CNN 預測不正確
[英]CNN prediction not correct
問題描述
我使用 keras 對德國交通標志分類進行了試驗。 當數據集不平衡時，我得到了 99% 的 val_accuracy。 然后，我使用分類報告檢查了 F1 分數：
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
然后，我檢查了混淆矩陣，這是正確的。
我將模型保存在我的磁盤上並再次加載它以使用以下代碼預測每個類的圖像：
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)
不幸的是，大多數預測都是錯誤的，我不知道為什么。 有什么建議？
編輯
它具有多標簽分類
我從每個標簽中獲取前 10 張圖像並進行預測
對應的標簽是
[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]
y_pred_bool
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])
1 個解決方案
解決方案1
0
如果您正在保存和加載模型並在同一數據集上進行評估，則在保存之前和加載模型之后結果不得更改。
示例：我運行了一個簡單的模型並使用 model.save 進行了保存，並使用 keras 的 load_model 進行了加載。 你可以從這里下載 pima-indians-diabetes.data.csv 數據集（如果這里沒有，你可以從 kaggle 和其他來源下載）。
構建、評估和保存模型 -
%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")
輸出 -
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
加載和評估模型 -
# 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))
輸出 -
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%
使用Tensorflow進行CNN預測
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