old_contents: import pytest
  import numpy as np
  
  from keras import backend as K
  from keras_contrib import backend as KC
  from keras_contrib.layers import core
  from keras.utils.test_utils import layer_test, keras_test
  from numpy.testing import assert_allclose
  
  
  @keras_test
  def test_cosinedense():
      from keras import regularizers
      from keras import constraints
      from keras.models import Sequential
  
      layer_test(core.CosineDense,
                 kwargs={'output_dim': 3},
                 input_shape=(3, 2))
  
      layer_test(core.CosineDense,
                 kwargs={'output_dim': 3},
                 input_shape=(3, 4, 2))
  
      layer_test(core.CosineDense,
                 kwargs={'output_dim': 3},
                 input_shape=(None, None, 2))
  
      layer_test(core.CosineDense,
                 kwargs={'output_dim': 3},
                 input_shape=(3, 4, 5, 2))
  
      layer_test(core.CosineDense,
                 kwargs={'output_dim': 3,
                         'W_regularizer': regularizers.l2(0.01),
                         'b_regularizer': regularizers.l1(0.01),
                         'activity_regularizer': regularizers.activity_l2(0.01),
                         'W_constraint': constraints.MaxNorm(1),
                         'b_constraint': constraints.MaxNorm(1)},
                 input_shape=(3, 2))
  
      X = np.random.randn(1, 20)
      model = Sequential()
      model.add(core.CosineDense(1, bias=True, input_shape=(20,)))
      model.compile(loss='mse', optimizer='rmsprop')
      W = model.get_weights()
      W[0] = X.T
      W[1] = np.asarray([1.])
      model.set_weights(W)
      out = model.predict(X)
      assert_allclose(out, np.ones((1, 1), dtype=K.floatx()))
  
      X = np.random.randn(1, 20)
      model = Sequential()
      model.add(core.CosineDense(1, bias=False, input_shape=(20,)))
      model.compile(loss='mse', optimizer='rmsprop')
      W = model.get_weights()
      W[0] = -X.T
      model.set_weights(W)
      out = model.predict(X)
      assert_allclose(out, -np.ones((1, 1), dtype=K.floatx()))
  
  
  if __name__ == '__main__':
      pytest.main([__file__])
  
new_contents: import pytest
  import numpy as np
  
  from keras import backend as K
  from keras_contrib import backend as KC
  from keras_contrib.layers import core
  from keras.utils.test_utils import layer_test, keras_test
  from numpy.testing import assert_allclose
  
  
  @keras_test
  def test_cosinedense():
      from keras import regularizers
      from keras import constraints
      from keras.models import Sequential
  
      layer_test(core.CosineDense,
                 kwargs={'output_dim': 3},
                 input_shape=(3, 2))
  
      layer_test(core.CosineDense,
                 kwargs={'output_dim': 3},
                 input_shape=(3, 4, 2))
  
      layer_test(core.CosineDense,
                 kwargs={'output_dim': 3},
                 input_shape=(None, None, 2))
  
      layer_test(core.CosineDense,
                 kwargs={'output_dim': 3},
                 input_shape=(3, 4, 5, 2))
  
      layer_test(core.CosineDense,
                 kwargs={'output_dim': 3,
                         'W_regularizer': regularizers.l2(0.01),
                         'b_regularizer': regularizers.l1(0.01),
                         'activity_regularizer': regularizers.activity_l2(0.01),
                         'W_constraint': constraints.MaxNorm(1),
                         'b_constraint': constraints.MaxNorm(1)},
                 input_shape=(3, 2))
  
      X = np.random.randn(1, 20)
      model = Sequential()
      model.add(core.CosineDense(1, bias=True, input_shape=(20,)))
      model.compile(loss='mse', optimizer='rmsprop')
      W = model.get_weights()
      W[0] = X.T
      W[1] = np.asarray([1.])
      model.set_weights(W)
      out = model.predict(X)
      assert_allclose(out, np.ones((1, 1), dtype=K.floatx()), atol=1e-5)
  
      X = np.random.randn(1, 20)
      model = Sequential()
      model.add(core.CosineDense(1, bias=False, input_shape=(20,)))
      model.compile(loss='mse', optimizer='rmsprop')
      W = model.get_weights()
      W[0] = -X.T
      model.set_weights(W)
      out = model.predict(X)
      assert_allclose(out, -np.ones((1, 1), dtype=K.floatx()), atol=1e-5)
  
  
  if __name__ == '__main__':
      pytest.main([__file__])
  
subject: change atol on allclose