def __init__(self, axis=-1, momentum=0.99, epsilon=1e-3, center=True, scale=True, beta_initializer=ZerosInitializerConfig(), gamma_initializer=OnesInitializerConfig(), moving_mean_initializer=ZerosInitializerConfig(), moving_variance_initializer=OnesInitializerConfig(), beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None, **kwargs): super(BatchNormalizationConfig, self).__init__(**kwargs) self.axis = axis self.momentum = momentum self.epsilon = epsilon self.center = center self.scale = scale self.beta_initializer = beta_initializer self.gamma_initializer = gamma_initializer self.moving_mean_initializer = moving_mean_initializer self.moving_variance_initializer = moving_variance_initializer self.beta_regularizer = beta_regularizer self.gamma_regularizer = gamma_regularizer self.beta_constraint = beta_constraint self.gamma_constraint = gamma_constraint
def __init__(self, filters, kernel_size, strides=1, padding='valid', dilation_rate=1, activation=None, use_bias=True, kernel_initializer=GlorotNormalInitializerConfig(), bias_initializer=ZerosInitializerConfig(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs): super(Conv1DConfig, self).__init__(**kwargs) self.filters = filters self.kernel_size = kernel_size self.strides = strides self.padding = padding self.dilation_rate = dilation_rate self.activation = activation self.use_bias = use_bias self.kernel_initializer = kernel_initializer self.bias_initializer = bias_initializer self.kernel_regularizer = kernel_regularizer self.bias_regularizer = bias_regularizer self.activity_regularizer = activity_regularizer self.kernel_constraint = kernel_constraint self.bias_constraint = bias_constraint
def test_conv_lstm_2d_config(self): config_dict = { 'filters': 20, 'kernel_size': 3, 'strides': [1, 1], 'padding': 'valid', 'data_format': None, 'dilation_rate': [1, 1], 'activation': 'tanh', 'recurrent_activation': 'hard_sigmoid', 'use_bias': True, 'kernel_initializer': GlorotNormalInitializerConfig().to_schema(), 'recurrent_initializer': OrthogonalInitializerConfig().to_schema(), 'bias_initializer': ZerosInitializerConfig().to_schema(), 'unit_forget_bias': True, 'kernel_regularizer': None, 'recurrent_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'recurrent_constraint': None, 'bias_constraint': None, 'return_sequences': False, 'go_backwards': False, 'stateful': False, 'dropout': 0., 'recurrent_dropout': 0. } config = ConvLSTM2DConfig.from_dict(config_dict) assert_equal_layers(config, config_dict)
def assert_separable_conv(conv_class, dim): config_dict = { 'filters': 30, 'kernel_size': 10, 'strides': [1, 1] if dim == 2 else [1, 1, 1], 'padding': 'valid', 'data_format': None, 'depth_multiplier': 1, 'activation': None, 'use_bias': True, 'depthwise_initializer': GlorotNormalInitializerConfig().to_schema(), 'pointwise_initializer': GlorotNormalInitializerConfig().to_schema(), 'bias_initializer': ZerosInitializerConfig().to_schema(), 'depthwise_regularizer': None, 'pointwise_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'depthwise_constraint': None, 'pointwise_constraint': None, 'bias_constraint': None } config = conv_class.from_dict(config_dict) assert_equal_layers(config, config_dict)
def __init__(self, filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, activation=None, use_bias=True, kernel_initializer=GlorotUniformInitializerConfig(), bias_initializer=ZerosInitializerConfig(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs): super(LocallyConnected2DConfig, self).__init__(**kwargs) self.filters = filters self.kernel_size = kernel_size self.strides = strides self.padding = padding self.data_format = data_format self.activation = activation self.use_bias = use_bias self.kernel_initializer = kernel_initializer self.bias_initializer = bias_initializer self.kernel_regularizer = kernel_regularizer self.bias_regularizer = bias_regularizer self.activity_regularizer = activity_regularizer self.kernel_constraint = kernel_constraint self.bias_constraint = bias_constraint
def __init__(self, units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer=GlorotUniformInitializerConfig(), recurrent_initializer=OrthogonalInitializerConfig(), bias_initializer=ZerosInitializerConfig(), kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., **kwargs): super(GRUConfig, self).__init__(**kwargs) self.units = units self.activation = activation self.recurrent_activation = recurrent_activation self.use_bias = use_bias self.kernel_initializer = kernel_initializer self.recurrent_initializer = recurrent_initializer self.bias_initializer = bias_initializer self.kernel_regularizer = kernel_regularizer self.recurrent_regularizer = recurrent_regularizer self.bias_regularizer = bias_regularizer self.activity_regularizer = activity_regularizer self.kernel_constraint = kernel_constraint self.recurrent_constraint = recurrent_constraint self.bias_constraint = bias_constraint self.dropout = dropout self.recurrent_dropout = recurrent_dropout
def test_prelu_config(self): config_dict = { 'alpha_initializer': ZerosInitializerConfig().to_schema(), 'alpha_regularizer': None, 'alpha_constraint': None, 'shared_axes': None } config = PReLUConfig.from_dict(config_dict) assert_equal_layers(config, config_dict)
def __init__(self, alpha_initializer=ZerosInitializerConfig(), alpha_regularizer=None, alpha_constraint=None, shared_axes=None, **kwargs): super(PReLUConfig, self).__init__(**kwargs) self.alpha_initializer = alpha_initializer self.alpha_regularizer = alpha_regularizer self.alpha_constraint = alpha_constraint self.shared_axes = shared_axes
def test_dense_config(self): config_dict = { 'units': 12, 'activation': 'elu', 'use_bias': True, 'kernel_initializer': GlorotNormalInitializerConfig().to_schema(), 'bias_initializer': ZerosInitializerConfig().to_schema(), 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'inbound_nodes': [['layer_1', 0, 1], ['layer_2', 1, 1]] } config = DenseConfig.from_dict(config_dict) assert_equal_layers(config, config_dict)
def test_simple_rnn_config(self): config_dict = { 'units': 3, 'activation': 'tanh', 'use_bias': True, 'kernel_initializer': GlorotUniformInitializerConfig().to_schema(), 'recurrent_initializer': OrthogonalInitializerConfig().to_schema(), 'bias_initializer': ZerosInitializerConfig().to_schema(), 'kernel_regularizer': None, 'recurrent_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'recurrent_constraint': None, 'bias_constraint': None, 'dropout': 0., 'recurrent_dropout': 0., } config = SimpleRNNConfig.from_dict(config_dict) assert_equal_layers(config, config_dict)
def assert_local_config(local_class, dim): config_dict = { 'filters': 20, 'kernel_size': 3, 'strides': 1 if dim == 1 else [1, 1], 'padding': 'valid', 'data_format': None, 'activation': None, 'use_bias': True, 'kernel_initializer': GlorotUniformInitializerConfig().to_schema(), 'bias_initializer': ZerosInitializerConfig().to_schema(), 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, } if dim > 1: config_dict['data_format'] = None config = local_class.from_dict(config_dict) assert_equal_layers(config, config_dict)
def __init__(self, filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer=GlorotNormalInitializerConfig(), pointwise_initializer=GlorotNormalInitializerConfig(), bias_initializer=ZerosInitializerConfig(), depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None, **kwargs): super(SeparableConv2DConfig, self).__init__(**kwargs) self.filters = filters self.kernel_size = kernel_size self.strides = strides self.padding = padding self.data_format = data_format self.depth_multiplier = depth_multiplier self.activation = activation self.use_bias = use_bias self.depthwise_initializer = depthwise_initializer self.pointwise_initializer = pointwise_initializer self.bias_initializer = bias_initializer self.depthwise_regularizer = depthwise_regularizer self.pointwise_regularizer = pointwise_regularizer self.bias_regularizer = bias_regularizer self.activity_regularizer = activity_regularizer self.depthwise_constraint = depthwise_constraint self.pointwise_constraint = pointwise_constraint self.bias_constraint = bias_constraint
def __init__(self, units, activation=None, use_bias=True, kernel_initializer=GlorotNormalInitializerConfig(), bias_initializer=ZerosInitializerConfig(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs): super(DenseConfig, self).__init__(**kwargs) self.units = units self.activation = activation self.use_bias = use_bias self.kernel_initializer = kernel_initializer self.bias_initializer = bias_initializer self.kernel_regularizer = kernel_regularizer self.bias_regularizer = bias_regularizer self.activity_regularizer = activity_regularizer self.kernel_constraint = kernel_constraint self.bias_constraint = bias_constraint
def assert_conv_config(conv_class, dim): config_dict = { 'filters': 30, 'kernel_size': 3, 'strides': 1 if dim == 1 else [1, 1] if dim == 2 else [1, 1, 1], 'padding': 'valid', 'activation': 'relu', 'dilation_rate': 1 if dim == 1 else [1, 1] if dim == 2 else [1, 1, 1], 'use_bias': True, 'kernel_initializer': GlorotNormalInitializerConfig().to_schema(), 'bias_initializer': ZerosInitializerConfig().to_schema(), 'kernel_regularizer': L1L2RegularizerConfig().to_schema(), 'bias_regularizer': None, 'activity_regularizer': L1RegularizerConfig().to_schema(), 'kernel_constraint': MaxNormConfig().to_schema(), 'bias_constraint': None, 'inbound_nodes': [['layer_1', 0, 1], ['layer_2', 1, 1]] } if dim > 1: config_dict['data_format'] = None config = conv_class.from_dict(config_dict) assert_equal_layers(config, config_dict)