Python tensorflow 模块，erfc() 实例源码

def ae(x):
    if nonlinearity_name == 'relu':
        f = tf.nn.relu
    elif nonlinearity_name == 'elu':
        f = tf.nn.elu
    elif nonlinearity_name == 'gelu':
        # def gelu(x):
        #     return tf.mul(x, tf.erfc(-x / tf.sqrt(2.)) / 2.)
        # f = gelu
        def gelu_fast(_x):
            return 0.5 * _x * (1 + tf.tanh(tf.sqrt(2 / np.pi) * (_x + 0.044715 * tf.pow(_x, 3))))
        f = gelu_fast
    elif nonlinearity_name == 'silu':
        def silu(_x):
            return _x * tf.sigmoid(_x)
        f = silu
    # elif nonlinearity_name == 'soi':
    #     def soi_map(x):
    #         u = tf.random_uniform(tf.shape(x))
    #         mask = tf.to_float(tf.less(u, (1 + tf.erf(x / tf.sqrt(2.))) / 2.))
    #         return tf.cond(is_training, lambda: tf.mul(mask, x),
    #                        lambda: tf.mul(x, tf.erfc(-x / tf.sqrt(2.)) / 2.))
    #     f = soi_map

    else:
        raise NameError("Need 'relu', 'elu', 'gelu', or 'silu' for nonlinearity_name")

    h1 = f(tf.matmul(x, W['1']) + b['1'])
    h2 = f(tf.matmul(h1, W['2']) + b['2'])
    h3 = f(tf.matmul(h2, W['3']) + b['3'])
    h4 = f(tf.matmul(h3, W['4']) + b['4'])
    h5 = f(tf.matmul(h4, W['5']) + b['5'])
    h6 = f(tf.matmul(h5, W['6']) + b['6'])
    h7 = f(tf.matmul(h6, W['7']) + b['7'])
    return tf.matmul(h7, W['8']) + b['8']

def setUp(self):
    super(CoreUnaryOpsTest, self).setUp()

    self.ops = [
        ('abs', operator.abs, tf.abs, core.abs_function),
        ('neg', operator.neg, tf.neg, core.neg),
        # TODO(shoyer): add unary + to core TensorFlow
        ('pos', None, None, None),
        ('sign', None, tf.sign, core.sign),
        ('reciprocal', None, tf.reciprocal, core.reciprocal),
        ('square', None, tf.square, core.square),
        ('round', None, tf.round, core.round_function),
        ('sqrt', None, tf.sqrt, core.sqrt),
        ('rsqrt', None, tf.rsqrt, core.rsqrt),
        ('log', None, tf.log, core.log),
        ('exp', None, tf.exp, core.exp),
        ('log', None, tf.log, core.log),
        ('ceil', None, tf.ceil, core.ceil),
        ('floor', None, tf.floor, core.floor),
        ('cos', None, tf.cos, core.cos),
        ('sin', None, tf.sin, core.sin),
        ('tan', None, tf.tan, core.tan),
        ('acos', None, tf.acos, core.acos),
        ('asin', None, tf.asin, core.asin),
        ('atan', None, tf.atan, core.atan),
        ('lgamma', None, tf.lgamma, core.lgamma),
        ('digamma', None, tf.digamma, core.digamma),
        ('erf', None, tf.erf, core.erf),
        ('erfc', None, tf.erfc, core.erfc),
        ('lgamma', None, tf.lgamma, core.lgamma),
    ]
    total_size = np.prod([v.size for v in self.original_lt.axes.values()])
    self.test_lt = core.LabeledTensor(
        tf.cast(self.original_lt, tf.float32) / total_size,
        self.original_lt.axes)

def _normal_cdf(lim, mu, sigma):
    return 0.5 * tf.erfc((mu - lim) / (tf.constant(np.sqrt(2), config.dtype) * sigma))

def test_Erfc(self):
        t = tf.erfc(self.random(4, 3))
        self.check(t)

def feedforward(x):
    if nonlinearity_name == 'relu':
        f = tf.nn.relu
    elif nonlinearity_name == 'elu':
        f = tf.nn.elu
    elif nonlinearity_name == 'gelu':
        # def gelu(x):
        #     return tf.mul(x, tf.erfc(-x / tf.sqrt(2.)) / 2.)
        # f = gelu
        def gelu_fast(_x):
            return 0.5 * _x * (1 + tf.tanh(tf.sqrt(2 / np.pi) * (_x + 0.044715 * tf.pow(_x, 3))))
        f = gelu_fast
    elif nonlinearity_name == 'silu':
        def silu(_x):
            return _x * tf.sigmoid(_x)
        f = silu
    # elif nonlinearity_name == 'soi':
    #     def soi_map(x):
    #         u = tf.random_uniform(tf.shape(x))
    #         mask = tf.to_float(tf.less(u, (1 + tf.erf(x / tf.sqrt(2.))) / 2.))
    #         return tf.cond(is_training, lambda: tf.mul(mask, x),
    #                        lambda: tf.mul(x, tf.erfc(-x / tf.sqrt(2.)) / 2.))
    #     f = soi_map

    else:
        raise NameError("Need 'relu', 'elu', 'gelu', or 'silu' for nonlinearity_name")

    h1 = f(tf.matmul(x, W['1']) + b['1'])
    h1 = tf.cond(is_training, lambda: tf.nn.dropout(h1, p), lambda: h1)
    h2 = f(tf.matmul(h1, W['2']) + b['2'])
    h2 = tf.cond(is_training, lambda: tf.nn.dropout(h2, p), lambda: h2)
    h3 = f(tf.matmul(h2, W['3']) + b['3'])
    h3 = tf.cond(is_training, lambda: tf.nn.dropout(h3, p), lambda: h3)
    h4 = f(tf.matmul(h3, W['4']) + b['4'])
    h4 = tf.cond(is_training, lambda: tf.nn.dropout(h4, p), lambda: h4)
    h5 = f(tf.matmul(h4, W['5']) + b['5'])
    h5 = tf.cond(is_training, lambda: tf.nn.dropout(h5, p), lambda: h5)
    h6 = f(tf.matmul(h5, W['6']) + b['6'])
    h6 = tf.cond(is_training, lambda: tf.nn.dropout(h6, p), lambda: h6)
    h7 = f(tf.matmul(h6, W['7']) + b['7'])
    h7 = tf.cond(is_training, lambda: tf.nn.dropout(h7, p), lambda: h7)
    h8 = f(tf.matmul(h7, W['8']) + b['8'])
    h8 = tf.cond(is_training, lambda: tf.nn.dropout(h8, p), lambda: h8)
    return tf.matmul(h8, W['9']) + b['9']