我使用tensorflow版本1.12.0的量化感知训练方法来训练人脸识别模型。该网络使用inception-resnet_v1(代码的来源是tensorflow/models/research/slim/nets/)。训练完成后,我得到了ckpt,然后我创建了一个新的freeze.py文件来生成eval.pb,然后使用toco成功地生成了tflite模型。但是当我最终用image测试tflite模型时,我得到了以下错误:
File "src/test_tflite.py", line 21, in <module>
Interpreter.allocate
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dropout, Dense, MaxPool2D, Conv2D, BatchNormalization, Flatten, Activation
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.keras.utils
我使用手套和CNN对文本进行分类,发现了以下问题:
File "c:\programfiles_anaconda\anaconda3\envs\math_stat_class\lib\site-packages\tensorflow\python\framework\ops.py", line 1657, in _create_c_op
raise ValueError(str(e))
ValueError: Negative dimension size caused by subtracting 5 from 1 for '{{node max_pooli
我试图在vgg19网络中添加一个密集的层,但是它给了我下面的错误。有人能帮我吗?
import tensorflow
from tensorflow.keras.applications.vgg19 import VGG19
model = VGG19()
x = tensorflow.keras.layers.Dense(10,
activation="relu",name="",trainable=True)(model.layers[-1])
model = tensorflow.keras.Model(inputs = model.layers
我试着用InceptionV3来训练我的形象。但还是有这个ValueError: Shapes (None, 9) and (None, 13, 7, 2048) are incompatible
输入图像大小: 480 x 270 (宽度x高度)输出标签:
套餐:
tensorflow-gpu 2.2.0
矮胖1.19.1
这是我的密码:
import numpy as np
from grabscreen import grab_screen
import cv2
import time
import pandas as pd
from random import
import tensorflow as tf
import tensorflow.keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
import pickle
import numpy as np
from keras.models import model_from_json
from keras.models import load_model
i
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Dense, Flatten, BatchNormalization, Conv2D, MaxPool2D, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator
impor