Python keras.initializers._compute_fans() Examples

The following are 30 code examples of keras.initializers._compute_fans(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module keras.initializers , or try the search function .
Example #1
Source File: init.py    From deep_complex_networks with MIT License 7 votes vote down vote up 
def __call__(self, shape, dtype=None):

        if self.nb_filters is not None:
            kernel_shape = tuple(self.kernel_size) + (int(self.input_dim), self.nb_filters)
        else:
            kernel_shape = (int(self.input_dim), self.kernel_size[-1])

        fan_in, fan_out = initializers._compute_fans(
            tuple(self.kernel_size) + (self.input_dim, self.nb_filters)
        )

        if self.criterion == 'glorot':
            s = 1. / (fan_in + fan_out)
        elif self.criterion == 'he':
            s = 1. / fan_in
        else:
            raise ValueError('Invalid criterion: ' + self.criterion)
        rng = RandomState(self.seed)
        modulus = rng.rayleigh(scale=s, size=kernel_shape)
        phase = rng.uniform(low=-np.pi, high=np.pi, size=kernel_shape)
        weight_real = modulus * np.cos(phase)
        weight_imag = modulus * np.sin(phase)
        weight = np.concatenate([weight_real, weight_imag], axis=-1)

        return weight
Example #2
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_glorot_uniform(tensor_shape):
    fan_in, fan_out = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(6. / (fan_in + fan_out))
    _runner(initializers.glorot_uniform(), tensor_shape,
            target_mean=0., target_max=scale, target_min=-scale)
Example #3
Source File: wide_residual_network.py    From AnomalyDetectionTransformations with MIT License 5 votes vote down vote up 
def _dense_kernel_initializer(shape, dtype=None):
    fan_in, fan_out = _compute_fans(shape)
    stddev = 1. / np.sqrt(fan_in)
    return K.random_uniform(shape, -stddev, stddev, dtype)
Example #4
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_he_uniform(tensor_shape):
    fan_in, _ = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(6. / fan_in)
    _runner(initializers.he_uniform(), tensor_shape,
            target_mean=0., target_max=scale, target_min=-scale)
Example #5
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_glorot_normal(tensor_shape):
    fan_in, fan_out = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(2. / (fan_in + fan_out))
    _runner(initializers.glorot_normal(), tensor_shape,
            target_mean=0., target_std=None, target_max=2 * scale)
Example #6
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_he_normal(tensor_shape):
    fan_in, _ = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(2. / fan_in)
    _runner(initializers.he_normal(), tensor_shape,
            target_mean=0., target_std=None, target_max=2 * scale)
Example #7
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_lecun_uniform(tensor_shape):
    fan_in, _ = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(3. / fan_in)
    _runner(initializers.lecun_uniform(), tensor_shape,
            target_mean=0., target_max=scale, target_min=-scale)
Example #8
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_glorot_uniform(tensor_shape):
    fan_in, fan_out = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(6. / (fan_in + fan_out))
    _runner(initializers.glorot_uniform(), tensor_shape,
            target_mean=0., target_max=scale, target_min=-scale)
Example #9
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_he_uniform(tensor_shape):
    fan_in, _ = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(6. / fan_in)
    _runner(initializers.he_uniform(), tensor_shape,
            target_mean=0., target_max=scale, target_min=-scale)
Example #10
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_glorot_normal(tensor_shape):
    fan_in, fan_out = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(2. / (fan_in + fan_out))
    _runner(initializers.glorot_normal(), tensor_shape,
            target_mean=0., target_std=None, target_max=2 * scale)
Example #11
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_he_normal(tensor_shape):
    fan_in, _ = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(2. / fan_in)
    _runner(initializers.he_normal(), tensor_shape,
            target_mean=0., target_std=None, target_max=2 * scale)
Example #12
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_lecun_uniform(tensor_shape):
    fan_in, _ = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(3. / fan_in)
    _runner(initializers.lecun_uniform(), tensor_shape,
            target_mean=0., target_max=scale, target_min=-scale)
Example #13
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_glorot_uniform(tensor_shape):
    fan_in, fan_out = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(6. / (fan_in + fan_out))
    _runner(initializers.glorot_uniform(), tensor_shape,
            target_mean=0., target_max=scale, target_min=-scale)
Example #14
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_he_uniform(tensor_shape):
    fan_in, _ = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(6. / fan_in)
    _runner(initializers.he_uniform(), tensor_shape,
            target_mean=0., target_max=scale, target_min=-scale)
Example #15
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_glorot_normal(tensor_shape):
    fan_in, fan_out = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(2. / (fan_in + fan_out))
    _runner(initializers.glorot_normal(), tensor_shape,
            target_mean=0., target_std=None, target_max=2 * scale)
Example #16
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_he_normal(tensor_shape):
    fan_in, _ = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(2. / fan_in)
    _runner(initializers.he_normal(), tensor_shape,
            target_mean=0., target_std=None, target_max=2 * scale)
Example #17
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_lecun_uniform(tensor_shape):
    fan_in, _ = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(3. / fan_in)
    _runner(initializers.lecun_uniform(), tensor_shape,
            target_mean=0., target_max=scale, target_min=-scale)
Example #18
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_glorot_uniform(tensor_shape):
    fan_in, fan_out = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(6. / (fan_in + fan_out))
    _runner(initializers.glorot_uniform(), tensor_shape,
            target_mean=0., target_max=scale, target_min=-scale)
Example #19
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_he_uniform(tensor_shape):
    fan_in, _ = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(6. / fan_in)
    _runner(initializers.he_uniform(), tensor_shape,
            target_mean=0., target_max=scale, target_min=-scale)
Example #20
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_glorot_normal(tensor_shape):
    fan_in, fan_out = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(2. / (fan_in + fan_out))
    _runner(initializers.glorot_normal(), tensor_shape,
            target_mean=0., target_std=None, target_max=2 * scale)
Example #21
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_he_normal(tensor_shape):
    fan_in, _ = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(2. / fan_in)
    _runner(initializers.he_normal(), tensor_shape,
            target_mean=0., target_std=None, target_max=2 * scale)
Example #22
Source File: initializers_test.py    From faceswap with GNU General Public License v3.0 5 votes vote down vote up 
def test_icnr(tensor_shape):
    """ ICNR Initialization Test

    Parameters
    ----------
    tensor_shape: tuple
        The shape of the tensor to feed to the initializer
    """
    fan_in, _ = k_initializers._compute_fans(tensor_shape)  # pylint:disable=protected-access
    std = np.sqrt(2. / fan_in)
    _runner(initializers.ICNR(initializer=k_initializers.he_uniform(), scale=2), tensor_shape,
            target_mean=0, target_std=std)
Example #23
Source File: initializers_test.py    From faceswap with GNU General Public License v3.0 5 votes vote down vote up 
def test_convolution_aware(tensor_shape):
    """ Convolution Aware Initialization Test

    Parameters
    ----------
    tensor_shape: tuple
        The shape of the tensor to feed to the initializer
    """
    fan_in, _ = k_initializers._compute_fans(tensor_shape)  # pylint:disable=protected-access
    std = np.sqrt(2. / fan_in)
    _runner(initializers.ConvolutionAware(seed=123, init=True), tensor_shape,
            target_mean=0, target_std=std)
Example #24
Source File: wide_residual_network.py    From AnomalyDetectionTransformations with MIT License 5 votes vote down vote up 
def _conv_kernel_initializer(shape, dtype=None):
    fan_in, fan_out = _compute_fans(shape)
    stddev = np.sqrt(2. / fan_in)
    return K.random_normal(shape, 0., stddev, dtype)
Example #25
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_he_normal(tensor_shape):
    fan_in, _ = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(2. / fan_in)
    _runner(initializers.he_normal(), tensor_shape,
            target_mean=0., target_std=None, target_max=2 * scale)
Example #26
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_glorot_normal(tensor_shape):
    fan_in, fan_out = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(2. / (fan_in + fan_out))
    _runner(initializers.glorot_normal(), tensor_shape,
            target_mean=0., target_std=None, target_max=2 * scale)
Example #27
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_he_uniform(tensor_shape):
    fan_in, _ = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(6. / fan_in)
    _runner(initializers.he_uniform(), tensor_shape,
            target_mean=0., target_max=scale, target_min=-scale)
Example #28
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_glorot_uniform(tensor_shape):
    fan_in, fan_out = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(6. / (fan_in + fan_out))
    _runner(initializers.glorot_uniform(), tensor_shape,
            target_mean=0., target_max=scale, target_min=-scale)
Example #29
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_lecun_uniform(tensor_shape):
    fan_in, _ = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(3. / fan_in)
    _runner(initializers.lecun_uniform(), tensor_shape,
            target_mean=0., target_max=scale, target_min=-scale)
Example #30
Source File: initializers_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_he_normal(tensor_shape):
    fan_in, _ = initializers._compute_fans(tensor_shape)
    scale = np.sqrt(2. / fan_in)
    _runner(initializers.he_normal(), tensor_shape,
            target_mean=0., target_std=None, target_max=2 * scale)