
               data_format=None,
               dilation_rate=(1, 1),
               activation='tanh',
               recurrent_activation='hard_sigmoid',
               use_bias=True,
               kernel_initializer='glorot_uniform',
               recurrent_initializer='orthogonal',
               bias_initializer='zeros',
               unit_forget_bias=True,
               kernel_regularizer=None,
               recurrent_regularizer=None,
               bias_regularizer=None,
               kernel_constraint=None,
               recurrent_constraint=None,
               bias_constraint=None,
               dropout=0.,
               recurrent_dropout=0.,
               **kwargs):
    super(ConvLSTM2DCell, self).__init__(**kwargs)
    self.filters = filters
    self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size')
    self.strides = conv_utils.normalize_tuple(strides, 2, 'strides')
    self.padding = conv_utils.normalize_padding(padding)
    self.data_format = conv_utils.normalize_data_format(data_format)
    self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, 2,
                                                    'dilation_rate')
    self.activation = activations.get(activation)
    self.recurrent_activation = activations.get(recurrent_activation)
    self.use_bias = use_bias

    self.kernel_initializer = initializers.get(kernel_initializer)
    self.recurrent_initializer = initializers.get(recurrent_initializer)
    self.bias_initializer = initializers.get(bias_initializer)
    self.unit_forget_bias = unit_forget_bias

    self.kernel_regularizer = regularizers.get(kernel_regularizer)
    self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
    self.bias_regularizer = regularizers.get(bias_regularizer)

    self.kernel_constraint = constraints.get(kernel_constraint)
    self.recurrent_constraint = constraints.get(recurrent_constraint)
    self.bias_constraint = constraints.get(bias_constraint)

    self.dropout = min(1., max(0., dropout))
    self.recurrent_dropout = min(1., max(0., recurrent_dropout))
    self.state_size = (self.filters, self.filters)
    self._dropout_mask = None
    self._recurrent_dropout_mask = None

 def __init__(self, pool_function, pool_size, strides,
              padding='valid', data_format='channels_last',
              name=None, **kwargs):
   super(Pooling1D, self).__init__(name=name, **kwargs)
   if data_format is None:
     data_format = backend.image_data_format()
   if strides is None:
     strides = pool_size
   self.pool_function = pool_function
   self.pool_size = conv_utils.normalize_tuple(pool_size, 1, 'pool_size')
   self.strides = conv_utils.normalize_tuple(strides, 1, 'strides')
   self.padding = conv_utils.normalize_padding(padding)
   self.data_format = conv_utils.normalize_data_format(data_format)
   self.input_spec = InputSpec(ndim=3)

 def __init__(self, data_format=None, **kwargs):
   super(Flatten, self).__init__(**kwargs)
   self.data_format = conv_utils.normalize_data_format(data_format)
   self.input_spec = InputSpec(min_ndim=2)