我之前已经意识到这个问题，但这些解决方案似乎都与我的问题无关。
我正在尝试使用逻辑回归来实现基本的二进制分类算法，以识别图像是猫还是狗。
我相信我正在正确地构建数据，我在初始致密层之前添加了一个展平层，我认为它正在接受正确的形状，然后我通过两个更密集的层运行它，最后一层只有2个输出（就像我一样）理解它，是这样的二元分类应该是这样的）。
请查看我的代码，并建议我可以做得更好：
1.）使预测输出变化（不总是选择一个或另一个） 2.）使我的准确性和损失在第二纪元后变化。
我尝试过： - 改变密集层的数量及其参数 - 更改数据集的大小（因此处理文件时的计数变量） - 改变时代的数量 - 将类型模型从sgd更改为adam
数据集初始化
import numpy as np
import cv2
import os
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import random
import keras
dataDir = '/content/gdrive/My Drive/AI'
categories = ['dog', 'cat']
x, y = [], []
imgSize = 100
for cat in categories:
folderPath = os.path.join(dataDir, cat) # path to the respective folders
classNum = categories.index(cat) # sets classification number (0 = dog, 1 = cat)
count = 0 # used for limiting the number of images to test
for file in os.listdir(folderPath):
count = count + 1
try:
# open image and convert to grayscale
img = cv2.imread(os.path.join(folderPath, file), cv2.IMREAD_GRAYSCALE)
# resize to a square of predefined dimensions
newImg = cv2.resize(img, (imgSize, imgSize))
# add images to x and labels to y
x.append(newImg)
y.append(classNum)
if count >= 100:
break;
# some images may be broken
except Exception as e:
pass
# y array to categorical
y = keras.utils.to_categorical(y, num_classes=2)
# shuffle data to increase training
random.shuffle(x)
random.shuffle(y)
x = np.array(x).reshape(-1, imgSize, imgSize, 1)
y = np.array(y)
# split data into default sized groups (75% train, 25% test)
xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size=0.25)
# display bar chart
objects = ('xTrain', 'xTest', 'yTrain', 'yTest')
y_pos = np.arange(len(objects))
maxItems = int((len(x) / 2 ) + 1)
arrays = [len(xTrain), len(xTest), len(yTrain), len(yTest)]
plt.bar(y_pos, arrays, align='center')
plt.xticks(y_pos, objects)
plt.ylabel('# of items')
plt.title('Items in Arrays')
plt.show()
模型设置
from keras.layers import Dense, Flatten
from keras.models import Sequential
shape = xTest.shape
model = Sequential([Flatten(),
Dense(100, activation = 'relu', input_shape = shape),
Dense(50, activation = 'relu'),
Dense(2, activation = 'softmax')])
model.compile(loss = keras.losses.binary_crossentropy,
optimizer = keras.optimizers.sgd(),
metrics = ['accuracy'])
model.fit(xTrain, yTrain,
epochs=3,
verbose=1,
validation_data=(xTest, yTest))
model.summary()
哪个输出：
Train on 150 samples, validate on 50 samples
Epoch 1/3
150/150 [==============================] - 1s 6ms/step - loss: 7.3177 - acc: 0.5400 - val_loss: 1.9236 - val_acc: 0.8800
Epoch 2/3
150/150 [==============================] - 0s 424us/step - loss: 3.4198 - acc: 0.7867 - val_loss: 1.9236 - val_acc: 0.8800
Epoch 3/3
150/150 [==============================] - 0s 430us/step - loss: 3.4198 - acc: 0.7867 - val_loss: 1.9236 - val_acc: 0.8800
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten_13 (Flatten) (None, 10000) 0
_________________________________________________________________
dense_45 (Dense) (None, 100) 1000100
_________________________________________________________________
dense_46 (Dense) (None, 50) 5050
_________________________________________________________________
dense_47 (Dense) (None, 2) 102
=================================================================
Total params: 1,005,252
Trainable params: 1,005,252
Non-trainable params: 0
预测
y_pred = model.predict(xTest)
for y in y_pred:
print(y)
哪个输出：
[1. 0.]
[1. 0.]
[1. 0.]
.
.
.
[1. 0.]
有几种方法可以给这只猫上皮...哈哈双关语。我不知道你的工作是否正常。因此，假设您的数据和标签是正确的，那么我认为这是您的数据收集和模型构建问题。
首先，我认为你没有足够的数据。大多数这些二进制分类模型都建立在> 1000张图片上。你的工作要少得多。其次，你只做了3个时代，这还不够。对于你需要的图片数量，我建议至少50个时代。但这是确定正确数字的试错，如果你过度拟合。
这就是我用来构建二进制分类模型的方法。
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Activation
from keras.optimizers import SGD
from keras.layers import Dense
from keras.utils import np_utils
import numpy as np
import cv2
data = []
labels = []
imageSize = 32
# Do whatever you gotta do to create a folder of flatten/resized images
# and another labels list with indexes that match the index of pitcure
for image in folder:
imagePath = 'path/to/image/'
imageLabel = 'whatever_label'
image = cv2.imread(imagePath)
features = cv2.resize(image, imageSize).flatten(image)
data.append(features)
labels.append(imageLabel)
# Encode the labels
labelEncoder = LabelEncoder()
labels = labelEncoder.fit_transforma(labels)
# Scale the image to [0, 1]
data = np.array(data) / 255.0
# Generate labels as [0, 1] instead of ['dog', 'cat']
labels = np_utils.to_categorical(labels, 2)
# Split data
(trainData, testData, trainLabels, testLabels) = train_test_split(data, labels, test_size = 0.25, random_state = 42)
# Construct Model
model = Sequential()
model.add(Dense(768, input_dim = imageSize * imageSize * 3, init = 'uniform', activation = 'relu'))
model.add(Dense(384, activation = 'relu', kernel_initializer = 'uniform'))
model.add(Dense(2))
model.add(Activation('softmax'))
# Compile
sgd = SGD(lr=0.01)
model.compile(loss = 'binary_crossentropy', optimizer = sgd, metrics = ['accuracy'])
model.fit(trainData, trainLabels, epochs = 50, batch_size = 128, verbose = 1)
# Determine Accuracy and loss
(loss, accuracy) = model.evaluate(testData, testLabels, batch_size = 128, verbose = 1)
print('[INFO] loss={:.4f}, accuracy: {:.4f}%'.format(loss, accuracy * 100))
希望有所帮助！