我正在用CNN对苹果进行分类。我在火车数据上取得了很高的准确性，但在测试数据上的准确性很低。数据分割为80:20。我不确定我的数据是否过拟合。
我有两个文件夹，包含TraningData和TestData，每个文件夹有4子文件夹braeburn, red_apple, red_delicious, rotten(包含相应的图片)。
TRAIN_DIR = 'apple_fruit'
TEST_DIR = 'apple_fruit'
classes = ['braeburn','red_apples','red_delicious','rotten'] train_datagen = ImageDataGenerator(rescale = 1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest')
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory(TRAIN_DIR,
shuffle=True,
target_size = (100,100),
batch_size = 25,
classes =['braeburn','red_apples','red_delicious','rotten'])
test_set= test_datagen.flow_from_directory(TEST_DIR,
target_size = (100, 100),
shuffle=True,
batch_size = 25,classes = classes)
model =Sequential()
model.add(Conv2D(filters=128, kernel_size=(3,3),input_shape=(100,100,3), activation='relu', padding
= 'same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(filters=16, kernel_size=(3,3), activation='relu', padding = 'same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.6))
model.add(Dense(4,activation='softmax'))
model.compile(optimizer ='adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
history = model.fit(x=training_set,#y=training_set.labels,
steps_per_epoch=len(training_set),
epochs =10)
model.save('Ripe2_model6.h5') # creates a HDF5 file 'my_model.h5'
model_path = "Ripe2_model6.h5"
loaded_model = keras.models.load_model(model_path)
classes = ['braeburn','red_apples','red_delicious','rotten']
predictions = model.predict(x=test_set, steps=len(test_set), verbose=True)
pred = np.round(predictions)
y_true=test_set.classes
y_pred=np.argmax(pred, axis=-1)
> cm = confusion_matrix(y_true=test_set.classes, y_pred=np.argmax(pred, axis=-1))
test_set.classes
np.argmax(pred, axis=-1)
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
accuracy = np.trace(cm) / float(np.sum(cm))
misclass = 1 - accuracy
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title,color = 'white')
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45,color = 'white')
plt.yticks(tick_marks, classes,color = 'white')
target_names = ['braeburn','red_apples','red_delicious','rotten']
if target_names is not None:
tick_marks = np.arange(len(target_names))
plt.xticks(tick_marks, target_names, rotation=45)
plt.yticks(tick_marks, target_names)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
thresh = cm.max() / 1.5 if normalize else cm.max() / 2
for i, j in itertools.product(range(cm.shape[0]),
range(cm.shape[1])):
if normalize:
plt.text(j, i, "{:0.4f}".format(cm[i, j]),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
else:
plt.text(j, i, "{:,}".format(cm[i, j]),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label',color = 'white')
plt.xlabel('Predicted label',color = 'white')
cm_plot_labels = ['braeburn','red_apples','red_delicious','rotten']
plot_confusion_matrix(cm=cm, classes=cm_plot_labels, title='Confusion Matrix')
print(accuracy_score(y_true, y_pred))
print(recall_score(y_true, y_pred, average=None))
print(precision_score(y_true, y_pred, average=None))
混淆矩阵:
- 精度- 0.2909090909090909
- 召回- [0.23484848 0.32319392 0.15151515 0.36213992]
- precision - [0.23308271 0.32319392 0.15151515 0.36363636]
我已经尝试更改了许多功能，但仍然没有进展。
这表明测试集中的数据与模型学到的数据有很大的不同。要了解它是过拟合还是一个不幸的分割:
- 检查您的结果是否依赖于初始训练/测试分割． 要实现这一点，您可以:
- [可选]将所有图片合并到整个数据集(train+test)文件夹。
- 将图像分割成随机训练/测试(而不是使用初始分割)
- 实施交叉验证(例如K-Fold)
- 你们有足够数量的样品吗？ 尝试添加更多的样本并检查它如何影响性能。您还可以应用数据增强技术。
如果训练数据的精度较好，但测试数据的精度较低，则模型倾向于过拟合。原因可能是一个简单的数据集，其中模型试图捕获包括噪声在内的所有数据点。在上述情况下，尝试调优参数并设置更高的批处理，执行交叉验证以了解性能并执行数据增强。