zoo/siamese_twin/siamese_twin_c.py
def learner(x):
            ''' Construct the learner 
                x   : input to the learner
            '''
    
            # 2nd convolutional layer doubling the number of filters, and reduce feature maps by 75% (max pooling)
            x = self.Conv2D(x, 128, (7, 7), activation='relu', kernel_initializer=conv_weights, bias_initializer=biases)
            x = MaxPooling2D((2, 2), strides=2)(x)
    
            # 3rd convolutional layer and reduce feature maps by 75% (max pooling)
            x = self.Conv2D(x, 128, (4, 4), activation='relu', kernel_initializer=conv_weights, bias_initializer=biases)
            x = MaxPooling2D((2, 2), strides=2)(x)
        
            # 4th convolutional layer doubling the number of filters with no feature map downsampling
            x = self.Conv2D(x, 256, (4, 4), activation='relu', kernel_initializer=conv_weights, bias_initializer=biases)

            # for a 105x105 input, the feature map size will be 6x6
            return x
 Construct the learner 
                x   : input to the learner
            
Construct the learner
x   : input to the learner
Construct the learner x : input to the learner
def learner(x):
            x = self.Conv2D(x, 128, (7, 7), activation='relu', kernel_initializer=conv_weights, bias_initializer=biases)
            x = MaxPooling2D((2, 2), strides=2)(x)
            x = self.Conv2D(x, 128, (4, 4), activation='relu', kernel_initializer=conv_weights, bias_initializer=biases)
            x = MaxPooling2D((2, 2), strides=2)(x)
            x = self.Conv2D(x, 256, (4, 4), activation='relu', kernel_initializer=conv_weights, bias_initializer=biases)
            return x
def learner ( x ) : x = self . Conv2D ( x , 128 , ( 7 , 7 ) , activation = 'relu' , kernel_initializer = conv_weights , bias_initializer = biases ) x = MaxPooling2D ( ( 2 , 2 ) , strides = 2 ) ( x ) x = self . Conv2D ( x , 128 , ( 4 , 4 ) , activation = 'relu' , kernel_initializer = conv_weights , bias_initializer = biases ) x = MaxPooling2D ( ( 2 , 2 ) , strides = 2 ) ( x ) x = self . Conv2D ( x , 256 , ( 4 , 4 ) , activation = 'relu' , kernel_initializer = conv_weights , bias_initializer = biases ) return x
Construct the learner
x   : input to the learner
Construct the learner x : input to the learner
''' Construct the learner 
                x   : input to the learner
            ''' # 2nd convolutional layer doubling the number of filters, and reduce feature maps by 75% (max pooling) # 3rd convolutional layer and reduce feature maps by 75% (max pooling) # 4th convolutional layer doubling the number of filters with no feature map downsampling # for a 105x105 input, the feature map size will be 6x6
x