Gfyuki

import numpy as np



def deConv(Z, cashe):



'''



deConv calculate the transpoe Convoultion between the output of the ConvNet and the filter



Arguments:

    Z-- Output of the ConvNet Layer, an array of the shape()

'''

# Retrieve information from "cache"

(X_prev, W, b, s, p) = cashe



# Retrieve dimensions from X_prev's shape

(m, n_H_prev, n_W_prev, n_C_prev) = X_prev.shape 



# Retrieve dimensions from W's shape

(f, f, n_C_prev, n_C) = W.shape



# Retrieve dimensions from Z's shape

(m, n_H, n_W, n_C) = Z.shape



#create initial array for the output of the Deconvolution

X_curr = np.zeros((m, n_H_prev, n_W_prev, n_C_prev))



#loop over the Training examples

for i in range (m):



    #loop over the vertical of the output

    for h in range(n_H):



        #loop over the horizontal of the output

        for w in range(n_W):



            #loop over the 

            for c in range (n_C):



                #loop over the color channels

                for x in range(n_C_prev):



                    #inverse_W = np.linalg.pinv(W[:, :, x, c])

                    transpose_W = np.transpose(W[:,:,x,c]) 

                    #X_curr[i, h*s:h*s+f, w*s:w*s+f, x] +=  Z[i, h, w, c] * inverse_W

                    X_curr[i, h*s:h*s+f, w*s:w*s+f, x] +=  Z[i, h, w, c] * transpose_W

                X_curr[i, h*s:h*s+f, w*s:w*s+f, :] += b[:,:,:,c]



X_curr = relu(X_curr)



return X_curr

import numpy as np



def deConv(Z, cashe):



'''



deConv calculate the transpoe Convoultion between the output of the ConvNet and the filter



Arguments:

    Z-- Output of the ConvNet Layer, an array of the shape()

'''

# Retrieve information from "cache"

(X_prev, W, b, s, p) = cashe



# Retrieve dimensions from X_prev's shape

(m, n_H_prev, n_W_prev, n_C_prev) = X_prev.shape 



# Retrieve dimensions from W's shape

(f, f, n_C_prev, n_C) = W.shape



# Retrieve dimensions from Z's shape

(m, n_H, n_W, n_C) = Z.shape



#create initial array for the output of the Deconvolution

X_curr = np.zeros((m, n_H_prev, n_W_prev, n_C_prev))



#loop over the Training examples

for i in range (m):



    #loop over the vertical of the output

    for h in range(n_H):



        #loop over the horizontal of the output

        for w in range(n_W):



            #loop over the 

            for c in range (n_C):



                #loop over the color channels

                for x in range(n_C_prev):



                    #inverse_W = np.linalg.pinv(W[:, :, x, c])

                    transpose_W = np.transpose(W[:,:,x,c]) 

                    #X_curr[i, h*s:h*s+f, w*s:w*s+f, x] +=  Z[i, h, w, c] * inverse_W

                    X_curr[i, h*s:h*s+f, w*s:w*s+f, x] +=  Z[i, h, w, c] * transpose_W

                X_curr[i, h*s:h*s+f, w*s:w*s+f, :] += b[:,:,:,c]



X_curr = relu(X_curr)



return X_curr

import numpy as np



def deConv(Z, cashe):



'''



deConv calculate the transpoe Convoultion between the output of the ConvNet and the filter



Arguments:

    Z-- Output of the ConvNet Layer, an array of the shape()

'''

# Retrieve information from "cache"

(X_prev, W, b, s, p) = cashe



# Retrieve dimensions from X_prev's shape

(m, n_H_prev, n_W_prev, n_C_prev) = X_prev.shape 



# Retrieve dimensions from W's shape

(f, f, n_C_prev, n_C) = W.shape



# Retrieve dimensions from Z's shape

(m, n_H, n_W, n_C) = Z.shape



#create initial array for the output of the Deconvolution

X_curr = np.zeros((m, n_H_prev, n_W_prev, n_C_prev))



#loop over the Training examples

for i in range (m):



    #loop over the vertical of the output

    for h in range(n_H):



        #loop over the horizontal of the output

        for w in range(n_W):



            #loop over the 

            for c in range (n_C):



                #loop over the color channels

                for x in range(n_C_prev):



                    #inverse_W = np.linalg.pinv(W[:, :, x, c])

                    transpose_W = np.transpose(W[:,:,x,c]) 

                    #X_curr[i, h*s:h*s+f, w*s:w*s+f, x] +=  Z[i, h, w, c] * inverse_W

                    X_curr[i, h*s:h*s+f, w*s:w*s+f, x] +=  Z[i, h, w, c] * transpose_W

                X_curr[i, h*s:h*s+f, w*s:w*s+f, :] += b[:,:,:,c]



X_curr = relu(X_curr)



return X_curr

import numpy as np



def deConv(Z, cashe):



'''



deConv calculate the transpoe Convoultion between the output of the ConvNet and the filter



Arguments:

    Z-- Output of the ConvNet Layer, an array of the shape()

'''

# Retrieve information from "cache"

(X_prev, W, b, s, p) = cashe



# Retrieve dimensions from X_prev's shape

(m, n_H_prev, n_W_prev, n_C_prev) = X_prev.shape 



# Retrieve dimensions from W's shape

(f, f, n_C_prev, n_C) = W.shape



# Retrieve dimensions from Z's shape

(m, n_H, n_W, n_C) = Z.shape



#create initial array for the output of the Deconvolution

X_curr = np.zeros((m, n_H_prev, n_W_prev, n_C_prev))



#loop over the Training examples

for i in range (m):



    #loop over the vertical of the output

    for h in range(n_H):



        #loop over the horizontal of the output

        for w in range(n_W):



            #loop over the 

            for c in range (n_C):



                #loop over the color channels

                for x in range(n_C_prev):



                    #inverse_W = np.linalg.pinv(W[:, :, x, c])

                    transpose_W = np.transpose(W[:,:,x,c]) 

                    #X_curr[i, h*s:h*s+f, w*s:w*s+f, x] +=  Z[i, h, w, c] * inverse_W

                    X_curr[i, h*s:h*s+f, w*s:w*s+f, x] +=  Z[i, h, w, c] * transpose_W

                X_curr[i, h*s:h*s+f, w*s:w*s+f, :] += b[:,:,:,c]



X_curr = relu(X_curr)



return X_curr

Your Answer

StackExchange.ifUsing("editor", function () {
return StackExchange.using("mathjaxEditing", function () {
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
});
});
}, "mathjax-editing");

StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "557"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});

function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});


}
});