zoo/inception/inception_v1_c.py
def stem(self, inputs):
        """ Construct the Stem Convolutional Group 
            inputs : the input vector
        """
        # The 224x224 images are zero padded (black - no signal) to be 230x230 images prior to the first convolution
        x = ZeroPadding2D(padding=(3, 3))(inputs)
    
        # First Convolutional layer which uses a large (coarse) filter 
        x = self.Conv2D(x, 64, (7, 7), strides=(2, 2), padding='valid')
        x = self.ReLU(x)

        # Pooled feature maps will be reduced by 75%
        x = ZeroPadding2D(padding=(1, 1))(x)
        x = MaxPooling2D((3, 3), strides=(2, 2))(x)

        # Second Convolutional layer which uses a mid-size filter
        x = self.Conv2D(x, 64, (1, 1), strides=(1, 1), padding='same')
        x = self.ReLU(x)
        x = ZeroPadding2D(padding=(1, 1))(x)
        x = self.Conv2D(x, 192, (3, 3), strides=(1, 1), padding='valid')
        x = self.ReLU(x)
    
        # Pooled feature maps will be reduced by 75%
        x = ZeroPadding2D(padding=(1, 1))(x)
        x = MaxPooling2D((3, 3), strides=(2, 2))(x)
        return x
 Construct the Stem Convolutional Group 
            inputs : the input vector
        
Construct the Stem Convolutional Group
inputs : the input vector
Construct the Stem Convolutional Group inputs : the input vector
def stem(self, inputs):
        x = ZeroPadding2D(padding=(3, 3))(inputs)
        x = self.Conv2D(x, 64, (7, 7), strides=(2, 2), padding='valid')
        x = self.ReLU(x)
        x = ZeroPadding2D(padding=(1, 1))(x)
        x = MaxPooling2D((3, 3), strides=(2, 2))(x)
        x = self.Conv2D(x, 64, (1, 1), strides=(1, 1), padding='same')
        x = self.ReLU(x)
        x = ZeroPadding2D(padding=(1, 1))(x)
        x = self.Conv2D(x, 192, (3, 3), strides=(1, 1), padding='valid')
        x = self.ReLU(x)
        x = ZeroPadding2D(padding=(1, 1))(x)
        x = MaxPooling2D((3, 3), strides=(2, 2))(x)
        return x
def stem ( self , inputs ) : x = ZeroPadding2D ( padding = ( 3 , 3 ) ) ( inputs ) x = self . Conv2D ( x , 64 , ( 7 , 7 ) , strides = ( 2 , 2 ) , padding = 'valid' ) x = self . ReLU ( x ) x = ZeroPadding2D ( padding = ( 1 , 1 ) ) ( x ) x = MaxPooling2D ( ( 3 , 3 ) , strides = ( 2 , 2 ) ) ( x ) x = self . Conv2D ( x , 64 , ( 1 , 1 ) , strides = ( 1 , 1 ) , padding = 'same' ) x = self . ReLU ( x ) x = ZeroPadding2D ( padding = ( 1 , 1 ) ) ( x ) x = self . Conv2D ( x , 192 , ( 3 , 3 ) , strides = ( 1 , 1 ) , padding = 'valid' ) x = self . ReLU ( x ) x = ZeroPadding2D ( padding = ( 1 , 1 ) ) ( x ) x = MaxPooling2D ( ( 3 , 3 ) , strides = ( 2 , 2 ) ) ( x ) return x
Construct the Stem Convolutional Group
inputs : the input vector
Construct the Stem Convolutional Group inputs : the input vector
""" Construct the Stem Convolutional Group 
            inputs : the input vector
        """ # The 224x224 images are zero padded (black - no signal) to be 230x230 images prior to the first convolution # First Convolutional layer which uses a large (coarse) filter  # Pooled feature maps will be reduced by 75% # Second Convolutional layer which uses a mid-size filter # Pooled feature maps will be reduced by 75%
self
inputs