Python keras.initializers.TruncatedNormal() Examples

The following are 10 code examples of keras.initializers.TruncatedNormal(). 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: seriesnet.py From seriesnet with MIT License

5 votes

def DC_CNN_Block(nb_filter, filter_length, dilation, l2_layer_reg):
    def f(input_):
        
        residual =    input_
        
        layer_out =   Conv1D(filters=nb_filter, kernel_size=filter_length, 
                      dilation_rate=dilation, 
                      activation='linear', padding='causal', use_bias=False,
                      kernel_initializer=TruncatedNormal(mean=0.0, stddev=0.05, 
                      seed=42), kernel_regularizer=l2(l2_layer_reg))(input_)
                    
        layer_out =   Activation('selu')(layer_out)
        
        skip_out =    Conv1D(1,1, activation='linear', use_bias=False, 
                      kernel_initializer=TruncatedNormal(mean=0.0, stddev=0.05, 
                      seed=42), kernel_regularizer=l2(l2_layer_reg))(layer_out)
        
        network_in =  Conv1D(1,1, activation='linear', use_bias=False, 
                      kernel_initializer=TruncatedNormal(mean=0.0, stddev=0.05, 
                      seed=42), kernel_regularizer=l2(l2_layer_reg))(layer_out)
                      
        network_out = Add()([residual, network_in])
        
        return network_out, skip_out
    
    return f

Example #2

Source File: seriesnet.py From seriesnet with MIT License

5 votes

def DC_CNN_Model(length):
    
    input = Input(shape=(length,1))
    
    l1a, l1b = DC_CNN_Block(32,2,1,0.001)(input)    
    l2a, l2b = DC_CNN_Block(32,2,2,0.001)(l1a) 
    l3a, l3b = DC_CNN_Block(32,2,4,0.001)(l2a)
    l4a, l4b = DC_CNN_Block(32,2,8,0.001)(l3a)
    l5a, l5b = DC_CNN_Block(32,2,16,0.001)(l4a)
    l6a, l6b = DC_CNN_Block(32,2,32,0.001)(l5a)
    l6b = Dropout(0.8)(l6b) #dropout used to limit influence of earlier data
    l7a, l7b = DC_CNN_Block(32,2,64,0.001)(l6a)
    l7b = Dropout(0.8)(l7b) #dropout used to limit influence of earlier data

    l8 =   Add()([l1b, l2b, l3b, l4b, l5b, l6b, l7b])
    
    l9 =   Activation('relu')(l8)
           
    l21 =  Conv1D(1,1, activation='linear', use_bias=False, 
           kernel_initializer=TruncatedNormal(mean=0.0, stddev=0.05, seed=42),
           kernel_regularizer=l2(0.001))(l9)

    model = Model(input=input, output=l21)
    
    adam = optimizers.Adam(lr=0.00075, beta_1=0.9, beta_2=0.999, epsilon=None, 
                           decay=0.0, amsgrad=False)

    model.compile(loss='mae', optimizer=adam, metrics=['mse'])
    
    return model

Example #3

Source File: squeezeDet.py From squeezedet-keras with MIT License

5 votes

def _fire_layer(self, name, input, s1x1, e1x1, e3x3, stdd=0.01):
            """
            wrapper for fire layer constructions

            :param name: name for layer
            :param input: previous layer
            :param s1x1: number of filters for squeezing
            :param e1x1: number of filter for expand 1x1
            :param e3x3: number of filter for expand 3x3
            :param stdd: standard deviation used for intialization
            :return: a keras fire layer
            """

            sq1x1 = Conv2D(
                name = name + '/squeeze1x1', filters=s1x1, kernel_size=(1, 1), strides=(1, 1), use_bias=True,
                padding='SAME', kernel_initializer=TruncatedNormal(stddev=stdd), activation="relu",
                kernel_regularizer=l2(self.config.WEIGHT_DECAY))(input)

            ex1x1 = Conv2D(
                name = name + '/expand1x1', filters=e1x1, kernel_size=(1, 1), strides=(1, 1), use_bias=True,
                padding='SAME',  kernel_initializer=TruncatedNormal(stddev=stdd), activation="relu",
                kernel_regularizer=l2(self.config.WEIGHT_DECAY))(sq1x1)

            ex3x3 = Conv2D(
                name = name + '/expand3x3',  filters=e3x3, kernel_size=(3, 3), strides=(1, 1), use_bias=True,
                padding='SAME', kernel_initializer=TruncatedNormal(stddev=stdd), activation="relu",
                kernel_regularizer=l2(self.config.WEIGHT_DECAY))(sq1x1)

            return concatenate([ex1x1, ex3x3], axis=3)

    #wrapper for padding, written in tensorflow. If you want to change to theano you need to rewrite this!

Example #4