我在keras中有一个非常简单的模型。我定义了一个函数来获取vgg19网络,然后将其与扁平层和致密层连接。当我打印模型摘要时,它没有显示vgg19网络中的每一层。有没有什么方法可以在不改变vgg19函数的情况下显示这一点?任何建议都是值得感谢的。
import keras
from keras.layers import Input, Dense, Flatten
from keras.models import Model
input = Input(shape=(32,32,3), name="main_input")
def Model_vgg19(input_sh
我正在使用Tensorflow1.14.0 1.14.0在Ubuntu虚拟机中加载一个独立的VGG19,如下所示:
VGG19 = scipy.io.loadmat(path_VGG19) #stored in my disc
VGG19_layers = VGG19['layers'][0]
然后将其传递给函数_conv2dWithRelu():
def _conv2dWithRelu(prev_layer, n_layer, layer_name,VGG19_layers):
# get weights for this layer:
weights = V
在试图导入VGG19模型时,下面的代码会生成非张量输入的错误。尽管我正在跟踪另一个代码片段。
代码:
from keras.applications.vgg19 import VGG19
import keras.backend as K
from keras.models import Model
import imageio as iio
image_shape = (384,384,3)
vgg19 = VGG19(include_top=False, weights='imagenet', input_shape=image_shape)
vgg19.trainabl
假设我已经在imagenet上预先训练过的VGG19中输入了一个图像,如下所示:
from tensorflow.keras.applications.vgg19 import VGG19
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg19 import preprocess_input
from tensorflow.keras.models import Model
import numpy as np
base_model = VGG19(weights=
在Keras中通过TimeDistributed使用预先训练好的VGG19时,我遇到以下错误:
TypeError: can only concatenate tuple (not "list") to tuple
这是在windows、Keras、python3.6中
def build_vgg(self):
img = Input(shape=(self.n_frames, self.img_rows, self.img_cols, 3))
# Get the vgg network from Keras applications
vgg = VG
import numpy as np
import pandas as pd
import os
import tensorflow as tf
import keras
from keras.applications import VGG19
from keras.models import Sequential
from keras.layers import Dense, Dropout
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import cv2
请帮助
我正在使用预先训练好的VGG19来训练模型。在训练时,我得到了92%左右的良好准确率(包括训练和验证)。 vgg19 = VGG19(input_shape=IMAGE_SIZE, weights='imagenet', include_top=False)
for layer in vgg19.layers:
layer.trainable = False
x = Flatten()(vgg19.output)
prediction = Dense(len(folders), activation='softmax')(x)
model = Mod