# Image classification
Antonin DELORME - TD 1 sur le cours Deep Learning
## Pepare the CIFAR dataset
### 2.
```rb
def read_cifar_batch(path):
""" path = "data\cifar-10-batches-py\data_batch_1"
par exemple """
with open(path, 'rb') as fo:
dict = pickle.load(fo, encoding='bytes')
labels=list(dict.items())[1][1] #labels[i] est le label de l'ième image
data=list(dict.items())[2][1] #data[i] sont les 3072 pixel de l'image i
return (labels,data)
```
### 3.
```rb
def read_cifar(path):
""" path="data\cifar-10-batches-py" par exemple """
(labels,data)=read_cifar_batch(path+"\\test_batch")
for i in range(1,6):
data=np.concatenate((data,read_cifar_batch(path+"\\data_batch_"+str(i))[1]),axis=0)
labels=labels+read_cifar_batch(path+"\\data_batch_"+str(i))[0]
return (labels,data)
```
### 4.
```rb
def split_dataset(labels,data,split):
split=round(split*len(labels))
test=[]
while len(test) != split:
Nb=rd.randint(0,len(labels)-1)
if Nb not in test :
test.append(Nb)
train=[i for i in range(len(labels)) if i not in test]
data_train=data[train]
data_test=data[test]
labels_test=[]
labels_train=[]
for i in test:
labels_test.append(labels[i])
for j in train:
labels_train.append(labels[j])
return(data_train,labels_train,data_test,labels_test)
```
## KNN
### 1.
```rb
def distance_matrix(a,b):
at=np.transpose(a)
bt=np.transpose(b)
som_carr_a=np.sum(np.square(a), axis=1, keepdims=True)
som_carr_b=np.sum(np.square(b), axis=1, keepdims=True)
prod=np.dot(a,bt)
return ( np.sqrt(som_carr_a + np.transpose(som_carr_b) - 2 * prod) )
```
### 2.
```rb
def knn_predict(dists,labels_train,k):
predict=[]
for i in range(len(dists)):
Glob_dist=[]
Glob_min=[]
for j in range(len(dists[i])):
Glob_dist.append(dists[i][j])
for p in range(k):
m = min(Glob_dist)
index = Glob_dist.index(m)
Glob_min.append(labels_train[index])
del(Glob_dist[index])
Temp=np.bincount(Glob_min)
predict.append(list(Temp).index(max(Temp)))
return predict
```
### 3.
```rb
def evaluate_knn(data_train,data_test,labels_train,labels_test,k,dist):
pred=knn_predict(dist, labels_train, k)
tot=0
bon=0
for i in range(len(labels_test)):
if labels_test[i]==pred[i]:
bon+=1
tot+=1
print("Accuracy :",bon/tot)
return bon/tot
```
### 4.
