def classify_embedding():
import classification_based
class_labels = unpickle('cifar-10-batches-py/batches.meta')['label_names']
# print class_labels
Y_GO_train = np.array(Y_train)
X_GO_train = np.array(X_train)
#removed_indices = np.where(Y_8_train!=8)
#Y_8_train = Y_8_train[removed_indices]
#X_8_train = X_8_train[removed_indices]
#removed_indices = np.where(Y_8_train!=9)
#Y_8_train = Y_8_train[removed_indices]
#X_8_train = X_8_train[removed_indices]
enc = OneHotEncoder(sparse=False)
Y_GO_train = enc.fit_transform(np.array(Y_GO_train).reshape(-1, 1))
Y_GO_validation = np.array(Y_validation)
X_GO_validation = np.array(X_validation)
#removed_indices = np.where(Y_8_validation!=8)
#Y_8_validation = Y_8_validation[removed_indices]
#X_8_validation = X_8_validation[removed_indices]
#removed_indices = np.where(Y_8_validation!=9)
#Y_8_validation = Y_8_validation[removed_indices]
#X_8_validation = X_8_validation[removed_indices]
Y_GO_validation = enc.fit_transform(
np.array(Y_GO_validation).reshape(-1, 1))
classification_based.train(X_GO_train, Y_GO_train, X_GO_validation,
Y_GO_validation)
#Y_2_validation = np.array(Y_validation)
#X_2_validation = np.array(X_validation)
#indices = np.where(Y_2_validation>=8)
#Y_2_validation = Y_2_validation[indices]
#X_2_validation = X_2_validation[indices]
validaiton_probab = classification_based.predict_probabilites(
X_GO_validation)
print validaiton_probab.shape
print validaiton_probab[0]
print Y_GO_validation[0]
weights = []
for i in class_labels:
weights.append(embeddings[i])
weights = np.array(weights, dtype=np.float32)
validaiton_embeddings = np.dot(validaiton_probab, weights)
print validaiton_embeddings.shape
targets_embeddings = []
targets_embeddings_words = []
for i in class_labels:
targets_embeddings.append(embeddings[i])
targets_embeddings_words.append(i)
for k in embeddings:
if k not in class_labels:
targets_embeddings.append(embeddings[k])
targets_embeddings_words.append(k)
targets_embeddings = np.array(targets_embeddings, dtype=np.float32)
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
from scipy.spatial.distance import cosine
Y_pred_validation = []
for i in validaiton_embeddings:
cos = []
for j in targets_embeddings:
val = 1 - cosine(i, j)
cos.append(val)
#vec = np.zeros(len(targets_embeddings))
#vec[np.argmax(cos)] = 1
Y_pred_validation.append(np.argmax(cos))
# neigh = KNeighborsClassifier(n_neighbors=1)
# neigh.fit(targets_embeddings, [0,1,2,3,4,5,6,7,8,9])
# Y_pred_validation = neigh.predict(validaiton_embeddings)
# print Y_2_validation
# print Y_pred_validation
print accuracy_score(np.argmax(Y_GO_validation, axis=1), Y_pred_validation)
def classify_embedding():
import classification_based
class_labels = unpickle('cifar-10-batches-py/batches.meta')['label_names']
# print class_labels
Y_8_train = np.array(Y_train)
X_8_train = np.array(X_train)
removed_indices = np.where(Y_8_train!=8)
Y_8_train = Y_8_train[removed_indices]
X_8_train = X_8_train[removed_indices]
removed_indices = np.where(Y_8_train!=9)
Y_8_train = Y_8_train[removed_indices]
X_8_train = X_8_train[removed_indices]
enc = OneHotEncoder(sparse=False)
Y_8_train = enc.fit_transform(np.array(Y_8_train).reshape(-1,1))
Y_8_validation = np.array(Y_validation)
X_8_validation = np.array(X_validation)
removed_indices = np.where(Y_8_validation!=8)
Y_8_validation = Y_8_validation[removed_indices]
X_8_validation = X_8_validation[removed_indices]
removed_indices = np.where(Y_8_validation!=9)
Y_8_validation = Y_8_validation[removed_indices]
X_8_validation = X_8_validation[removed_indices]
Y_8_validation = enc.fit_transform(np.array(Y_8_validation).reshape(-1,1))
classification_based.train(X_8_train, Y_8_train, X_8_validation, Y_8_validation)
Y_2_validation = np.array(Y_validation)
X_2_validation = np.array(X_validation)
indices = np.where(Y_2_validation>=8)
Y_2_validation = Y_2_validation[indices]
X_2_validation = X_2_validation[indices]
validaiton_probab = classification_based.predict_probabilites(X_2_validation)
weights = []
for i in class_labels[:-2]:
weights.append(embeddings[i])
weights = np.array(weights, dtype=np.float32)
validaiton_embeddings = np.dot(validaiton_probab, weights)
targets_embeddings = []
for i in class_labels:
targets_embeddings.append(embeddings[i])
targets_embeddings = np.array(targets_embeddings, dtype=np.float32)
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
from scipy.spatial.distance import cosine
Y_pred_validation = []
for i in validaiton_embeddings:
cos = []
for j in targets_embeddings:
val = cosine(i,j)
cos.append(val)
Y_pred_validation.append(np.argmax(cos))
# neigh = KNeighborsClassifier(n_neighbors=1)
# neigh.fit(targets_embeddings, [0,1,2,3,4,5,6,7,8,9])
# Y_pred_validation = neigh.predict(validaiton_embeddings)
# print Y_2_validation
# print Y_pred_validation
print accuracy_score(Y_2_validation, Y_pred_validation)
for i,j in zip(Y_2_validation, Y_pred_validation):
print (i,j)