Keras, Tensorflow, ValueError

把csdn上一个颜值打分程序放到jupyter notebook上跑,程序如下:

from keras.applications import ResNet50
from keras import optimizers
from keras.layers import Dense, Dropout
from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint
from keras.backend.tensorflow_backend import set_session


os.environ['CUDA_VISIBLE_DEVICES'] = '1'
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))


batch_size = 32
target_size = (224, 224)

resnet = ResNet50(include_top=False, pooling='avg')
resnet.trainable = False
# keras.backend.clear_session()
# tf.reset_default_graph() 
model = Sequential()
model.add(resnet)
model.add(Dropout(0.5))   
model.add(Dense(1, activation='sigmoid')) 
print(model.summary())
model.compile(optimizer=optimizers.SGD(lr=0.001), loss='mse')

callbacks = [EarlyStopping(monitor='val_loss',
                           patience=5,
                           verbose=1,
                           min_delta=1e-4),
             ReduceLROnPlateau(monitor='val_loss',
                               patience=3,
                               factor=0.1,
                               epsilon=1e-4),
             ModelCheckpoint(monitor='val_loss',
                             filepath='weights/resnet50_weights.hdf5',
                             save_best_only=True,
                             save_weights_only=True)]

train_file_list, test_file_list = read_data_list()
train_steps_per_epoch = math.ceil(len(train_file_list) / batch_size)
test_steps_per_epoch = math.ceil(len(test_file_list) / batch_size)

train_data = DataGenerator(train_file_list, target_size,batch_size)
test_data = DataGenerator(test_file_list, target_size, batch_size)

model.fit_generator(train_data,
                    steps_per_epoch=train_steps_per_epoch,
                    epochs=30,
                    verbose=1,
                    callbacks=callbacks,
                    validation_data=test_data,
                    validation_steps=test_steps_per_epoch,
                    use_multiprocessing=True)

结果引发如下错误:

ValueError Traceback (most recent call last)
in ()
20 # tf.reset_default_graph()
21 model = Sequential()
---> 22 model.add(resnet)
23 model.add(Dropout(0.5))
24 model.add(Dense(1, activation='sigmoid'))

...Ignoring many tracing lines...

ValueError: Variable bn_conv1/moving_mean/biased already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:

File "xxxx\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1269, in init
self._traceback = _extract_stack()
File "xxxx\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "xxxx\anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 767, in apply_op
op_def=op_def)

    我按照网上说法在model语句前加了tf.reset_default_graph() ,结果又产生新的error:
    ValueError: Tensor("conv1_1/kernel:0", shape=(7, 7, 3, 64), dtype=float32_ref) must be from the same graph as Tensor("resnet50/conv1_pad/Pad:0", shape=(?, ?, ?, 3), dtype=float32).

    又按照网上说法加了keras.backend.clear_session(),总共加的两句前前后后在很多地方放了测试,结果都会有新的问题:
    ValueError: Tensor("conv1/kernel:0", shape=(7, 7, 3, 64), dtype=float32_ref) must be from the same graph as Tensor("resnet50/conv1_pad/Pad:0", shape=(?, ?, ?, 3), dtype=float32).


    请教大牛究竟该如何彻底解决问题。
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ValueError: None values not supported.

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Traceback (most recent call last): File "F:/python3.5/projects/untitled1/CNN/MN/test2.py", line 11, in <module> from keras.models import Sequential File "F:\python3.5\lib\site-packages\keras\__init__.py", line 3, in <module> from . import utils File "F:\python3.5\lib\site-packages\keras\utils\__init__.py", line 6, in <module> from . import conv_utils File "F:\python3.5\lib\site-packages\keras\utils\conv_utils.py", line 9, in <module> from .. import backend as K File "F:\python3.5\lib\site-packages\keras\backend\__init__.py", line 72, in <module> assert _backend in {'theano', 'tensorflow', 'cntk'} AssertionError 为什么kears出现这种错误 后端的tensorflow也配置了 求大神解答一下

Segnet网络用keras实现的时候报错ValueError,求大神帮忙看看

![图片说明](https://img-ask.csdn.net/upload/201904/05/1554454470_801036.jpg) 报错为:Error when checking target: expected activation_1 to have 3 dimensions, but got array with shape (32, 10) keras+tensorflow后端 代码如下 ``` # coding=utf-8 import matplotlib from PIL import Image matplotlib.use("Agg") import matplotlib.pyplot as plt import argparse import numpy as np from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D, UpSampling2D, BatchNormalization, Reshape, Permute, Activation, Flatten # from keras.utils.np_utils import to_categorical # from keras.preprocessing.image import img_to_array from keras.models import Model from keras.layers import Input from keras.callbacks import ModelCheckpoint # from sklearn.preprocessing import LabelBinarizer # from sklearn.model_selection import train_test_split # import pickle import matplotlib.pyplot as plt import os from keras.preprocessing.image import ImageDataGenerator train_datagen = ImageDataGenerator( rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) test_datagen = ImageDataGenerator(rescale=1./255) path = '/tmp/2' os.chdir(path) training_set = train_datagen.flow_from_directory( 'trainset', target_size=(64,64), batch_size=32, class_mode='categorical', shuffle=True) test_set = test_datagen.flow_from_directory( 'testset', target_size=(64,64), batch_size=32, class_mode='categorical', shuffle=True) def SegNet(): model = Sequential() # encoder model.add(Conv2D(64, (3, 3), strides=(1, 1), input_shape=(64, 64, 3), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(64, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) # (128,128) model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) # (64,64) model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) # (32,32) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) # (16,16) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) # (8,8) # decoder model.add(UpSampling2D(size=(2, 2))) # (16,16) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(UpSampling2D(size=(2, 2))) # (32,32) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(UpSampling2D(size=(2, 2))) # (64,64) model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(UpSampling2D(size=(2, 2))) # (128,128) model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(UpSampling2D(size=(2, 2))) # (256,256) model.add(Conv2D(64, (3, 3), strides=(1, 1), input_shape=(64, 64, 3), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(64, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(10, (1, 1), strides=(1, 1), padding='valid', activation='relu')) model.add(BatchNormalization()) model.add(Reshape((64*64, 10))) # axis=1和axis=2互换位置,等同于np.swapaxes(layer,1,2) model.add(Permute((2, 1))) #model.add(Flatten()) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) model.summary() return model def main(): model = SegNet() filepath = "/tmp/2/weights.best.hdf5" checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max') callbacks_list = [checkpoint] history = model.fit_generator( training_set, steps_per_epoch=(training_set.samples / 32), epochs=20, callbacks=callbacks_list, validation_data=test_set, validation_steps=(test_set.samples / 32)) # Plotting the Loss and Classification Accuracy model.metrics_names print(history.history.keys()) # "Accuracy" plt.plot(history.history['acc']) plt.plot(history.history['val_acc']) plt.title('Model Accuracy') plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend(['train', 'test'], loc='upper left') plt.show() # "Loss" plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('Model loss') plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['train', 'test'], loc='upper left') plt.show() if __name__ == '__main__': main() ``` 主要是这里,segnet没有全连接层,最后输出的应该是一个和输入图像同等大小的有判别标签的shape吗。。。求教怎么改。 输入图像是64 64的,3通道,总共10类,分别放在testset和trainset两个文件夹里

ValueError: No gradients provided for any variable?

在使用卷积神经网络+全连接神经网络计算句子相似度训练模型出现无梯度的问题。 以下是源代码 ``` import numpy as np import tensorflow as tf from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics import os import math os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' #构建输入的向量 sentence_x = np.random.randn(1000, 38, 300) sentence_x = tf.cast(tf.reshape(sentence_x, [1000, 38, 300, 1]), dtype=tf.float32) sentence_y = np.random.randn(1000, 38, 300) sentence_y = tf.cast(tf.reshape(sentence_y, [1000, 38, 300, 1]), dtype=tf.float32) label = np.random.randint(0, 2, (1, 1000)) label = tf.reshape(label, [1000]) train_db = tf.data.Dataset.from_tensor_slices((sentence_x, sentence_y, label)) train_db = train_db.shuffle(100).batch(20) #卷积层 conv_layers = [ # 5 units of 2 * conv +maxpooling # unit 1 layers.Conv2D(3, kernel_size=[2, 2], strides=[2, 2], padding='same', activation = tf.nn.relu), layers.Conv2D(3, kernel_size=[2, 2], padding='same', activation = tf.nn.relu), layers.MaxPool2D(pool_size=[2, 2], strides= 2, padding='same'), # unit 2 layers.Conv2D(3, kernel_size=[2, 2], strides=[2, 2], padding='same', activation = tf.nn.relu), layers.Conv2D(3, kernel_size=[2, 2], padding='same', activation = tf.nn.relu), layers.MaxPool2D(pool_size=[2, 2], strides= 2, padding='same'), ] fc_net = Sequential([ layers.Dense(150, activation = tf.nn.relu), layers.Dense(80, activation = tf.nn.relu), layers.Dense(20, activation = None), ]) conv_net = Sequential(conv_layers) conv_net.build(input_shape = [None, 38, 300, 1]) fc_net.build(input_shape = [None, 171]) optimizer = tf.keras.optimizers.Adam(1e-3) variables = conv_net.trainable_variables + fc_net.trainable_variables def main(): for epoch in range(50): for step, (sentence_x, sentence_y, label) in enumerate(train_db): with tf.GradientTape() as tape: out1 = conv_net(sentence_x) out2 = conv_net(sentence_y) fc_input_x = tf.reshape(out1, [-1, 171]) fc_input_y = tf.reshape(out2, [-1, 171]) vec_x = fc_net(fc_input_x) vec_y = fc_net(fc_input_y) #对输出的句向量进行计算相似度值 output = tf.exp(-tf.reduce_sum(tf.abs(vec_x - vec_y), axis=1)) output = tf.reshape(output, [-1]) output = tf.math.ceil(output) output1 = tf.one_hot(tf.cast(output, dtype=tf.int32), depth=2) label = tf.cast(label, dtype=tf.int32) label= tf.one_hot(label, depth=2) print("output1", output1) print("label", label) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=output1, labels=label)) #loss = tf.reduce_sum(tf.square(output1-label)) grad = tape.gradient(loss, variables) optimizer.apply_gradients(zip(grad, variables)) if step % 10 == 0: print("epoch={0}, step = {1}, loss={2}".format(epoch, step, loss)) if __name__ == '__main__': main() ``` 希望大佬们能指点一下,本人入门级小白。

keras报错:All inputs to the layer should be tensors.

深度学习小白,初次使用keras构建网络,遇到问题向各位大神请教: ``` from keras.models import Sequential from keras.layers import Embedding from keras.layers import Dense, Activation from keras.layers import Concatenate from keras.layers import Add 构建了一些嵌入层_ model_store = Embedding(1115, 10) model_dow = Embedding(7, 6) model_day = Embedding(31, 10) model_month = Embedding(12, 6) model_year = Embedding(3, 2) model_promotion = Embedding(2, 1) model_state = Embedding(12, 6) 将这些嵌入层连接起来 output_embeddings = [model_store, model_dow, model_day, model_month, model_year, model_promotion, model_state] output_model = Concatenate()(output_embeddings) ``` 运行报错: --------------------------------------------------------------------------- ValueError Traceback (most recent call last) D:\python\lib\site-packages\keras\engine\base_layer.py in assert_input_compatibility(self, inputs) 278 try: --> 279 K.is_keras_tensor(x) 280 except ValueError: D:\python\lib\site-packages\keras\backend\tensorflow_backend.py in is_keras_tensor(x) 473 raise ValueError('Unexpectedly found an instance of type `' + --> 474 str(type(x)) + '`. ' 475 'Expected a symbolic tensor instance.') ValueError: Unexpectedly found an instance of type `<class 'keras.layers.embeddings.Embedding'>`. Expected a symbolic tensor instance. During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) <ipython-input-32-8e957c4150f0> in <module> ----> 1 output_model = Concatenate()(output_embeddings) D:\python\lib\site-packages\keras\engine\base_layer.py in __call__(self, inputs, **kwargs) 412 # Raise exceptions in case the input is not compatible 413 # with the input_spec specified in the layer constructor. --> 414 self.assert_input_compatibility(inputs) 415 416 # Collect input shapes to build layer. D:\python\lib\site-packages\keras\engine\base_layer.py in assert_input_compatibility(self, inputs) 283 'Received type: ' + 284 str(type(x)) + '. Full input: ' + --> 285 str(inputs) + '. All inputs to the layer ' 286 'should be tensors.') 287 ValueError: Layer concatenate_5 was called with an input that isn't a symbolic tensor. Received type: <class 'keras.layers.embeddings.Embedding'>. Full input: [<keras.layers.embeddings.Embedding object at 0x000001C82EA1EC88>, <keras.layers.embeddings.Embedding object at 0x000001C82EA1EB38>, <keras.layers.embeddings.Embedding object at 0x000001C82EA1EB00>, <keras.layers.embeddings.Embedding object at 0x000001C82E954240>, <keras.layers.embeddings.Embedding object at 0x000001C82E954198>, <keras.layers.embeddings.Embedding object at 0x000001C82E9542E8>, <keras.layers.embeddings.Embedding object at 0x000001C82E954160>]. All inputs to the layer should be tensors. 报错提示是:所有层的输入应该为张量,请问应该怎么修改呢?麻烦了!

Python Tensorflow中dense问题

tf.layers.dense中units的参数设定依据什么规则?是维数越大越精确吗?刚刚开始学,希望能细讲下谢谢

反归一化时报错ValueError: operands could not be broadcast together with shapes

在使用scaler.inverse_transform(y_test)进行反归一化时,报错ValueError: operands could not be broadcast together with shapes (984,2) (4,)(984,2),我断调试了一下,在这个位置报错:![图片说明](https://img-ask.csdn.net/upload/202005/14/1589426709_978169.png)

Tensorflow 2.0 : When using data tensors as input to a model, you should specify the `steps_per_epoch` argument.

下面代码每次执行到epochs 中的最后一个step 都会报错,请教大牛这是什么问题呢? ``` import tensorflow_datasets as tfds dataset, info = tfds.load('imdb_reviews/subwords8k', with_info=True, as_supervised=True) train_dataset,test_dataset = dataset['train'],dataset['test'] tokenizer = info.features['text'].encoder print('vocabulary size: ', tokenizer.vocab_size) sample_string = 'Hello world, tensorflow' tokenized_string = tokenizer.encode(sample_string) print('tokened id: ', tokenized_string) src_string= tokenizer.decode(tokenized_string) print(src_string) for t in tokenized_string: print(str(t) + ': '+ tokenizer.decode([t])) BUFFER_SIZE=6400 BATCH_SIZE=64 num_train_examples = info.splits['train'].num_examples num_test_examples=info.splits['test'].num_examples print("Number of training examples: {}".format(num_train_examples)) print("Number of test examples: {}".format(num_test_examples)) train_dataset=train_dataset.shuffle(BUFFER_SIZE) train_dataset=train_dataset.padded_batch(BATCH_SIZE,train_dataset.output_shapes) test_dataset=test_dataset.padded_batch(BATCH_SIZE,test_dataset.output_shapes) def get_model(): model=tf.keras.Sequential([ tf.keras.layers.Embedding(tokenizer.vocab_size,64), tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)), tf.keras.layers.Dense(64,activation='relu'), tf.keras.layers.Dense(1,activation='sigmoid') ]) return model model =get_model() model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) import math #from tensorflow import keras #train_dataset= keras.preprocessing.sequence.pad_sequences(train_dataset, maxlen=BUFFER_SIZE) history =model.fit(train_dataset, epochs=2, steps_per_epoch=(math.ceil(BUFFER_SIZE/BATCH_SIZE) -90 ), validation_data= test_dataset) ``` Train on 10 steps Epoch 1/2 9/10 [==========================>...] - ETA: 3s - loss: 0.6955 - accuracy: 0.4479 --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-111-8ddec076c096> in <module> 6 epochs=2, 7 steps_per_epoch=(math.ceil(BUFFER_SIZE/BATCH_SIZE) -90 ), ----> 8 validation_data= test_dataset) /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow_core/python/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, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs) 726 max_queue_size=max_queue_size, 727 workers=workers, --> 728 use_multiprocessing=use_multiprocessing) 729 730 def evaluate(self, /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_arrays.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs) 672 validation_steps=validation_steps, 673 validation_freq=validation_freq, --> 674 steps_name='steps_per_epoch') 675 676 def evaluate(self, /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_arrays.py in model_iteration(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs) 437 validation_in_fit=True, 438 prepared_feed_values_from_dataset=(val_iterator is not None), --> 439 steps_name='validation_steps') 440 if not isinstance(val_results, list): 441 val_results = [val_results] /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_arrays.py in model_iteration(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs) 174 if not is_dataset: 175 num_samples_or_steps = _get_num_samples_or_steps(ins, batch_size, --> 176 steps_per_epoch) 177 else: 178 num_samples_or_steps = steps_per_epoch /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_arrays.py in _get_num_samples_or_steps(ins, batch_size, steps_per_epoch) 491 return steps_per_epoch 492 return training_utils.check_num_samples(ins, batch_size, steps_per_epoch, --> 493 'steps_per_epoch') 494 495 /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_utils.py in check_num_samples(ins, batch_size, steps, steps_name) 422 raise ValueError('If ' + steps_name + 423 ' is set, the `batch_size` must be None.') --> 424 if check_steps_argument(ins, steps, steps_name): 425 return None 426 /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_utils.py in check_steps_argument(input_data, steps, steps_name) 1199 raise ValueError('When using {input_type} as input to a model, you should' 1200 ' specify the `{steps_name}` argument.'.format( -> 1201 input_type=input_type_str, steps_name=steps_name)) 1202 return True 1203 ValueError: When using data tensors as input to a model, you should specify the `steps_per_epoch` argument.

修改的SSD—Tensorflow 版本在训练的时候遇到loss输入维度不一致

目前在学习目标检测识别的方向。 自己参考了一些论文 对原版的SSD进行了一些改动工作 前面的网络模型部分已经修改完成且不报错。 但是在进行训练操作的时候会出现 ’ValueError: Dimension 0 in both shapes must be equal, but are 233920 and 251392. Shapes are [233920] and [251392]. for 'ssd_losses/Select' (op: 'Select') with input shapes: [251392], [233920], [251392]. ‘ ‘两个形状中的尺寸0必须相等,但分别为233920和251392。形状有[233920]和[251392]。对于输入形状为[251392]、[233920]、[251392]的''ssd_losses/Select' (op: 'Select') ![图片说明](https://img-ask.csdn.net/upload/201904/06/1554539638_631515.png) ![图片说明](https://img-ask.csdn.net/upload/201904/06/1554539651_430990.png) # SSD loss function. # =========================================================================== # def ssd_losses(logits, localisations, gclasses, glocalisations, gscores, match_threshold=0.5, negative_ratio=3., alpha=1., label_smoothing=0., device='/cpu:0', scope=None): with tf.name_scope(scope, 'ssd_losses'): lshape = tfe.get_shape(logits[0], 5) num_classes = lshape[-1] batch_size = lshape[0] # Flatten out all vectors! flogits = [] fgclasses = [] fgscores = [] flocalisations = [] fglocalisations = [] for i in range(len(logits)): flogits.append(tf.reshape(logits[i], [-1, num_classes])) fgclasses.append(tf.reshape(gclasses[i], [-1])) fgscores.append(tf.reshape(gscores[i], [-1])) flocalisations.append(tf.reshape(localisations[i], [-1, 4])) fglocalisations.append(tf.reshape(glocalisations[i], [-1, 4])) # And concat the crap! logits = tf.concat(flogits, axis=0) gclasses = tf.concat(fgclasses, axis=0) gscores = tf.concat(fgscores, axis=0) localisations = tf.concat(flocalisations, axis=0) glocalisations = tf.concat(fglocalisations, axis=0) dtype = logits.dtype # Compute positive matching mask... pmask = gscores > match_threshold fpmask = tf.cast(pmask, dtype) n_positives = tf.reduce_sum(fpmask) # Hard negative mining... no_classes = tf.cast(pmask, tf.int32) predictions = slim.softmax(logits) nmask = tf.logical_and(tf.logical_not(pmask), gscores > -0.5) fnmask = tf.cast(nmask, dtype) nvalues = tf.where(nmask, predictions[:, 0], 1. - fnmask) nvalues_flat = tf.reshape(nvalues, [-1]) # Number of negative entries to select. max_neg_entries = tf.cast(tf.reduce_sum(fnmask), tf.int32) n_neg = tf.cast(negative_ratio * n_positives, tf.int32) + batch_size n_neg = tf.minimum(n_neg, max_neg_entries) val, idxes = tf.nn.top_k(-nvalues_flat, k=n_neg) max_hard_pred = -val[-1] # Final negative mask. nmask = tf.logical_and(nmask, nvalues < max_hard_pred) fnmask = tf.cast(nmask, dtype) # Add cross-entropy loss. with tf.name_scope('cross_entropy_pos'): loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=gclasses) loss = tf.div(tf.reduce_sum(loss * fpmask), batch_size, name='value') tf.losses.add_loss(loss) with tf.name_scope('cross_entropy_neg'): loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=no_classes) loss = tf.div(tf.reduce_sum(loss * fnmask), batch_size, name='value') tf.losses.add_loss(loss) # Add localization loss: smooth L1, L2, ... with tf.name_scope('localization'): # Weights Tensor: positive mask + random negative. weights = tf.expand_dims(alpha * fpmask, axis=-1) loss = custom_layers.abs_smooth(localisations - glocalisations) loss = tf.div(tf.reduce_sum(loss * weights), batch_size, name='value') tf.losses.add_loss(loss) ``` ``` 研究了一段时间的源码 (因为只是SSD-Tensorflow-Master中的ssd_vgg_300.py中定义网络结构的那部分做了修改 ,loss函数代码部分并没有进行改动)所以没所到错误所在,网上也找不到相关的解决方案。 希望大神能够帮忙解答 感激不尽~

keras.util.sequence + fit_generator 如何实现多输出model

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