Python keras.utils.conv_utils.normalize_padding() Examples

The following are 11 code examples of keras.utils.conv_utils.normalize_padding(). 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.utils.conv_utils , or try the search function .
Example #1
Source File: sn.py    From Coloring-greyscale-images with MIT License 5 votes vote down vote up 
def __init__(self, rank,
                 filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 data_format=None,
                 dilation_rate=1,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 spectral_normalization=True,
                 **kwargs):
        super(_ConvSN, self).__init__(**kwargs)
        self.rank = rank
        self.filters = filters
        self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')
        self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
        self.padding = conv_utils.normalize_padding(padding)
        self.data_format = conv_utils.normalize_data_format(data_format)
        self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, rank, 'dilation_rate')
        self.activation = activations.get(activation)
        self.use_bias = use_bias
        self.kernel_initializer = initializers.get(kernel_initializer)
        self.bias_initializer = initializers.get(bias_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.bias_constraint = constraints.get(bias_constraint)
        self.input_spec = InputSpec(ndim=self.rank + 2)
        self.spectral_normalization = spectral_normalization
        self.u = None
Example #2
Source File: conv_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_invalid_padding():
    with pytest.raises(ValueError):
        conv_utils.normalize_padding('diagonal')
Example #3
Source File: conv_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_invalid_padding():
    with pytest.raises(ValueError):
        conv_utils.normalize_padding('diagonal')
Example #4
Source File: conv_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_invalid_padding():
    with pytest.raises(ValueError):
        conv_utils.normalize_padding('diagonal')
Example #5
Source File: conv_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_invalid_padding():
    with pytest.raises(ValueError):
        conv_utils.normalize_padding('diagonal')
Example #6
Source File: conv_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_invalid_padding():
    with pytest.raises(ValueError):
        conv_utils.normalize_padding('diagonal')
Example #7
Source File: conv_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_invalid_padding():
    with pytest.raises(ValueError):
        conv_utils.normalize_padding('diagonal')
Example #8
Source File: conv_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_invalid_padding():
    with pytest.raises(ValueError):
        conv_utils.normalize_padding('diagonal')
Example #9
Source File: capslayers.py    From deepcaps with MIT License 5 votes vote down vote up 
def __init__(self, ch_j, n_j,
                 kernel_size=(3, 3),
                 strides=(1, 1),
                 r_num=1,
                 b_alphas=[8, 8, 8],
                 padding='same',
                 data_format='channels_last',
                 dilation_rate=(1, 1),
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 **kwargs):
        super(Conv2DCaps, self).__init__(**kwargs)
        rank = 2
        self.ch_j = ch_j  # Number of capsules in layer J
        self.n_j = n_j  # Number of neurons in a capsule in J
        self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')
        self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
        self.r_num = r_num
        self.b_alphas = b_alphas
        self.padding = conv_utils.normalize_padding(padding)
        #self.data_format = conv_utils.normalize_data_format(data_format)
        self.data_format = K.normalize_data_format(data_format)
        self.dilation_rate = (1, 1)
        self.kernel_initializer = initializers.get(kernel_initializer)
        self.bias_initializer = initializers.get(bias_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.input_spec = InputSpec(ndim=rank + 3)
Example #10
Source File: conv.py    From deep_complex_networks with MIT License 4 votes vote down vote up 
def __init__(self, rank,
                 filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 data_format=None,
                 dilation_rate=1,
                 activation=None,
                 use_bias=True,
                 normalize_weight=False,
                 kernel_initializer='complex',
                 bias_initializer='zeros',
                 gamma_diag_initializer=sqrt_init,
                 gamma_off_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 gamma_diag_regularizer=None,
                 gamma_off_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 gamma_diag_constraint=None,
                 gamma_off_constraint=None,
                 init_criterion='he',
                 seed=None,
                 spectral_parametrization=False,
                 epsilon=1e-7,
                 **kwargs):
        super(ComplexConv, self).__init__(**kwargs)
        self.rank = rank
        self.filters = filters
        self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')
        self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
        self.padding = conv_utils.normalize_padding(padding)
        self.data_format = 'channels_last' if rank == 1 else conv_utils.normalize_data_format(data_format)
        self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, rank, 'dilation_rate')
        self.activation = activations.get(activation)
        self.use_bias = use_bias
        self.normalize_weight = normalize_weight
        self.init_criterion = init_criterion
        self.spectral_parametrization = spectral_parametrization
        self.epsilon = epsilon
        self.kernel_initializer = sanitizedInitGet(kernel_initializer)
        self.bias_initializer = sanitizedInitGet(bias_initializer)
        self.gamma_diag_initializer = sanitizedInitGet(gamma_diag_initializer)
        self.gamma_off_initializer = sanitizedInitGet(gamma_off_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.gamma_diag_regularizer = regularizers.get(gamma_diag_regularizer)
        self.gamma_off_regularizer = regularizers.get(gamma_off_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.bias_constraint = constraints.get(bias_constraint)
        self.gamma_diag_constraint = constraints.get(gamma_diag_constraint)
        self.gamma_off_constraint = constraints.get(gamma_off_constraint)
        if seed is None:
            self.seed = np.random.randint(1, 10e6)
        else:
            self.seed = seed
        self.input_spec = InputSpec(ndim=self.rank + 2)
Example #11
Source File: conv.py    From Quaternion-Convolutional-Neural-Networks-for-End-to-End-Automatic-Speech-Recognition with GNU General Public License v3.0 4 votes vote down vote up 
def __init__(self, rank,
                 filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 data_format='channels_last',
                 dilation_rate=1,
                 activation=None,
                 use_bias=True,
                 normalize_weight=False,
                 kernel_initializer='quaternion',
                 bias_initializer='zeros',
                 gamma_diag_initializer=sqrt_init,
                 gamma_off_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 gamma_diag_regularizer=None,
                 gamma_off_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 gamma_diag_constraint=None,
                 gamma_off_constraint=None,
                 init_criterion='he',
                 seed=None,
                 spectral_parametrization=False,
                 epsilon=1e-7,
                 **kwargs):
        super(QuaternionConv, self).__init__(**kwargs)
        self.rank = rank
        self.filters = filters
        self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')
        self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
        self.padding = conv_utils.normalize_padding(padding)
        self.data_format = K.normalize_data_format(data_format)
        self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, rank, 'dilation_rate')
        self.activation = activations.get(activation)
        self.use_bias = use_bias
        self.normalize_weight = normalize_weight
        self.init_criterion = init_criterion
        self.spectral_parametrization = spectral_parametrization
        self.epsilon = epsilon
        self.kernel_initializer = sanitizedInitGet(kernel_initializer)
        self.bias_initializer = sanitizedInitGet(bias_initializer)
        self.gamma_diag_initializer = sanitizedInitGet(gamma_diag_initializer)
        self.gamma_off_initializer = sanitizedInitGet(gamma_off_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.gamma_diag_regularizer = regularizers.get(gamma_diag_regularizer)
        self.gamma_off_regularizer = regularizers.get(gamma_off_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.bias_constraint = constraints.get(bias_constraint)
        self.gamma_diag_constraint = constraints.get(gamma_diag_constraint)
        self.gamma_off_constraint = constraints.get(gamma_off_constraint)
        if seed is None:
            self.seed = np.random.randint(1, 10e6)
        else:
            self.seed = seed
        self.input_spec = InputSpec(ndim=self.rank + 2)