Python tensorflow.inv方法代码示例

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import inv [as 别名]
def ycbcr2rgb(inputs):
    with tf.name_scope('ycbcr2rgb'):
        if inputs.get_shape()[-1].value == 1:
            return inputs
        assert inputs.get_shape()[-1].value == 3, 'Error: rgb2ycbcr input should be RGB or grayscale!'
        ndims = len(inputs.get_shape())
        # origT = np.array([[65.481, 128.553, 24.966], [-37.797 -74.203 112], [112 -93.786 -18.214]])
        # T = tf.inv(origT)
        Tinv = [[0.00456621, 0., 0.00625893], [0.00456621, -0.00153632, -0.00318811], [0.00456621, 0.00791071, 0.]]
        origOffset = [16.0, 128.0, 128.0]
        if ndims == 4:
            origT = [tf.reshape(Tinv[i], [1, 1, 1, 3]) * 255.0 for i in xrange(3)]
            origOffset = tf.reshape(origOffset, [1, 1, 1, 3]) / 255.0
        elif ndims == 5:
            origT = [tf.reshape(Tinv[i], [1, 1, 1, 1, 3]) * 255.0 for i in xrange(3)]
            origOffset = tf.reshape(origOffset, [1, 1, 1, 1, 3]) / 255.0
        output = []
        for i in xrange(3):
            output.append(tf.reduce_sum((inputs - origOffset) * origT[i], reduction_indices=-1, keep_dims=True))
        return tf.concat(output, -1) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import inv [as 别名]
def ycbcr2rgb(inputs):
    with tf.name_scope('ycbcr2rgb'):
        if inputs.get_shape()[-1].value == 1:
            return inputs
        assert inputs.get_shape()[-1].value == 3, 'Error: rgb2ycbcr input should be RGB or grayscale!'
        ndims = len(inputs.get_shape())
        # origT = np.array([[65.481, 128.553, 24.966], [-37.797 -74.203 112], [112 -93.786 -18.214]])
        # T = tf.inv(origT)
        Tinv = [[0.00456621, 0., 0.00625893], [0.00456621, -0.00153632, -0.00318811], [0.00456621, 0.00791071, 0.]]
        origOffset = [16.0, 128.0, 128.0]
        if ndims == 4:
            origT = [tf.reshape(Tinv[i], [1, 1, 1, 3]) * 255.0 for i in range(3)]
            origOffset = tf.reshape(origOffset, [1, 1, 1, 3]) / 255.0
        elif ndims == 5:
            origT = [tf.reshape(Tinv[i], [1, 1, 1, 1, 3]) * 255.0 for i in range(3)]
            origOffset = tf.reshape(origOffset, [1, 1, 1, 1, 3]) / 255.0
        output = []
        for i in range(3):
            output.append(tf.reduce_sum((inputs - origOffset) * origT[i], reduction_indices=-1, keep_dims=True))
        return tf.concat(output, -1) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import inv [as 别名]
def test_Inv(self):
        if td._tf_version[:2] <= (0, 11):
            t = tf.inv(self.random(4, 3))
            self.check(t) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import inv [as 别名]
def testFloatBasic(self):
    x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float32)
    y = (x + .5).astype(np.float32)     # no zero
    z = (x + 15.5).astype(np.float32)   # all positive
    k = np.arange(-0.90, 0.90, 0.25).astype(np.float32) # between -1 and 1

    self._compareBoth(x, np.abs, tf.abs)
    self._compareBoth(x, np.abs, _ABS)
    self._compareBoth(x, np.negative, tf.neg)
    self._compareBoth(x, np.negative, _NEG)
    self._compareBoth(y, self._inv, tf.inv)
    self._compareBoth(x, np.square, tf.square)
    self._compareBoth(z, np.sqrt, tf.sqrt)
    self._compareBoth(z, self._rsqrt, tf.rsqrt)
    self._compareBoth(x, np.exp, tf.exp)
    self._compareBoth(z, np.log, tf.log)
    self._compareBoth(z, np.log1p, tf.log1p)
    self._compareBoth(x, np.tanh, tf.tanh)
    self._compareBoth(x, self._sigmoid, tf.sigmoid)
    self._compareBoth(y, np.sign, tf.sign)
    self._compareBoth(x, np.sin, tf.sin)
    self._compareBoth(x, np.cos, tf.cos)
    self._compareBoth(k, np.arcsin, tf.asin)
    self._compareBoth(k, np.arccos, tf.acos)
    self._compareBoth(x, np.arctan, tf.atan)
    self._compareBoth(x, np.tan, tf.tan)
    self._compareBoth(
        y,
        np.vectorize(self._replace_domain_error_with_inf(math.lgamma)),
        tf.lgamma)
    self._compareBoth(x, np.vectorize(math.erf), tf.erf)
    self._compareBoth(x, np.vectorize(math.erfc), tf.erfc)

    self._compareBothSparse(x, np.abs, tf.abs)
    self._compareBothSparse(x, np.negative, tf.neg)
    self._compareBothSparse(x, np.square, tf.square)
    self._compareBothSparse(z, np.sqrt, tf.sqrt, tol=1e-3)
    self._compareBothSparse(x, np.tanh, tf.tanh)
    self._compareBothSparse(y, np.sign, tf.sign)
    self._compareBothSparse(x, np.vectorize(math.erf), tf.erf) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import inv [as 别名]
def testFloatEmpty(self):
    x = np.empty((2, 0, 5), dtype=np.float32)
    self._compareBoth(x, np.abs, tf.abs)
    self._compareBoth(x, np.abs, _ABS)
    self._compareBoth(x, np.negative, tf.neg)
    self._compareBoth(x, np.negative, _NEG)
    self._compareBoth(x, self._inv, tf.inv)
    self._compareBoth(x, np.square, tf.square)
    self._compareBoth(x, np.sqrt, tf.sqrt)
    self._compareBoth(x, self._rsqrt, tf.rsqrt)
    self._compareBoth(x, np.exp, tf.exp)
    self._compareBoth(x, np.log, tf.log)
    self._compareBoth(x, np.log1p, tf.log1p)
    self._compareBoth(x, np.tanh, tf.tanh)
    self._compareBoth(x, self._sigmoid, tf.sigmoid)
    self._compareBoth(x, np.sign, tf.sign)
    self._compareBoth(x, np.sin, tf.sin)
    self._compareBoth(x, np.cos, tf.cos)
    # Can't use vectorize below, so just use some arbitrary function
    self._compareBoth(x, np.sign, tf.lgamma)
    self._compareBoth(x, np.sign, tf.erf)
    self._compareBoth(x, np.sign, tf.erfc)
    self._compareBoth(x, np.tan, tf.tan)
    self._compareBoth(x, np.arcsin, tf.asin)
    self._compareBoth(x, np.arccos, tf.acos)
    self._compareBoth(x, np.arctan, tf.atan)

    self._compareBothSparse(x, np.abs, tf.abs)
    self._compareBothSparse(x, np.negative, tf.neg)
    self._compareBothSparse(x, np.square, tf.square)
    self._compareBothSparse(x, np.sqrt, tf.sqrt, tol=1e-3)
    self._compareBothSparse(x, np.tanh, tf.tanh)
    self._compareBothSparse(x, np.sign, tf.sign)
    self._compareBothSparse(x, np.sign, tf.erf) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import inv [as 别名]
def testDoubleBasic(self):
    x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float64)
    y = (x + .5).astype(np.float64)    # no zero
    z = (x + 15.5).astype(np.float64)  # all positive
    k = np.arange(-0.90, 0.90, 0.35).reshape(1, 3, 2).astype(np.float64) # between -1 and 1
    self._compareBoth(x, np.abs, tf.abs)
    self._compareBoth(x, np.abs, _ABS)
    self._compareBoth(x, np.negative, tf.neg)
    self._compareBoth(x, np.negative, _NEG)
    self._compareBoth(y, self._inv, tf.inv)
    self._compareBoth(x, np.square, tf.square)
    self._compareBoth(z, np.sqrt, tf.sqrt)
    self._compareBoth(z, self._rsqrt, tf.rsqrt)
    self._compareBoth(x, np.exp, tf.exp)
    self._compareBoth(z, np.log, tf.log)
    self._compareBoth(z, np.log1p, tf.log1p)
    self._compareBoth(x, np.tanh, tf.tanh)
    self._compareBoth(x, self._sigmoid, tf.sigmoid)
    self._compareBoth(y, np.sign, tf.sign)
    self._compareBoth(x, np.sin, tf.sin)
    self._compareBoth(x, np.cos, tf.cos)
    self._compareBoth(
        y,
        np.vectorize(self._replace_domain_error_with_inf(math.lgamma)),
        tf.lgamma)
    self._compareBoth(x, np.vectorize(math.erf), tf.erf)
    self._compareBoth(x, np.vectorize(math.erfc), tf.erfc)
    self._compareBoth(x, np.arctan, tf.atan)
    self._compareBoth(k, np.arcsin, tf.asin)
    self._compareBoth(k, np.arccos, tf.acos)
    self._compareBoth(k, np.tan, tf.tan)

    self._compareBothSparse(x, np.abs, tf.abs)
    self._compareBothSparse(x, np.negative, tf.neg)
    self._compareBothSparse(x, np.square, tf.square)
    self._compareBothSparse(z, np.sqrt, tf.sqrt, tol=1e-3)
    self._compareBothSparse(x, np.tanh, tf.tanh)
    self._compareBothSparse(y, np.sign, tf.sign)
    self._compareBothSparse(x, np.vectorize(math.erf), tf.erf) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import inv [as 别名]
def testHalfBasic(self):
    x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float16)
    y = (x + .5).astype(np.float16)    # no zero
    z = (x + 15.5).astype(np.float16)  # all positive
    self._compareBoth(x, np.abs, tf.abs)
    self._compareBoth(x, np.abs, _ABS)
    self._compareBoth(x, np.negative, tf.neg)
    self._compareBoth(x, np.negative, _NEG)
    self._compareBoth(y, self._inv, tf.inv)
    self._compareBoth(x, np.square, tf.square)
    self._compareBoth(z, np.sqrt, tf.sqrt)
    self._compareBoth(z, self._rsqrt, tf.rsqrt)
    self._compareBoth(x, np.exp, tf.exp)
    self._compareBoth(z, np.log, tf.log)
    self._compareBoth(z, np.log1p, tf.log1p)
    self._compareBoth(x, np.tanh, tf.tanh)
    self._compareBoth(x, self._sigmoid, tf.sigmoid)
    self._compareBoth(y, np.sign, tf.sign)
    self._compareBoth(x, np.sin, tf.sin)
    self._compareBoth(x, np.cos, tf.cos)
    self._compareBoth(
        y,
        np.vectorize(self._replace_domain_error_with_inf(math.lgamma)),
        tf.lgamma)
    self._compareBoth(x, np.vectorize(math.erf), tf.erf)
    self._compareBoth(x, np.vectorize(math.erfc), tf.erfc)

    self._compareBothSparse(x, np.abs, tf.abs)
    self._compareBothSparse(x, np.negative, tf.neg)
    self._compareBothSparse(x, np.square, tf.square)
    self._compareBothSparse(z, np.sqrt, tf.sqrt, tol=1e-3)
    self._compareBothSparse(x, np.tanh, tf.tanh)
    self._compareBothSparse(y, np.sign, tf.sign)
    self._compareBothSparse(x, np.vectorize(math.erf), tf.erf, tol=1e-3) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import inv [as 别名]
def testComplex64Basic(self):
    x = np.complex(1, 1) * np.arange(-3, 3).reshape(1, 3, 2).astype(
        np.complex64)
    y = x + 0.5  # no zeros
    self._compareCpu(x, np.abs, tf.complex_abs)
    self._compareCpu(x, np.abs, _ABS)
    self._compareCpu(x, np.negative, tf.neg)
    self._compareCpu(x, np.negative, _NEG)
    self._compareCpu(y, self._inv, tf.inv)
    self._compareCpu(x, np.square, tf.square)
    self._compareCpu(y, np.sqrt, tf.sqrt)
    self._compareCpu(y, self._rsqrt, tf.rsqrt)
    self._compareCpu(x, np.exp, tf.exp)
    self._compareCpu(y, np.log, tf.log)
    self._compareCpu(y, np.log1p, tf.log1p)
    self._compareCpu(x, np.tanh, tf.tanh)
    self._compareCpu(x, self._sigmoid, tf.sigmoid)
    self._compareCpu(x, np.sin, tf.sin)
    self._compareCpu(x, np.cos, tf.cos)

    self._compareBothSparse(x, np.abs, tf.abs)
    self._compareBothSparse(x, np.negative, tf.neg)
    self._compareBothSparse(x, np.square, tf.square)
    self._compareBothSparse(x, np.sqrt, tf.sqrt, 1e-3)
    self._compareBothSparse(x, np.tanh, tf.tanh)

    # Numpy uses an incorrect definition of sign; use the right one instead.
    def complex_sign(x):
      return x / np.abs(x)
    self._compareCpu(y, complex_sign, tf.sign)
    self._compareBothSparse(y, complex_sign, tf.sign) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import inv [as 别名]
def drop_layer(x, keep_prob, seed=None, name=None):
  """Computes dropout.
  With probability `keep_prob`, outputs the input element scaled up by
  `1 / keep_prob`, otherwise outputs `0`.  The scaling is so that the expected
  sum is unchanged.

  Args:
    x: A tensor.
    keep_prob: A scalar `Tensor` with the same type as x. The probability
      that each element is kept.
    noise_shape: A 1-D `Tensor` of type `int32`, representing the
      shape for randomly generated keep/drop flags.
    seed: A Python integer. Used to create random seeds. See
      [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
      for behavior.
    name: A name for this operation (optional).
  Returns:
    A Tensor of the same shape of `x`.
  Raises:
    ValueError: If `keep_prob` is not in `(0, 1]`.
  """
  with tf.op_scope([x], name, "drop_layer") as name:
    x = tf.convert_to_tensor(x, name="x")
    if isinstance(keep_prob, float) and not 0 < keep_prob <= 1:
      raise ValueError("keep_prob must be a scalar tensor or a float in the "
                       "range (0, 1], got %g" % keep_prob)
    keep_prob = tf.convert_to_tensor(keep_prob,
                                      dtype=x.dtype,
                                      name="keep_prob")
    keep_prob.get_shape().assert_is_compatible_with(tensor_shape.scalar())

    noise_shape = [ tf.shape(x)[0], 1 ]
    # uniform [keep_prob, 1.0 + keep_prob)
    random_tensor = keep_prob
    random_tensor += tf.random_uniform(
        noise_shape,
        seed=seed,
        dtype=x.dtype
    )

    # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
    binary_tensor = tf.floor(random_tensor)
    ret = x * tf.inv(keep_prob) * binary_tensor
    ret.set_shape(x.get_shape())
    return ret