我正在尝试实现一个简单的人脸识别应用程序，但我已经困难了好几天了。希望有更多经验的人可以提供帮助。手指交叉。
以下是该计划：
import tensorflow as tf
#from keras.applications.vgg16 import VGG16
from keras.applications.resnet50 import ResNet50, preprocess_input
from keras.preprocessing import image
#import PIL
#from PIL import Image
from keras.models import Model
from keras.layers import Dense, Input, subtract, concatenate, Lambda, add, maximum
from keras import backend as K
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam
import numpy as np
def identity_loss(y_true, y_pred):
return K.mean(y_pred - 0 * y_true)
def triplet_loss(inputs, dist='euclidean', margin='maxplus'):
anchor, positive, negative = inputs
positive_distance = K.square(anchor - positive)
negative_distance = K.square(anchor - negative)
if dist == 'euclidean':
positive_distance = K.sqrt(K.sum(positive_distance, axis=-1, keepdims=True))
negative_distance = K.sqrt(K.sum(negative_distance, axis=-1, keepdims=True))
elif dist == 'sqeuclidean':
positive_distance = K.sum(positive_distance, axis=-1, keepdims=True)
negative_distance = K.sum(negative_distance, axis=-1, keepdims=True)
loss = positive_distance - negative_distance
if margin == 'maxplus':
loss = K.maximum(0.0, 1 + loss)
elif margin == 'softplus':
loss = K.log(1 + K.exp(loss))
return K.mean(loss)
model = ResNet50(weights='imagenet')
model.layers.pop()
x = model.get_layer('flatten_1').output
model_out = Dense(128, activation='relu', name='model_out')(x)
model_out = Lambda(lambda x: K.l2_normalize(x,axis=-1))(model_out)
new_model = Model(inputs=model.input, outputs=model_out)
anchor_input = Input(shape=(224, 224, 3), name='anchor_input')
pos_input = Input(shape=(224, 224, 3), name='pos_input')
neg_input = Input(shape=(224, 224, 3), name='neg_input')
encoding_anchor = new_model(anchor_input)
encoding_pos = new_model(pos_input)
encoding_neg = new_model(neg_input)
loss = Lambda(triplet_loss)([encoding_anchor, encoding_pos, encoding_neg])
siamese_network = Model(inputs = [anchor_input, pos_input, neg_input],
outputs = loss)
siamese_network.compile(optimizer=Adam(lr=.00001), loss=identity_loss)
######################### For reading img path info - start ##########
train_path1 = '/home/cesncn/Desktop/github_projects/face_recog_proj_with_triplet_loss/training_img_pairs.csv'
TRAIN_INPUT_PATHS = [train_path1]
RECORD_DEFAULTS_TRAIN = [[0], [''], [''], ['']]
def decode_csv_train(line):
parsed_line = tf.decode_csv(line, RECORD_DEFAULTS_TRAIN)
anchor_path = parsed_line[1]
pos_path = parsed_line[2]
neg_path = parsed_line[3]
return anchor_path, pos_path, neg_path
######################### For reading img path info - end ##########
batch_size = 16
filenames = tf.placeholder(tf.string, shape=[None])
dataset = tf.data.Dataset.from_tensor_slices(filenames)
dataset = dataset.flat_map(lambda filename: tf.data.TextLineDataset(filename).skip(1).map(decode_csv_train))
dataset = dataset.shuffle(buffer_size=1000)
dataset = dataset.batch(batch_size)
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
init_global_var = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init_global_var)
nr_epochs = 5
for i in range(0, nr_epochs):
print("\nnr_epoch: ", str(i), "\n")
sess.run(iterator.initializer, feed_dict={filenames: TRAIN_INPUT_PATHS})
while True:
try:
anchor_path, pos_path, neg_path = sess.run(next_element)
anchor_imgs = np.empty((0, 224, 224, 3))
pos_imgs = np.empty((0, 224, 224, 3))
neg_imgs = np.empty((0, 224, 224, 3))
for j in range (0, len(anchor_path)):
anchor_img = image.load_img(anchor_path[j], target_size=(224, 224))
anchor_img = image.img_to_array(anchor_img)
anchor_img = np.expand_dims(anchor_img, axis=0)
anchor_img = preprocess_input(anchor_img)
anchor_imgs = np.append(anchor_imgs, anchor_img, axis=0)
pos_img = image.load_img(pos_path[j], target_size=(224, 224))
pos_img = image.img_to_array(pos_img)
pos_img = np.expand_dims(pos_img, axis=0)
pos_img = preprocess_input(pos_img)
pos_imgs = np.append(pos_imgs, pos_img, axis=0)
neg_img = image.load_img(neg_path[j], target_size=(224, 224))
neg_img = image.img_to_array(neg_img)
neg_img = np.expand_dims(neg_img, axis=0)
neg_img = preprocess_input(neg_img)
neg_imgs = np.append(neg_imgs, neg_img, axis=0)
# HERE IT CRASHES WHEN I CALL THE FIT FUNCTION! ! !
siamese_network.fit([anchor_imgs, pos_imgs, neg_imgs],
batch_size = batch_size,
epochs = 1,
verbose = 2)
except tf.errors.OutOfRangeError:
print("Out of range error triggered (looped through training set 1 time)")
break
当我调用函数siamese_network.fit（...）时，它会崩溃并给出以下错误，这绝对没有告诉我!!
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-16-65401d04281c> in <module>()
61 batch_size = batch_size,
62 epochs = 1,
---> 63 verbose = 2)
64
65 #siamese_network.fit({'anchor_input': anchor_imgs, 'pos_input': pos_imgs, 'neg_input': neg_imgs},
~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
1628 sample_weight=sample_weight,
1629 class_weight=class_weight,
-> 1630 batch_size=batch_size)
1631 # Prepare validation data.
1632 do_validation = False
~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
1485 sample_weights = [_standardize_weights(ref, sw, cw, mode)
1486 for (ref, sw, cw, mode)
-> 1487 in zip(y, sample_weights, class_weights, self._feed_sample_weight_modes)]
1488
1489 if check_array_lengths:
~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/keras/engine/training.py in <listcomp>(.0)
1484 self._feed_output_names)
1485 sample_weights = [_standardize_weights(ref, sw, cw, mode)
-> 1486 for (ref, sw, cw, mode)
1487 in zip(y, sample_weights, class_weights, self._feed_sample_weight_modes)]
1488
~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/keras/engine/training.py in _standardize_weights(y, sample_weight, class_weight, sample_weight_mode)
538 else:
539 if sample_weight_mode is None:
--> 540 return np.ones((y.shape[0],), dtype=K.floatx())
541 else:
542 return np.ones((y.shape[0], y.shape[1]), dtype=K.floatx())
AttributeError: 'NoneType' object has no attribute 'shape'
有没有人知道如何解决这个问题？
答案 0 :(得分：0)
我一直在努力实现三重损失函数的几种变体。以上版本只是其中一个尝试...
无论如何，在上面的版本中，问题似乎如下：
以下是我创建和编译模型的方法：
siamese_network = Model(inputs = [anchor_input, pos_input, neg_input],
outputs = loss)
siamese_network.compile(optimizer=Adam(lr=.00001), loss=identity_loss)
显然，我在这里说这个模型有一个输出，名为＆＃34; loss＆＃34;。
后来当我训练模型时，我在fit函数中将y设置为null。大错。
z = [0] # ADDED LINE
siamese_network.fit(x = [anchor_imgs, pos_imgs, neg_imgs],
y = z # ADDED LINE
batch_size = batch_size,
epochs = 1,
verbose = 2)
这个解决方案解决了这个问题。
我宁愿选择回答而不是删除问题。希望它可以帮助其他可能遇到类似问题的人。
PS。如果在Keras中没有设置为任何内容，则y默认设置为NULL。