Python Conv2d Tensorflow结果错误-精度=0.0000e+；00

我正在使用tensorflow和keras进行分类，建立一个分类模型。当运行下面的代码时，输出似乎在每个历元之后都不会收敛，损失稳步增加，精度持续设置为0.0000e+00。我是机器学习新手，不太清楚为什么会发生这种情况

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.models import Sequential
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
import numpy as np

import time
import tensorflow as tf

from google.colab import drive

drive.mount('/content/drive')
import pandas as pd 
data = pd.read_csv("hmnist_28_28_RGB.csv") 
X = data.iloc[:, 0:-1]
y = data.iloc[:, -1]

X = X / 255.0
X = X.values.reshape(-1,28,28,3)
print(X.shape)

model = Sequential()
model.add(Conv2D(256, (3, 3), input_shape=X.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))


model.add(Conv2D(256, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors

model.add(Dense(64))

model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

model.fit(X, y, batch_size=32, epochs=10, validation_split=0.3)

输出

(378, 28, 28, 3)
Epoch 1/10
9/9 [==============================] - 4s 429ms/step - loss: -34.6735 - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00
Epoch 2/10
9/9 [==============================] - 4s 400ms/step - loss: -1074.2162 - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00
Epoch 3/10
9/9 [==============================] - 4s 399ms/step - loss: -7446.1872 - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00
Epoch 4/10
9/9 [==============================] - 4s 396ms/step - loss: -30012.9553 - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00
Epoch 5/10
9/9 [==============================] - 4s 406ms/step - loss: -89006.4180 - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00
Epoch 6/10
9/9 [==============================] - 4s 400ms/step - loss: -221087.9078 - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00
Epoch 7/10
9/9 [==============================] - 4s 399ms/step - loss: -480032.9313 - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00
Epoch 8/10
9/9 [==============================] - 4s 403ms/step - loss: -956052.3375 - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00
Epoch 9/10
9/9 [==============================] - 4s 396ms/step - loss: -1733128.9000 - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00
Epoch 10/10
9/9 [==============================] - 4s 401ms/step - loss: -2953626.5750 - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00

---

您需要对模型进行一些更改才能使其正常工作

数据集中有7个不同的标签，因此最后一层需要7个输出神经元

对于最后一层，您当前正在使用

sigmoid

激活。这不适用于多类分类。相反，您应该使用

softmax

激活

作为损失函数，您使用的是

loss='binary\u crossentropy'

。这仅用于二进制分类。在您的例子中，由于标签由整数组成，因此应该使用

loss='sparse\u categorical\u crossentropy'

。你可以找到更多的信息

对代码的最后几行进行以下更改：

model.add(Dense(7))
model.add(Activation('softmax'))
model.compile(loss='sparse_categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

model.fit(X, y, batch_size=32, epochs=10, validation_split=0.3)

您将获得以下培训历史记录：

(10015, 28, 28, 3)
Epoch 1/10
220/220 [==============================] - 89s 403ms/step - loss: 1.0345 - accuracy: 0.6193 - val_loss: 1.7980 - val_accuracy: 0.4353
Epoch 2/10
220/220 [==============================] - 88s 398ms/step - loss: 0.8282 - accuracy: 0.6851 - val_loss: 3.3646 - val_accuracy: 0.0676
Epoch 3/10
220/220 [==============================] - 88s 399ms/step - loss: 0.6944 - accuracy: 0.7502 - val_loss: 2.9686 - val_accuracy: 0.1228
Epoch 4/10
220/220 [==============================] - 87s 395ms/step - loss: 0.6630 - accuracy: 0.7611 - val_loss: 3.3777 - val_accuracy: 0.0646
Epoch 5/10
220/220 [==============================] - 87s 396ms/step - loss: 0.5976 - accuracy: 0.7812 - val_loss: 2.3929 - val_accuracy: 0.2532
Epoch 6/10
220/220 [==============================] - 87s 396ms/step - loss: 0.5577 - accuracy: 0.7935 - val_loss: 2.9879 - val_accuracy: 0.2592
Epoch 7/10
220/220 [==============================] - 88s 398ms/step - loss: 0.7644 - accuracy: 0.7215 - val_loss: 2.5258 - val_accuracy: 0.2852
Epoch 8/10
220/220 [==============================] - 87s 395ms/step - loss: 0.5629 - accuracy: 0.7879 - val_loss: 2.6053 - val_accuracy: 0.3055
Epoch 9/10
220/220 [==============================] - 89s 404ms/step - loss: 0.5380 - accuracy: 0.8008 - val_loss: 2.7401 - val_accuracy: 0.1694
Epoch 10/10
220/220 [==============================] - 92s 419ms/step - loss: 0.5296 - accuracy: 0.8065 - val_loss: 3.7208 - val_accuracy: 0.0529

该模型仍需优化以获得更好的结果，但总体而言，它是可行的

---

我正在使用它进行培训。

您从哪里获得文件hmnist_28_28_RGB.csv？你能给我源代码吗，这样我就可以自己运行代码了？我在上找到该文件，但希望确保它与您正在使用的文件相同。是的，数据集位于Kaggle上。使用

y=data.iloc[：，-1]

可以从文件的最后一列获取标签。不幸的是，所有标签（在我上面发布的源文件中）的值都是2.0。你能证实这一点吗？最初，大多数数据都标记为2.0，但也有其他0-6的标签。非常感谢，你知道我是否也应该删除某些特定类别的数据，因为数据是不平衡的？这取决于你的目标是什么。既然您刚刚开始学习ML和DL，我想说您应该尝试一下。尝试并积累经验是正确理解ML/DL的最佳方式。