Python tensorflow.complex128() Examples

def testDtype(self):
    with self.test_session():
      d = tf.fill([2, 3], 12., name="fill")
      self.assertEqual(d.get_shape(), [2, 3])
      # Test default type for both constant size and dynamic size
      z = tf.zeros([2, 3])
      self.assertEqual(z.dtype, tf.float32)
      self.assertEqual([2, 3], z.get_shape())
      self.assertAllEqual(z.eval(), np.zeros([2, 3]))
      z = tf.zeros(tf.shape(d))
      self.assertEqual(z.dtype, tf.float32)
      self.assertEqual([2, 3], z.get_shape())
      self.assertAllEqual(z.eval(), np.zeros([2, 3]))
      # Test explicit type control
      for dtype in [tf.float32, tf.float64, tf.int32,
                    tf.uint8, tf.int16, tf.int8,
                    tf.complex64, tf.complex128, tf.int64, tf.bool]:
        z = tf.zeros([2, 3], dtype=dtype)
        self.assertEqual(z.dtype, dtype)
        self.assertEqual([2, 3], z.get_shape())
        self.assertAllEqual(z.eval(), np.zeros([2, 3]))
        z = tf.zeros(tf.shape(d), dtype=dtype)
        self.assertEqual(z.dtype, dtype)
        self.assertEqual([2, 3], z.get_shape())
        self.assertAllEqual(z.eval(), np.zeros([2, 3])) 

def testComplex128N(self):
    t = tensor_util.make_tensor_proto([(1+2j), (3+4j), (5+6j)], shape=[1, 3],
                                      dtype=tf.complex128)
    self.assertProtoEquals("""
      dtype: DT_COMPLEX128
      tensor_shape { dim { size: 1 } dim { size: 3 } }
      dcomplex_val: 1
      dcomplex_val: 2
      dcomplex_val: 3
      dcomplex_val: 4
      dcomplex_val: 5
      dcomplex_val: 6
      """, t)
    a = tensor_util.MakeNdarray(t)
    self.assertEquals(np.complex128, a.dtype)
    self.assertAllEqual(np.array([[(1+2j), (3+4j), (5+6j)]]), a) 

def testComplex128NpArray(self):
    t = tensor_util.make_tensor_proto(
        np.array([[(1+2j), (3+4j)], [(5+6j), (7+8j)]]), dtype=tf.complex128)
    # scomplex_val are real_0, imag_0, real_1, imag_1, ...
    self.assertProtoEquals("""
      dtype: DT_COMPLEX128
      tensor_shape { dim { size: 2 } dim { size: 2 } }
      dcomplex_val: 1
      dcomplex_val: 2
      dcomplex_val: 3
      dcomplex_val: 4
      dcomplex_val: 5
      dcomplex_val: 6
      dcomplex_val: 7
      dcomplex_val: 8
      """, t)
    a = tensor_util.MakeNdarray(t)
    self.assertEquals(np.complex128, a.dtype)
    self.assertAllEqual(np.array([[(1+2j), (3+4j)], [(5+6j), (7+8j)]]), a) 

def _testGrad(self, shape, dtype=None, max_error=None, bias=None, sigma=None):
    np.random.seed(7)
    if dtype in (tf.complex64, tf.complex128):
      value = tf.complex(self._biasedRandN(shape, bias=bias, sigma=sigma),
                         self._biasedRandN(shape, bias=bias, sigma=sigma))
    else:
      value = tf.convert_to_tensor(self._biasedRandN(shape, bias=bias),
                                   dtype=dtype)

    with self.test_session(use_gpu=True):
      if dtype in (tf.complex64, tf.complex128):
        output = tf.complex_abs(value)
      else:
        output = tf.abs(value)
      error = tf.test.compute_gradient_error(
          value, shape, output, output.get_shape().as_list())
    self.assertLess(error, max_error) 

def testTensorCompareTensor(self):
    x = np.linspace(-15, 15, 6).reshape(1, 3, 2)
    y = np.linspace(20, -10, 6).reshape(1, 3, 2)
    for t in [np.float16, np.float32, np.float64, np.int32, np.int64]:
      xt = x.astype(t)
      yt = y.astype(t)
      self._compareBoth(xt, yt, np.less, tf.less)
      self._compareBoth(xt, yt, np.less_equal, tf.less_equal)
      self._compareBoth(xt, yt, np.greater, tf.greater)
      self._compareBoth(xt, yt, np.greater_equal, tf.greater_equal)
      self._compareBoth(xt, yt, np.equal, tf.equal)
      self._compareBoth(xt, yt, np.not_equal, tf.not_equal)
    # TODO(zhifengc): complex64 doesn't work on GPU yet.
    for t in [np.complex64, np.complex128]:
      self._compareCpu(x.astype(t), y.astype(t), np.equal, tf.equal)
      self._compareCpu(x.astype(t), y.astype(t), np.not_equal, tf.not_equal) 

def testValues(self):
    dtypes = [tf.float32,
              tf.float64,
              tf.int64,
              tf.int32,
              tf.complex64,
              tf.complex128]
    indices_flat = np.array([0, 4, 0, 8, 3, 8, 4, 7, 7, 3])
    num_segments = 12
    for indices in indices_flat, indices_flat.reshape(5, 2):
      shape = indices.shape + (2,)
      for dtype in dtypes:
        with self.test_session(use_gpu=self.use_gpu):
          tf_x, np_x = self._input(shape, dtype=dtype)
          np_ans = self._segmentReduce(indices,
                                       np_x,
                                       np.add,
                                       op2=None,
                                       num_out_rows=num_segments)
          s = tf.unsorted_segment_sum(data=tf_x,
                                      segment_ids=indices,
                                      num_segments=num_segments)
          tf_ans = s.eval()
        self._assertAllClose(indices, np_ans, tf_ans)
        self.assertShapeEqual(np_ans, s) 

def _testBCastByFunc(self, funcs, xs, ys):
    dtypes = [
        np.float16,
        np.float32,
        np.float64,
        np.int32,
        np.int64,
        np.complex64,
        np.complex128,
    ]
    for dtype in dtypes:
      for (np_func, tf_func) in funcs:
        if (dtype in (np.complex64, np.complex128) and
              tf_func in (_FLOORDIV, tf.floordiv)):
          continue  # floordiv makes no sense for complex numbers
        self._compareBCast(xs, ys, dtype, np_func, tf_func)
        self._compareBCast(ys, xs, dtype, np_func, tf_func) 

def _compareBCast(self, xs, ys, dtype, np_func, tf_func):
    if dtype in (np.complex64, np.complex128):
      x = (1 + np.linspace(0, 2 + 3j, np.prod(xs))).astype(dtype).reshape(xs)
      y = (1 + np.linspace(0, 2 - 2j, np.prod(ys))).astype(dtype).reshape(ys)
    else:
      x = (1 + np.linspace(0, 5, np.prod(xs))).astype(dtype).reshape(xs)
      y = (1 + np.linspace(0, 5, np.prod(ys))).astype(dtype).reshape(ys)
    self._compareCpu(x, y, np_func, tf_func)
    if x.dtype in (np.float16, np.float32, np.float64, np.complex64,
                   np.complex128):
      if tf_func not in (_FLOORDIV, tf.floordiv):
        if x.dtype == np.float16:
          # Compare fp16 theoretical gradients to fp32 numerical gradients,
          # since fp16 numerical gradients are too imprecise unless great
          # care is taken with choosing the inputs and the delta. This is
          # a weaker check (in particular, it does not test the op itself,
          # only its gradient), but it's much better than nothing.
          self._compareGradientX(x, y, np_func, tf_func, np.float)
          self._compareGradientY(x, y, np_func, tf_func, np.float)
        else:
          self._compareGradientX(x, y, np_func, tf_func)
          self._compareGradientY(x, y, np_func, tf_func)
      self._compareGpu(x, y, np_func, tf_func)

  # TODO(josh11b,vrv): Refactor this to use parameterized tests. 

def _toDataType(self, dtype):
    """Returns TensorFlow data type for numpy type."""
    if dtype == np.float32:
      return tf.float32
    elif dtype == np.float64:
      return tf.float64
    elif dtype == np.int32:
      return tf.int32
    elif dtype == np.int64:
      return tf.int64
    elif dtype == np.bool:
      return tf.bool
    elif dtype == np.complex64:
      return tf.complex64
    elif dtype == np.complex128:
      return tf.complex128
    else:
      return None 

def channels_to_complex(image,
                        data_format='channels_last',
                        name='channels2complex'):
    """Convert data from channels to complex."""
    if len(image.shape) != 3 and len(image.shape) != 4:
        raise TypeError('Input data must be have 3 or 4 dimensions')

    axis_c = -1 if data_format == 'channels_last' else -3
    shape_c = image.shape[axis_c].value

    if shape_c and (shape_c % 2 != 0):
        raise TypeError(
            'Number of channels (%d) must be divisible by 2' % shape_c)
    if image.dtype is tf.complex64 or image.dtype is tf.complex128:
        raise TypeError('Input data cannot be complex')

    with tf.name_scope(name):
        image_real, image_imag = tf.split(image, 2, axis=axis_c)
        image_out = tf.complex(image_real, image_imag)
    return image_out 

def _testTypes(self, x, use_gpu=False):
    """Tests cast(x) to different tf."""
    if use_gpu:
      type_list = [np.float32, np.float64, np.int64,
                   np.complex64, np.complex128]
    else:
      type_list = [np.float32, np.float64, np.int32,
                   np.int64, np.complex64, np.complex128]
    for from_type in type_list:
      for to_type in type_list:
        self._test(x.astype(from_type), to_type, use_gpu)

    self._test(x.astype(np.bool), np.float32, use_gpu)
    self._test(x.astype(np.uint8), np.float32, use_gpu)
    if not use_gpu:
      self._test(x.astype(np.bool), np.int32, use_gpu)
      self._test(x.astype(np.int32), np.int32, use_gpu) 

def testDtype(self):
    with self.test_session():
      d = tf.fill([2, 3], 12., name="fill")
      self.assertEqual(d.get_shape(), [2, 3])
      # Test default type for both constant size and dynamic size
      z = tf.ones([2, 3])
      self.assertEqual(z.dtype, tf.float32)
      self.assertEqual([2, 3], z.get_shape())
      self.assertAllEqual(z.eval(), np.ones([2, 3]))
      z = tf.ones(tf.shape(d))
      self.assertEqual(z.dtype, tf.float32)
      self.assertEqual([2, 3], z.get_shape())
      self.assertAllEqual(z.eval(), np.ones([2, 3]))
      # Test explicit type control
      for dtype in (tf.float32, tf.float64, tf.int32,
                    tf.uint8, tf.int16, tf.int8,
                    tf.complex64, tf.complex128, tf.int64, tf.bool):
        z = tf.ones([2, 3], dtype=dtype)
        self.assertEqual(z.dtype, dtype)
        self.assertEqual([2, 3], z.get_shape())
        self.assertAllEqual(z.eval(), np.ones([2, 3]))
        z = tf.ones(tf.shape(d), dtype=dtype)
        self.assertEqual(z.dtype, dtype)
        self.assertEqual([2, 3], z.get_shape())
        self.assertAllEqual(z.eval(), np.ones([2, 3])) 

def call(self, inputx):
        
        if not inputx.dtype in [tf.complex64, tf.complex128]:
            print('Warning: inputx is not complex. Converting.', file=sys.stderr)
        
            # if inputx is float, this will assume 0 imag channel
            inputx = tf.cast(inputx, tf.complex64)
        
        # get the right fft
        if self.ndims == 1:
            ifft = tf.ifft
        elif self.ndims == 2:
            ifft = tf.ifft2d
        else:
            ifft = tf.ifft3d

        perm_dims = [0, self.ndims + 1] + list(range(1, self.ndims + 1))
        invert_perm_ndims = [0] + list(range(2, self.ndims + 2)) + [1]
        
        perm_inputx = K.permute_dimensions(inputx, perm_dims)  # [batch_size, nb_features, *vol_size]
        ifft_inputx = ifft(perm_inputx)
        return K.permute_dimensions(ifft_inputx, invert_perm_ndims) 

def call(self, inputx):
        
        if not inputx.dtype in [tf.complex64, tf.complex128]:
            print('Warning: inputx is not complex. Converting.', file=sys.stderr)
        
            # if inputx is float, this will assume 0 imag channel
            inputx = tf.cast(inputx, tf.complex64)

        # get the right fft
        if self.ndims == 1:
            fft = tf.fft
        elif self.ndims == 2:
            fft = tf.fft2d
        else:
            fft = tf.fft3d

        perm_dims = [0, self.ndims + 1] + list(range(1, self.ndims + 1))
        invert_perm_ndims = [0] + list(range(2, self.ndims + 2)) + [1]
        
        perm_inputx = K.permute_dimensions(inputx, perm_dims)  # [batch_size, nb_features, *vol_size]
        fft_inputx = fft(perm_inputx)
        return K.permute_dimensions(fft_inputx, invert_perm_ndims) 

def testDtypes(self):
    with self.test_session() as sess:
      dtypes = [tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8,
                tf.int64, tf.bool, tf.complex64, tf.complex128]
      shape = (32, 4, 128)
      q = tf.FIFOQueue(32, dtypes, [shape[1:]] * len(dtypes))

      input_tuple = []
      for dtype in dtypes:
        np_dtype = dtype.as_numpy_dtype
        np_array = np.random.randint(-10, 10, shape)
        if dtype == tf.bool:
          np_array = np_array > 0
        elif dtype in (tf.complex64, tf.complex128):
          np_array = np.sqrt(np_array.astype(np_dtype))
        else:
          np_array = np_array.astype(np_dtype)
        input_tuple.append(np_array)

      q.enqueue_many(input_tuple).run()

      output_tuple_t = q.dequeue_many(32)
      output_tuple = sess.run(output_tuple_t)

      for (input_elem, output_elem) in zip(input_tuple, output_tuple):
        self.assertAllEqual(input_elem, output_elem) 

def testValues(self):
    dtypes = [tf.float32,
              tf.float64,
              tf.int64,
              tf.int32,
              tf.complex64,
              tf.complex128]

    # Each item is np_op1, np_op2, tf_op
    ops_list = [(np.add, None, tf.segment_sum),
                (self._mean_cum_op, self._mean_reduce_op,
                 tf.segment_mean),
                (np.ndarray.__mul__, None, tf.segment_prod),
                (np.minimum, None, tf.segment_min),
                (np.maximum, None, tf.segment_max)]

    # A subset of ops has been enabled for complex numbers
    complex_ops_list = [(np.add, None, tf.segment_sum),
                        (np.ndarray.__mul__, None, tf.segment_prod)]

    n = 10
    shape = [n, 2]
    indices = [i // 3 for i in range(n)]
    for dtype in dtypes:
      if dtype in (tf.complex64, tf.complex128):
        curr_ops_list = complex_ops_list
      else:
        curr_ops_list = ops_list

      with self.test_session(use_gpu=False):
        tf_x, np_x = self._input(shape, dtype=dtype)
        for np_op1, np_op2, tf_op in curr_ops_list:
          np_ans = self._segmentReduce(indices, np_x, np_op1, np_op2)
          s = tf_op(data=tf_x, segment_ids=indices)
          tf_ans = s.eval()
          self._assertAllClose(indices, np_ans, tf_ans)
          # NOTE(mrry): The static shape inference that computes
          # `tf_ans.shape` can only infer that sizes from dimension 1
          # onwards, because the size of dimension 0 is data-dependent
          # and may therefore vary dynamically.
          self.assertAllEqual(np_ans.shape[1:], tf_ans.shape[1:]) 

def testFillComplex128(self):
    np_ans = np.array([[0.15] * 3] * 2).astype(np.complex128)
    self._compare([2, 3], np_ans[0][0], np_ans, use_gpu=False) 

def testEmptySecondDimension(self):
    dtypes = [np.float32,
              np.float64,
              np.int64,
              np.int32,
              np.complex64,
              np.complex128]
    with self.test_session(use_gpu=self.use_gpu):
      for dtype in dtypes:
        for itype in (np.int32, np.int64):
          data = np.zeros((2, 0), dtype=dtype)
          segment_ids = np.array([0, 1], dtype=itype)
          unsorted = tf.unsorted_segment_sum(data, segment_ids, 2)
          self.assertAllEqual(unsorted.eval(), np.zeros((2, 0), dtype=dtype)) 

def testComplex128(self):
    t = tensor_util.make_tensor_proto((1+2j), dtype=tf.complex128)
    self.assertProtoEquals("""
      dtype: DT_COMPLEX128
      tensor_shape {}
      dcomplex_val: 1
      dcomplex_val: 2
      """, t)
    a = tensor_util.MakeNdarray(t)
    self.assertEquals(np.complex128, a.dtype)
    self.assertAllEqual(np.array(1 + 2j), a) 

def testZerosLikeCPU(self):
    for dtype in [tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8,
                  tf.complex64, tf.complex128, tf.int64]:
      self._compareZeros(dtype, False) 

def testStringConversion(self):
    self.assertIs(tf.float32, tf.as_dtype("float32"))
    self.assertIs(tf.float64, tf.as_dtype("float64"))
    self.assertIs(tf.int32, tf.as_dtype("int32"))
    self.assertIs(tf.uint8, tf.as_dtype("uint8"))
    self.assertIs(tf.uint16, tf.as_dtype("uint16"))
    self.assertIs(tf.int16, tf.as_dtype("int16"))
    self.assertIs(tf.int8, tf.as_dtype("int8"))
    self.assertIs(tf.string, tf.as_dtype("string"))
    self.assertIs(tf.complex64, tf.as_dtype("complex64"))
    self.assertIs(tf.complex128, tf.as_dtype("complex128"))
    self.assertIs(tf.int64, tf.as_dtype("int64"))
    self.assertIs(tf.bool, tf.as_dtype("bool"))
    self.assertIs(tf.qint8, tf.as_dtype("qint8"))
    self.assertIs(tf.quint8, tf.as_dtype("quint8"))
    self.assertIs(tf.qint32, tf.as_dtype("qint32"))
    self.assertIs(tf.bfloat16, tf.as_dtype("bfloat16"))
    self.assertIs(tf.float32_ref, tf.as_dtype("float32_ref"))
    self.assertIs(tf.float64_ref, tf.as_dtype("float64_ref"))
    self.assertIs(tf.int32_ref, tf.as_dtype("int32_ref"))
    self.assertIs(tf.uint8_ref, tf.as_dtype("uint8_ref"))
    self.assertIs(tf.int16_ref, tf.as_dtype("int16_ref"))
    self.assertIs(tf.int8_ref, tf.as_dtype("int8_ref"))
    self.assertIs(tf.string_ref, tf.as_dtype("string_ref"))
    self.assertIs(tf.complex64_ref, tf.as_dtype("complex64_ref"))
    self.assertIs(tf.complex128_ref, tf.as_dtype("complex128_ref"))
    self.assertIs(tf.int64_ref, tf.as_dtype("int64_ref"))
    self.assertIs(tf.bool_ref, tf.as_dtype("bool_ref"))
    self.assertIs(tf.qint8_ref, tf.as_dtype("qint8_ref"))
    self.assertIs(tf.quint8_ref, tf.as_dtype("quint8_ref"))
    self.assertIs(tf.qint32_ref, tf.as_dtype("qint32_ref"))
    self.assertIs(tf.bfloat16_ref, tf.as_dtype("bfloat16_ref"))
    with self.assertRaises(TypeError):
      tf.as_dtype("not_a_type") 

def testComplexWithImplicitRepeat(self):
    for dtype, np_dtype in [(tf.complex64, np.complex64),
                            (tf.complex128, np.complex128)]:
      t = tensor_util.make_tensor_proto((1+1j), shape=[3, 4],
                                        dtype=dtype)
      a = tensor_util.MakeNdarray(t)
      self.assertAllClose(np.array([[(1+1j), (1+1j), (1+1j), (1+1j)],
                                    [(1+1j), (1+1j), (1+1j), (1+1j)],
                                    [(1+1j), (1+1j), (1+1j), (1+1j)]],
                                   dtype=np_dtype), a) 

def testRealDtype(self):
    for dtype in [tf.float32, tf.float64, tf.bool, tf.uint8, tf.int8, tf.int16,
                  tf.int32, tf.int64]:
      self.assertIs(dtype.real_dtype, dtype)
    self.assertIs(tf.complex64.real_dtype, tf.float32)
    self.assertIs(tf.complex128.real_dtype, tf.float64) 

def testGradients(self):
    t = [tf.float32, tf.float64, tf.complex64, tf.complex128]
    for src_t in t:
      for dst_t in t:
        with self.test_session():
          x = tf.constant(1.0, src_t)
          z = tf.identity(x)
          y = tf.cast(z, dst_t)
          err = tf.test.compute_gradient_error(x, [], y, [])
          self.assertLess(err, 1e-3) 

def testIsInteger(self):
    self.assertEqual(tf.as_dtype("int8").is_integer, True)
    self.assertEqual(tf.as_dtype("int16").is_integer, True)
    self.assertEqual(tf.as_dtype("int32").is_integer, True)
    self.assertEqual(tf.as_dtype("int64").is_integer, True)
    self.assertEqual(tf.as_dtype("uint8").is_integer, True)
    self.assertEqual(tf.as_dtype("uint16").is_integer, True)
    self.assertEqual(tf.as_dtype("complex64").is_integer, False)
    self.assertEqual(tf.as_dtype("complex128").is_integer, False)
    self.assertEqual(tf.as_dtype("float").is_integer, False)
    self.assertEqual(tf.as_dtype("double").is_integer, False)
    self.assertEqual(tf.as_dtype("string").is_integer, False)
    self.assertEqual(tf.as_dtype("bool").is_integer, False)
    self.assertEqual(tf.as_dtype("bfloat16").is_integer, False) 

def testIsFloating(self):
    self.assertEqual(tf.as_dtype("int8").is_floating, False)
    self.assertEqual(tf.as_dtype("int16").is_floating, False)
    self.assertEqual(tf.as_dtype("int32").is_floating, False)
    self.assertEqual(tf.as_dtype("int64").is_floating, False)
    self.assertEqual(tf.as_dtype("uint8").is_floating, False)
    self.assertEqual(tf.as_dtype("uint16").is_floating, False)
    self.assertEqual(tf.as_dtype("complex64").is_floating, False)
    self.assertEqual(tf.as_dtype("complex128").is_floating, False)
    self.assertEqual(tf.as_dtype("float32").is_floating, True)
    self.assertEqual(tf.as_dtype("float64").is_floating, True)
    self.assertEqual(tf.as_dtype("string").is_floating, False)
    self.assertEqual(tf.as_dtype("bool").is_floating, False)
    self.assertEqual(tf.as_dtype("bfloat16").is_integer, False) 

def testIsComplex(self):
    self.assertEqual(tf.as_dtype("int8").is_complex, False)
    self.assertEqual(tf.as_dtype("int16").is_complex, False)
    self.assertEqual(tf.as_dtype("int32").is_complex, False)
    self.assertEqual(tf.as_dtype("int64").is_complex, False)
    self.assertEqual(tf.as_dtype("uint8").is_complex, False)
    self.assertEqual(tf.as_dtype("uint16").is_complex, False)
    self.assertEqual(tf.as_dtype("complex64").is_complex, True)
    self.assertEqual(tf.as_dtype("complex128").is_complex, True)
    self.assertEqual(tf.as_dtype("float32").is_complex, False)
    self.assertEqual(tf.as_dtype("float64").is_complex, False)
    self.assertEqual(tf.as_dtype("string").is_complex, False)
    self.assertEqual(tf.as_dtype("bool").is_complex, False)
    self.assertEqual(tf.as_dtype("bfloat16").is_integer, False) 

def test_odeint_all_dtypes(self):
    func = lambda y, t: y
    t = np.linspace(0.0, 1.0, 11)
    for y0_dtype in [tf.float32, tf.float64, tf.complex64, tf.complex128]:
      for t_dtype in [tf.float32, tf.float64]:
        y0 = tf.cast(1.0, y0_dtype)
        y_solved = tf.contrib.integrate.odeint(func, y0, tf.cast(t, t_dtype))
        with self.test_session() as sess:
          y_solved = sess.run(y_solved)
        expected = np.asarray(np.exp(t))
        self.assertAllClose(y_solved, expected, rtol=1e-5)
        self.assertEqual(tf.as_dtype(y_solved.dtype), y0_dtype) 

def complex_to_channels(image,
                        data_format='channels_last',
                        name='complex2channels'):
    """Convert data from complex to channels."""
    if len(image.shape) != 3 and len(image.shape) != 4:
        raise TypeError('Input data must be have 3 or 4 dimensions')

    axis_c = -1 if data_format == 'channels_last' else -3

    if image.dtype is not tf.complex64 and image.dtype is not tf.complex128:
        raise TypeError('Input data must be complex')

    with tf.name_scope(name):
        image_out = tf.concat((tf.real(image), tf.imag(image)), axis_c)
    return image_out 

def config_iterator():
    return product(
        range(1, 11),
        [5, 10],  # K
        [2, 4, 6],  # num_mics # range(2, 11)
        [512],  # frame_size # 1024
        [tf.complex64],  # dtype # , tf.complex128
        ['/cpu:0']  # device # '/gpu:0'
    )