精确度停留在50%Kera [英] Accuracy Stuck at 50% Keras

问题描述

代码

import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential,Model
from keras.layers import Dropout, Flatten, Dense,Input
from keras import applications
from keras.preprocessing import image
from keras import backend as K
K.set_image_dim_ordering('tf')


# dimensions of our images.
img_width, img_height = 150,150

top_model_weights_path = 'bottleneck_fc_model.h5'
train_data_dir = 'Cats and Dogs Dataset/train'
validation_data_dir = 'Cats and Dogs Dataset/validation'
nb_train_samples = 20000
nb_validation_samples = 5000
epochs = 50
batch_size = 16
input_tensor = Input(shape=(150,150,3))

base_model=applications.VGG16(include_top=False, weights='imagenet',input_tensor=input_tensor)
for layer in base_model.layers:
    layer.trainable = False

top_model=Sequential()
top_model.add(Flatten(input_shape=base_model.output_shape[1:]))
top_model.add(Dense(256,activation="relu"))
top_model.add(Dropout(0.5))
top_model.add(Dense(1,activation='softmax'))
top_model.load_weights(top_model_weights_path)
model = Model(inputs=base_model.input,outputs=top_model(base_model.output))


datagen = ImageDataGenerator(rescale=1. / 255)

train_data = datagen.flow_from_directory(train_data_dir,target_size=(img_width, img_height),batch_size=batch_size,classes=['dogs', 'cats'],class_mode="binary",shuffle=False)


validation_data = datagen.flow_from_directory(validation_data_dir,target_size=(img_width, img_height),classes=['dogs', 'cats'], batch_size=batch_size,class_mode="binary",shuffle=False)


model.compile(optimizer='adam',loss='binary_crossentropy', metrics=['accuracy'])

model.fit_generator(train_data, steps_per_epoch=nb_train_samples//batch_size, epochs=epochs,validation_data=validation_data, shuffle=False,verbose=

我已经使用KERAS(使用VGG16网络学习的传输)在猫和狗数据集(https://www.kaggle.com/c/dogs-vs-cats/data)上实现了一个图像分类器。代码在没有错误的情况下运行，但是精度在大约一半的历元内停留在0.0%，并且在一半之后它增加到50%的精度。我正在使用带氢的原子。

我该如何解决这个问题？我真的不认为我对VGG16这样的数据集有偏见问题(尽管我对该领域相对较新)。

推荐答案

将输出层的激活更改为Sigmoid

发件人

top_model.add(Dense(1,activation='softmax')) 

至

top_model.add(Dense(1,activation='sigmoid'))

这篇关于精确度停留在50%Kera的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！