ValueError: X.shape[1] = 128 should be equal to 512, the number of features at training time
1
Currently i am working on facial recognition and i am having the following issue with the given code.
for i in range(nrof_faces):
emb_array = np.zeros((1, embedding_size))
bb[i][0] = det[i][0]
bb[i][1] = det[i][1]
bb[i][2] = det[i][2]
bb[i][3] = det[i][3]
# inner exception
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][2] >= len(frame[0]) or bb[i]
[3] >= len(frame):
print('Face is very close!')
continue
cropped.append(frame[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2], :])
cropped[i] = facenet.flip(cropped[i], False)
scaled.append(misc.imresize(cropped[i], (image_size, image_size),
interp='bilinear'))
scaled[i] = cv2.resize(scaled[i], (input_image_size,input_image_size),
interpolation=cv2.INTER_CUBIC)
scaled[i] = facenet.prewhiten(scaled[i])
scaled_reshape.append(scaled[i].reshape(-1,input_image_size,input_image_size,3))
feed_dict = images_placeholder: scaled_reshape[i], phase_train_placeholder: False
emb_array[0, :] = sess.run(embeddings, feed_dict=feed_dict)
predictions = model.predict_proba(emb_array)
print(predictions)
And it is giving me following error:
Traceback (most recent call last):
File "F:stdprogramspythonCameraFacenet-Real-time-face-recognition-using-deep-learning-Tensorflowtest_video.py", line 107, in <module>
predictions = model.predict_proba(emb_array)
File "C:Program FilesPython36libsite-packagessklearnsvmbase.py", line 613, in _predict_proba
X = self._validate_for_predict(X)
File "C:Program FilesPython36libsite-packagessklearnsvmbase.py", line 478, in _validate_for_predict
(n_features, self.shape_fit_[1]))
ValueError: X.shape[1] = 128 should be equal to 512, the number of features at training time
python tensorflow scikit-learn svm
asked Nov 12 '18 at 16:57
MeeT ValaniMeeT Valani
61
add a comment |
1
Currently i am working on facial recognition and i am having the following issue with the given code.
for i in range(nrof_faces):
emb_array = np.zeros((1, embedding_size))
bb[i][0] = det[i][0]
bb[i][1] = det[i][1]
bb[i][2] = det[i][2]
bb[i][3] = det[i][3]
# inner exception
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][2] >= len(frame[0]) or bb[i]
[3] >= len(frame):
print('Face is very close!')
continue
cropped.append(frame[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2], :])
cropped[i] = facenet.flip(cropped[i], False)
scaled.append(misc.imresize(cropped[i], (image_size, image_size),
interp='bilinear'))
scaled[i] = cv2.resize(scaled[i], (input_image_size,input_image_size),
interpolation=cv2.INTER_CUBIC)
scaled[i] = facenet.prewhiten(scaled[i])
scaled_reshape.append(scaled[i].reshape(-1,input_image_size,input_image_size,3))
feed_dict = images_placeholder: scaled_reshape[i], phase_train_placeholder: False
emb_array[0, :] = sess.run(embeddings, feed_dict=feed_dict)
predictions = model.predict_proba(emb_array)
print(predictions)
And it is giving me following error:
Traceback (most recent call last):
File "F:stdprogramspythonCameraFacenet-Real-time-face-recognition-using-deep-learning-Tensorflowtest_video.py", line 107, in <module>
predictions = model.predict_proba(emb_array)
File "C:Program FilesPython36libsite-packagessklearnsvmbase.py", line 613, in _predict_proba
X = self._validate_for_predict(X)
File "C:Program FilesPython36libsite-packagessklearnsvmbase.py", line 478, in _validate_for_predict
(n_features, self.shape_fit_[1]))
ValueError: X.shape[1] = 128 should be equal to 512, the number of features at training time
python tensorflow scikit-learn svm
asked Nov 12 '18 at 16:57
MeeT ValaniMeeT Valani
61
Check that your train data (the one you built the model upon) and your new data (the data you are trying to classify) have the exact same attributes
– Roee Anuar
Nov 13 '18 at 15:14
add a comment |
1
1
1
Currently i am working on facial recognition and i am having the following issue with the given code.
for i in range(nrof_faces):
emb_array = np.zeros((1, embedding_size))
bb[i][0] = det[i][0]
bb[i][1] = det[i][1]
bb[i][2] = det[i][2]
bb[i][3] = det[i][3]
# inner exception
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][2] >= len(frame[0]) or bb[i]
[3] >= len(frame):
print('Face is very close!')
continue
cropped.append(frame[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2], :])
cropped[i] = facenet.flip(cropped[i], False)
scaled.append(misc.imresize(cropped[i], (image_size, image_size),
interp='bilinear'))
scaled[i] = cv2.resize(scaled[i], (input_image_size,input_image_size),
interpolation=cv2.INTER_CUBIC)
scaled[i] = facenet.prewhiten(scaled[i])
scaled_reshape.append(scaled[i].reshape(-1,input_image_size,input_image_size,3))
feed_dict = images_placeholder: scaled_reshape[i], phase_train_placeholder: False
emb_array[0, :] = sess.run(embeddings, feed_dict=feed_dict)
predictions = model.predict_proba(emb_array)
print(predictions)
And it is giving me following error:
Traceback (most recent call last):
File "F:stdprogramspythonCameraFacenet-Real-time-face-recognition-using-deep-learning-Tensorflowtest_video.py", line 107, in <module>
predictions = model.predict_proba(emb_array)
File "C:Program FilesPython36libsite-packagessklearnsvmbase.py", line 613, in _predict_proba
X = self._validate_for_predict(X)
File "C:Program FilesPython36libsite-packagessklearnsvmbase.py", line 478, in _validate_for_predict
(n_features, self.shape_fit_[1]))
ValueError: X.shape[1] = 128 should be equal to 512, the number of features at training time
python tensorflow scikit-learn svm
asked Nov 12 '18 at 16:57
MeeT ValaniMeeT Valani
61
Currently i am working on facial recognition and i am having the following issue with the given code.
for i in range(nrof_faces):
emb_array = np.zeros((1, embedding_size))
bb[i][0] = det[i][0]
bb[i][1] = det[i][1]
bb[i][2] = det[i][2]
bb[i][3] = det[i][3]
# inner exception
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][2] >= len(frame[0]) or bb[i]
[3] >= len(frame):
print('Face is very close!')
continue
cropped.append(frame[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2], :])
cropped[i] = facenet.flip(cropped[i], False)
scaled.append(misc.imresize(cropped[i], (image_size, image_size),
interp='bilinear'))
scaled[i] = cv2.resize(scaled[i], (input_image_size,input_image_size),
interpolation=cv2.INTER_CUBIC)
scaled[i] = facenet.prewhiten(scaled[i])
scaled_reshape.append(scaled[i].reshape(-1,input_image_size,input_image_size,3))
feed_dict = images_placeholder: scaled_reshape[i], phase_train_placeholder: False
emb_array[0, :] = sess.run(embeddings, feed_dict=feed_dict)
predictions = model.predict_proba(emb_array)
print(predictions)
And it is giving me following error:
Traceback (most recent call last):
File "F:stdprogramspythonCameraFacenet-Real-time-face-recognition-using-deep-learning-Tensorflowtest_video.py", line 107, in <module>
predictions = model.predict_proba(emb_array)
File "C:Program FilesPython36libsite-packagessklearnsvmbase.py", line 613, in _predict_proba
X = self._validate_for_predict(X)
File "C:Program FilesPython36libsite-packagessklearnsvmbase.py", line 478, in _validate_for_predict
(n_features, self.shape_fit_[1]))
ValueError: X.shape[1] = 128 should be equal to 512, the number of features at training time
python tensorflow scikit-learn svm
python tensorflow scikit-learn svm
asked Nov 12 '18 at 16:57
MeeT ValaniMeeT Valani
61
asked Nov 12 '18 at 16:57
MeeT ValaniMeeT Valani
61
asked Nov 12 '18 at 16:57
MeeT ValaniMeeT Valani
61
asked Nov 12 '18 at 16:57
MeeT ValaniMeeT Valani
61
asked Nov 12 '18 at 16:57
MeeT ValaniMeeT Valani
61
61
Check that your train data (the one you built the model upon) and your new data (the data you are trying to classify) have the exact same attributes
– Roee Anuar
Nov 13 '18 at 15:14
add a comment |
Check that your train data (the one you built the model upon) and your new data (the data you are trying to classify) have the exact same attributes
– Roee Anuar
Nov 13 '18 at 15:14
Check that your train data (the one you built the model upon) and your new data (the data you are trying to classify) have the exact same attributes
– Roee Anuar
Nov 13 '18 at 15:14
Check that your train data (the one you built the model upon) and your new data (the data you are trying to classify) have the exact same attributes
– Roee Anuar
Nov 13 '18 at 15:14
add a comment |
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Check that your train data (the one you built the model upon) and your new data (the data you are trying to classify) have the exact same attributes
– Roee Anuar
Nov 13 '18 at 15:14