bpatra

operation TwoBitstringSuperposition (qs : Qubit[], bits1 : Bool[], bits2 : Bool[]) : Unit {
    mutable i0 = Length(qs);
    for (i in 0..Length(qs)-1){
        if(bits1[i] == bits2[i]){
            if(bits1[i]){
                X(qs[i]);
            }
        }else{
            if(i > i0){
                if(bits1[i] == bits1[i0]){

bpatra

---
layout: about/about_lyt
title: "A propos"
lang: fr
---

bpatra

---
layout: about/about_lyt
title: "about"
lang: en
---

bpatra

---
layout: default
t:
first:
en: "This is my first sentence"
fr: "C'est ma premiàre phrase"
second:
en: "My second sentence"
fr: "Ma deuxième phrase"
third:

bpatra

├── _site
|    └── ...
├── _layouts
|    ├── about
|    |    └──about_lyt.html
|    └── hello_lyt.html
├── fr
|    ├── à propos
|    |    └── apropos.html
|    └── bonjour.html

bpatra

├── fr
|    ├── à propos
|    |    └── apropos.html
|    └── bonjour.html
├── about
|    └──about.html
└── hello.html

bpatra

├── _config.yml
├── _layouts
|    ├── default.html
|    └── post.html
├── _site
|    └── ...
└── index.html

bpatra

from inception_resnet_v1 import *

def model_with_inception_resnet_base(pretrained_weights):
  model = InceptionResNetV1()

  if pretrained_weights == True:
  #pre-trained weights https://drive.google.com/file/d/1971Xk5RwedbudGgTIrGAL4F7Aifu7id1/view?usp=sharing
    model.load_weights('facenet_weights.h5')

  new_model = models.Sequential()

bpatra

Layer (type) Output Shape Param #
=================================================================
inception_resnet_v1 (Model) (None, 128) 22808144
_________________________________________________________________
dense_1 (Dense) (None, 256) 33024
_________________________________________________________________
dropout_1 (Dropout) (None, 256) 0
_________________________________________________________________
dense_2 (Dense) (None, 64) 16448
_________________________________________________________________

bpatra

layer_outputs = [layer.output for layer in model.layers[:depth]]
activation_model = models.Model(inputs=model.input, outputs=layer_outputs)
predictions = models.predict(input_img_tensor)