Python CNN的预测不正确
我用keras试验了德国交通标志分类。当数据集不平衡时，我获得了99%的val_精度。然后，我使用分类报告检查F1分数：Python CNN的预测不正确,python,tensorflow,keras,neural-network,conv-neural-network,Python,Tensorflow,Keras,Neural Network,Conv Neural Network,我用keras试验了德国交通标志分类。当数据集不平衡时，我获得了99%的val_精度。然后，我使用分类报告检查F1分数： print(classification_report(y_test1, y_pred_bool)) precision recall f1-score support 0 1.00 0.96 0.98 54 1 0.99 0.
print(classification_report(y_test1, y_pred_bool))
precision recall f1-score support
0 1.00 0.96 0.98 54
1 0.99 0.99 0.99 618
2 0.98 1.00 0.99 499
3 0.98 0.99 0.99 341
4 1.00 0.98 0.99 472
5 0.98 1.00 0.99 572
6 0.99 1.00 0.99 176
7 1.00 0.98 0.99 176
8 1.00 1.00 1.00 98
9 1.00 1.00 1.00 294
10 0.99 1.00 1.00 313
11 0.96 0.98 0.97 56
12 0.99 0.99 0.99 553
13 0.99 0.96 0.97 95
14 1.00 1.00 1.00 87
15 0.98 1.00 0.99 104
16 0.99 1.00 0.99 133
17 1.00 0.99 0.99 67
18 0.98 1.00 0.99 345
19 1.00 1.00 1.00 151
20 1.00 1.00 1.00 50
21 1.00 0.98 0.99 153
22 0.97 0.99 0.98 73
23 0.99 0.99 0.99 350
24 0.99 0.96 0.98 104
25 1.00 1.00 1.00 217
26 1.00 1.00 1.00 54
27 1.00 0.99 0.99 165
28 0.95 0.98 0.96 93
29 0.99 1.00 0.99 275
30 0.98 1.00 0.99 95
31 1.00 1.00 1.00 58
32 1.00 0.99 1.00 535
33 0.98 1.00 0.99 62
34 0.99 1.00 0.99 497
35 1.00 0.98 0.99 100
36 1.00 1.00 1.00 65
37 0.98 0.98 0.98 49
38 1.00 0.94 0.97 446
39 0.98 1.00 0.99 90
40 0.97 1.00 0.99 368
41 1.00 0.99 1.00 337
42 1.00 0.99 1.00 363
accuracy 0.99 9803
macro avg 0.99 0.99 0.99 9803
weighted avg 0.99 0.99 0.99 9803
model = load_model('/kaggle/working/models-07-0.9904.h5')
pred = model.predict(images1)
print(pred)
y_pred_bool = np.argmax(pred, axis = 1)
print(y_pred_bool)
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42]
array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 23, 23, 23, 23,
23, 23, 23, 23, 23, 23, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 38,
38, 38, 38, 38, 38, 38, 38, 38, 38, 39, 39, 39, 39, 39, 39, 39, 39,
39, 39, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 41, 41, 41, 41, 41,
41, 41, 41, 41, 41, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 13, 13, 13, 13,
13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15,
15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16,
16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18,
18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20,
20, 20, 20, 20, 20, 20, 20, 3, 21, 21, 21, 21, 21, 21, 21, 21, 21,
21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 24, 24, 24, 24, 24, 24,
24, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26,
26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 29,
29, 29, 29, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 31,
31, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 33,
33, 33, 33, 33, 33, 33, 33, 33, 33, 35, 35, 35, 35, 35, 35, 35, 35,
35, 35, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 37, 37, 37, 37, 37,
37, 37, 37, 37, 37])
如果您正在保存和加载模型并对同一数据集进行评估，则在保存模型之前和加载模型之后，结果不得更改 示例：我运行了一个简单的模型，并使用model.save保存，使用keras的load_模型加载。您可以从下载pima-indians-diabetes.data.csv数据集（如果此处不可用，您可以从kaggle和其他来源下载） 构建、评估和保存模型-
%tensorflow_version 1.x
# MLP for Pima Indians Dataset saved to single file
import numpy as np
from numpy import loadtxt
from keras.models import Sequential
from keras.layers import Dense
# load pima indians dataset
dataset = np.loadtxt("/content/pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# define model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Model Summary
model.summary()
# Fit the model
model.fit(X, Y, epochs=150, batch_size=10, verbose=0)
# evaluate the model
scores = model.evaluate(X, Y, verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
# save model and architecture to single file
model.save("model.h5")
print("Saved model to disk")
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 12) 108
_________________________________________________________________
dense_2 (Dense) (None, 8) 104
_________________________________________________________________
dense_3 (Dense) (None, 1) 9
=================================================================
Total params: 221
Trainable params: 221
Non-trainable params: 0
_________________________________________________________________
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
accuracy: 75.78%
Saved model to disk
# load and evaluate a saved model
from numpy import loadtxt
from keras.models import load_model
# load model
model = load_model('model.h5')
# summarize model.
model.summary()
# load dataset
dataset = loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# evaluate the model
score = model.evaluate(X, Y, verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], score[1]*100))
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 12) 108
_________________________________________________________________
dense_2 (Dense) (None, 8) 104
_________________________________________________________________
dense_3 (Dense) (None, 1) 9
=================================================================
Total params: 221
Trainable params: 221
Non-trainable params: 0
_________________________________________________________________
accuracy: 75.78%
如何对标签进行编码和解码？你在做什么分类，二进制还是多标签？我会尝试调试标签，因为您的模型似乎工作得很好打印（pred）和打印（y_pred_bool）的输出是什么？您可能忘记了像对输入文件那样预处理新图像1文件。检查预处理部分。检查规格化、文件重塑、颜色等。我的预处理是灰度图像，然后调整大小为32,32，并按1/255进行缩放。我用培训中的方法预处理了测试图像。很难从输出中识别错误。您使用的优化器、损失函数是什么？请将您的代码与数据集统计信息（类数、每个类的记录数等）一起发布。还有，您是如何保存模型的？