Python keras.initializers._compute_fans() Examples
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code examples of keras.initializers._compute_fans().
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Example #1
Source File: init.py From deep_complex_networks with MIT License | 7 votes |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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)