为什么我的 CNN 的准确率/损失在训练期间没有变化？

Question

My goal is to train a convolutional neural network to recognise the images present in the mnist sign language dataset . Here is my attempt to process the data and train the model

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Activation, Dropout, Flatten, Dense
import cv2
import keras
import sys
import tensorflow as tf
from keras import optimizers
import json

train_df = pd.read_csv("data/sign_mnist_train.csv")
test_df = pd.read_csv("data/sign_mnist_test.csv")
X = np.array(train_df.drop(["label"], axis=1))
y = np.array(train_df[["label"]])
X = X.reshape(-1, 28, 28, 1)
X = tf.cast(X, tf.float32)
model = Sequential()
model.add(Conv2D(28, (3,3), activation = 'relu'))
model.add(MaxPooling2D((2,2)))
model.add(Flatten())
model.add(Dense(24, activation = 'softmax'))
model.compile(optimizer='RMSprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])
model.fit(X, y, epochs=10, validation_split=0.2)

and after running this I get this result

Epoch 1/10
687/687 [==============================] - 4s 6ms/step - loss: 174.9729 - accuracy: 0.0438 - val_loss: 174.6281 - val_accuracy: 0.0382
Epoch 2/10
687/687 [==============================] - 2s 3ms/step - loss: 174.9779 - accuracy: 0.0433 - val_loss: 174.6281 - val_accuracy: 0.0382
Epoch 3/10
687/687 [==============================] - 2s 3ms/step - loss: 174.9777 - accuracy: 0.0433 - val_loss: 174.6281 - val_accuracy: 0.0382

and this continues for the remaining 7 epochs. My model is slightly different from what I have provided (for brevity) but this sequential model has the same issue, which makes me suspect that the issue must come before the model = Sequential() line. Furthermore, I have tried countless combinations of optimizers/loss and all those do is make the accuracy/loss converge to slightly different numbers, so I doubt that's the problem.

Answer 1

One of potential is that you use loss='binary_crossentropy' rather than loss='CategoricalCrossentropy' .

Besides, you defined the split datasets for training and testing, but you again defined it as model.fit(X, y, epochs=10, validation_split=0.2) to split datasets with 20% for validation and 80% for training.