alidastgheib

From an open Jupyter Notebook homework assignment, select "Coursera" to take you to the home page. Make a new notebook and fill it with the following and excute the cell with:

%%bash
tar cvfz hw.tar.gz .

This may take a little while to run depending on the packages. Select "Coursera" again to take you to the Home directory. Check the hw.tar.gz file and then Download. After the file is downloaded, delete it.

alidastgheib

import keras.backend as K
import tensorflow as tf

def samplewise_tf_dct(x_sample):
    print('=======> input of "samplewise_tf_dct"')
    print(type(x_sample))
    print((x_sample.shape))
    print(x_sample)
    
    output_list = [] 

alidastgheib

import keras.backend as K

def samplewise_tf_dct(x_sample):
    print('=======> input of "samplewise_tf_dct"')
    print(type(x_sample))
    print((x_sample.shape))
    print(x_sample)
    
    for idx_channel in range(x_sample.shape[-1]):
        x_sample[:, :, idx_channel] = K.spectral.dct(K.transpose(K.spectral.dct(x_sample[:, :, idx_channel]))) # a 2D DCT    

alidastgheib

import keras.backend as K # which is Tensorflow

def dct_layer_function(x_batch):
    output = K.zeros(x_batch.shape)
    batch_size = x_batch.shape[0]
    channel_size = x_batch.shape[-1]

    for idx_batch in range(batch_size):
        for idx_channel in range(channel_size):
            output[idx_batch, :, :, idx_channel] = K.spectral.dct(