With Python 3.8 and TensorFlow 2.5, my objective is to remove filters/kernels having lowest L2 norms. Sample code for this is:
# Generate random 1 image/data point sample- x = tf.random.normal(shape = (1, 5, 5, 3), mean = 1.0, stddev = 0.5) x.shape # TensorShape([1, 5, 5, 3]) # Create conv layer- conv = Conv2D( filters = 3, kernel_size = (3, 3), activation='relu', kernel_initializer = tf.initializers.GlorotNormal(), bias_initializer = tf.ones_initializer, strides = (1, 1), padding = 'same', ) # Pass input through conv layer- out = conv(x) out.shape # TensorShape([1, 5, 5, 3]) out = tf.squeeze(out) out.shape # TensorShape([5, 5, 3])
According to my understanding, the output consists of three (5, 5) matrices stacked together. However, printing ‘out’ shows five (5, 3) matrices stacked together:
out.numpy() ''' array([[[1.45877 , 0. , 1.9293344 ], [0.9910869 , 0.01100129, 1.7364411 ], [1.8199034 , 0. , 1.3457474 ], [1.219409 , 0.22021294, 0.62214017], [0.5572515 , 0.7246016 , 0.6772853 ]], [[1.161148 , 0. , 2.0277915 ], [0.38071448, 0. , 2.2438798 ], [2.2897398 , 0.1658966 , 2.3147004 ], [1.2516301 , 0.14660472, 1.6381929 ], [1.1554463 , 0.72516847, 1.6170584 ]], [[0. , 0. , 1.2525308 ], [0.4337383 , 0. , 0.91200435], [0.71451795, 0. , 2.093022 ], [2.265062 , 0. , 2.7562256 ], [0.82517993, 0. , 1.8439718 ]], [[0.7089497 , 0. , 1.041831 ], [0. , 0. , 1.2754116 ], [0.41919613, 0. , 0.88135654], [0. , 0. , 0.71492153], [0.18725157, 0.27108306, 0.11248505]], [[0.86042166, 0.45840383, 1.084069 ], [0.53202367, 0.42414713, 1.2529668 ], [1.2257886 , 0.31592917, 1.3377004 ], [0.36588144, 0. , 0.6085663 ], [0.3065148 , 0.574654 , 1.0214479 ]]], dtype=float32) '''
So, if I use the code out[:, :, 0], out[:, :, 1] & out[:, :, 2], do they refer to the first, second and third filters?
And if yes, is computing L2-norm using:
tf.norm(out, ord = 'euclidean', axis = (0, 1)).numpy() # array([5.275869 , 1.4290226, 7.545658 ], dtype=float32)
the correct way?
submitted by /u/grid_world
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