Keras 为什么我的培训损失和验证损失在减少，但培训准确度和验证准确度却没有增加？

我正在训练一个DNN模型，将图像分为两类：完美图像和不完美图像。我有60张培训图片，每堂课30张图片。对于有限的数据，我决定通过过度拟合来检查模型，即提供与训练数据相同的验证数据。在这里，我希望在训练和验证数据上都能达到100%的准确性（因为训练数据集和验证数据集是相同的）。训练损失和验证损失似乎在减少，但训练和验证的准确性都是恒定的

import tensorflow as tf
import tensorflow.keras as keras
from sklearn.preprocessing import LabelBinarizer
from sklearn.metrics import classification_report
from keras.models import Sequential
from keras.layers import Dropout
from keras.layers.core import Dense
from keras.optimizers import SGD
from keras.datasets import cifar10
import matplotlib.pyplot as plt
import numpy as np
import argparse
import cv2
import glob
initial_lr=0.001

#getting labels from Directories
right_labels=[]
wrong_labels=[]
rightimage_path=glob.glob("images/right_location/*")
wrongimage_path=glob.glob("images/wrong_location/*")
for _ in rightimage_path:
    right_labels.append(1)
#print(labels)
for _ in wrongimage_path:
    wrong_labels.append(0)

labelNames=["right_location","wrong_location"]

right_images=[]
wrong_images=[]

#getting images data from Directories
for img in rightimage_path:
    im=cv2.imread(img)
    im2=cv2.resize(im,(64,64))
    im2=np.expand_dims(im2,axis=0)
    max_pool=keras.layers.MaxPooling2D(pool_size=(2, 2),strides=(1, 1))
    output=max_pool(im2)
    output=np.squeeze(output)
    output=output.flatten()
    output=output/255
    right_images.append(output)

#wrong images
for img in wrongimage_path:
    im=cv2.imread(img)
    im2=cv2.resize(im,(64,64))
    im2=np.expand_dims(im2,axis=0)
    max_pool=keras.layers.MaxPooling2D(pool_size=(2, 2),strides=(1, 1))
    output=max_pool(im2)
    output=np.squeeze(output)
    output=output.flatten()
    output=output/255
    wrong_images.append(output)    
#print(len(wrong_images))    
trainX=right_images[:30]+wrong_images[:30]
trainX=np.array(trainX)
trainY=right_labels[:30]+wrong_labels[:30]
trainY=np.array(trainY)
#print(trainX[0].shape)

testX=trainX
testY=trainY
#testX=right_images[31:]+wrong_images[31:]
#testX=np.array(testX)
#print(len(testX))
#print(len(right_labels[31:]))
#testY=right_labels[31:]+wrong_labels[31:]
#testY=np.array(testY)
#print(testY)

print(trainY)
print(testY)

#Contruction of Neural Network model
model = Sequential()
model.add(Dense(1024, input_shape=(11907,), activation="relu"))
model.add(Dense(512, activation="relu"))
model.add(Dense(256, activation="relu"))
model.add(Dense(1, activation="softmax"))

#Training model
print("[INFO] training network...")
decay_steps = 1000
sgd = SGD(initial_lr,momentum=0.8)
lr_decayed_fn = tf.keras.experimental.CosineDecay(initial_lr, decay_steps)
model.compile(loss="binary_crossentropy", optimizer=sgd,metrics=["accuracy"])
H = model.fit(trainX, trainY, validation_data=(testX, testY),epochs=100, batch_size=1)

#evaluating the model
print("[INFO] evaluating network...")
predictions = model.predict(testX, batch_size=32)
print(predictions)
print(classification_report(testY,predictions, target_names=labelNames))

培训结果：

[INFO] training network...
Epoch 1/100
60/60 [==============================] - 3s 43ms/step - loss: 0.8908 - accuracy: 0.4867 - val_loss: 0.6719 - val_accuracy: 0.5000
Epoch 2/100
60/60 [==============================] - 2s 41ms/step - loss: 0.6893 - accuracy: 0.4791 - val_loss: 0.8592 - val_accuracy: 0.5000
Epoch 3/100
60/60 [==============================] - 2s 41ms/step - loss: 0.7008 - accuracy: 0.5290 - val_loss: 0.6129 - val_accuracy: 0.5000
Epoch 4/100
60/60 [==============================] - 2s 41ms/step - loss: 0.6971 - accuracy: 0.5279 - val_loss: 0.5619 - val_accuracy: 0.5000
Epoch 5/100
60/60 [==============================] - 2s 41ms/step - loss: 0.6770 - accuracy: 0.4745 - val_loss: 0.5669 - val_accuracy: 0.5000
Epoch 6/100
60/60 [==============================] - 2s 41ms/step - loss: 0.5685 - accuracy: 0.5139 - val_loss: 0.4953 - val_accuracy: 0.5000
Epoch 7/100
60/60 [==============================] - 2s 41ms/step - loss: 0.5679 - accuracy: 0.5312 - val_loss: 0.8273 - val_accuracy: 0.5000
Epoch 8/100
60/60 [==============================] - 2s 41ms/step - loss: 0.4373 - accuracy: 0.6591 - val_loss: 0.8112 - val_accuracy: 0.5000
Epoch 9/100
60/60 [==============================] - 2s 41ms/step - loss: 0.7427 - accuracy: 0.5848 - val_loss: 0.5419 - val_accuracy: 0.5000
Epoch 10/100
60/60 [==============================] - 2s 40ms/step - loss: 0.4719 - accuracy: 0.5377 - val_loss: 0.3118 - val_accuracy: 0.5000
Epoch 11/100
60/60 [==============================] - 2s 40ms/step - loss: 0.3253 - accuracy: 0.4684 - val_loss: 0.4851 - val_accuracy: 0.5000
Epoch 12/100
60/60 [==============================] - 3s 42ms/step - loss: 0.5194 - accuracy: 0.4514 - val_loss: 0.1976 - val_accuracy: 0.5000
Epoch 13/100
60/60 [==============================] - 2s 41ms/step - loss: 0.3114 - accuracy: 0.6019 - val_loss: 0.3483 - val_accuracy: 0.5000
Epoch 14/100
60/60 [==============================] - 2s 41ms/step - loss: 0.3794 - accuracy: 0.6003 - val_loss: 0.4723 - val_accuracy: 0.5000
Epoch 15/100
60/60 [==============================] - 2s 41ms/step - loss: 0.4172 - accuracy: 0.5873 - val_loss: 0.4992 - val_accuracy: 0.5000
Epoch 16/100
60/60 [==============================] - 2s 41ms/step - loss: 0.3110 - accuracy: 0.4338 - val_loss: 0.6209 - val_accuracy: 0.5000
Epoch 17/100
60/60 [==============================] - 2s 41ms/step - loss: 0.6362 - accuracy: 0.6615 - val_loss: 0.2337 - val_accuracy: 0.5000
Epoch 18/100
60/60 [==============================] - 3s 42ms/step - loss: 0.1652 - accuracy: 0.5617 - val_loss: 0.0841 - val_accuracy: 0.5000
Epoch 19/100
60/60 [==============================] - 3s 42ms/step - loss: 0.1050 - accuracy: 0.4714 - val_loss: 0.2853 - val_accuracy: 0.5000
Epoch 20/100
60/60 [==============================] - 2s 41ms/step - loss: 0.1031 - accuracy: 0.5254 - val_loss: 0.2085 - val_accuracy: 0.5000
Epoch 21/100
60/60 [==============================] - 2s 42ms/step - loss: 0.0375 - accuracy: 0.5124 - val_loss: 0.0564 - val_accuracy: 0.5000
Epoch 22/100
60/60 [==============================] - 2s 41ms/step - loss: 0.0298 - accuracy: 0.5482 - val_loss: 0.5937 - val_accuracy: 0.5000
Epoch 23/100
60/60 [==============================] - 2s 41ms/step - loss: 0.3126 - accuracy: 0.3884 - val_loss: 0.0527 - val_accuracy: 0.5000
Epoch 24/100
60/60 [==============================] - 2s 41ms/step - loss: 0.1054 - accuracy: 0.5572 - val_loss: 0.0356 - val_accuracy: 0.5000
Epoch 25/100
60/60 [==============================] - 3s 42ms/step - loss: 0.1067 - accuracy: 0.4170 - val_loss: 0.1262 - val_accuracy: 0.5000
Epoch 26/100
60/60 [==============================] - 2s 40ms/step - loss: 0.0551 - accuracy: 0.5608 - val_loss: 0.0255 - val_accuracy: 0.5000
Epoch 27/100
60/60 [==============================] - 2s 41ms/step - loss: 0.0188 - accuracy: 0.5816 - val_loss: 0.3153 - val_accuracy: 0.5000
Epoch 28/100
60/60 [==============================] - 2s 40ms/step - loss: 0.1106 - accuracy: 0.4583 - val_loss: 0.3419 - val_accuracy: 0.5000
Epoch 29/100
60/60 [==============================] - 2s 40ms/step - loss: 0.1493 - accuracy: 0.5334 - val_loss: 0.0351 - val_accuracy: 0.5000
Epoch 30/100
60/60 [==============================] - 2s 41ms/step - loss: 0.1099 - accuracy: 0.4537 - val_loss: 0.1217 - val_accuracy: 0.5000
Epoch 31/100
60/60 [==============================] - 3s 43ms/step - loss: 0.0893 - accuracy: 0.4828 - val_loss: 0.1276 - val_accuracy: 0.5000
Epoch 32/100
60/60 [==============================] - 3s 43ms/step - loss: 0.1806 - accuracy: 0.4265 - val_loss: 0.0157 - val_accuracy: 0.5000
Epoch 33/100
60/60 [==============================] - 3s 44ms/step - loss: 0.0154 - accuracy: 0.3411 - val_loss: 0.0152 - val_accuracy: 0.5000
Epoch 34/100
60/60 [==============================] - 3s 42ms/step - loss: 0.0088 - accuracy: 0.4385 - val_loss: 0.0075 - val_accuracy: 0.5000
Epoch 35/100
60/60 [==============================] - 2s 41ms/step - loss: 0.0068 - accuracy: 0.5450 - val_loss: 0.0045 - val_accuracy: 0.5000
Epoch 36/100
60/60 [==============================] - 2s 41ms/step - loss: 0.0051 - accuracy: 0.4283 - val_loss: 0.0039 - val_accuracy: 0.5000
Epoch 37/100
60/60 [==============================] - 2s 40ms/step - loss: 0.0026 - accuracy: 0.3970 - val_loss: 0.0035 - val_accuracy: 0.5000
Epoch 38/100
60/60 [==============================] - 2s 41ms/step - loss: 0.0037 - accuracy: 0.4758 - val_loss: 0.0030 - val_accuracy: 0.5000
Epoch 39/100
60/60 [==============================] - 2s 41ms/step - loss: 0.0021 - accuracy: 0.5036 - val_loss: 0.0025 - val_accuracy: 0.5000
Epoch 40/100
60/60 [==============================] - 2s 41ms/step - loss: 0.0028 - accuracy: 0.6088 - val_loss: 0.0022 - val_accuracy: 0.5000
Epoch 41/100
60/60 [==============================] - 2s 40ms/step - loss: 0.0023 - accuracy: 0.3521 - val_loss: 0.0020 - val_accuracy: 0.5000
Epoch 42/100
60/60 [==============================] - 2s 39ms/step - loss: 0.0023 - accuracy: 0.4832 - val_loss: 0.0020 - val_accuracy: 0.5000
Epoch 43/100
60/60 [==============================] - 2s 39ms/step - loss: 0.0019 - accuracy: 0.6031 - val_loss: 0.0019 - val_accuracy: 0.5000
Epoch 44/100
60/60 [==============================] - 2s 39ms/step - loss: 0.0014 - accuracy: 0.4757 - val_loss: 0.0017 - val_accuracy: 0.5000
Epoch 45/100
60/60 [==============================] - 2s 39ms/step - loss: 0.0012 - accuracy: 0.5074 - val_loss: 0.0016 - val_accuracy: 0.5000
Epoch 46/100
60/60 [==============================] - 2s 39ms/step - loss: 0.0019 - accuracy: 0.4907 - val_loss: 0.0014 - val_accuracy: 0.5000
Epoch 47/100
60/60 [==============================] - 2s 39ms/step - loss: 0.0013 - accuracy: 0.5113 - val_loss: 0.0013 - val_accuracy: 0.5000
Epoch 48/100
60/60 [==============================] - 2s 42ms/step - loss: 0.0013 - accuracy: 0.4616 - val_loss: 0.0012 - val_accuracy: 0.5000
Epoch 49/100
60/60 [==============================] - 3s 43ms/step - loss: 9.2667e-04 - accuracy: 0.4932 - val_loss: 0.0012 - val_accuracy: 0.5000
Epoch 50/100
60/60 [==============================] - 2s 40ms/step - loss: 0.0012 - accuracy: 0.5685 - val_loss: 0.0011 - val_accuracy: 0.5000
Epoch 51/100
60/60 [==============================] - 2s 41ms/step - loss: 0.0014 - accuracy: 0.4952 - val_loss: 0.0011 - val_accuracy: 0.5000
Epoch 52/100
60/60 [==============================] - 3s 44ms/step - loss: 9.6710e-04 - accuracy: 0.4953 - val_loss: 0.0010 - val_accuracy: 0.5000
Epoch 53/100
60/60 [==============================] - 2s 40ms/step - loss: 0.0013 - accuracy: 0.5196 - val_loss: 9.4684e-04 - val_accuracy: 0.5000
Epoch 54/100
60/60 [==============================] - 2s 39ms/step - loss: 0.0012 - accuracy: 0.6033 - val_loss: 9.0767e-04 - val_accuracy: 0.5000
Epoch 55/100
60/60 [==============================] - 2s 39ms/step - loss: 0.0011 - accuracy: 0.5339 - val_loss: 8.7093e-04 - val_accuracy: 0.5000
Epoch 56/100
60/60 [==============================] - 2s 40ms/step - loss: 7.3141e-04 - accuracy: 0.4408 - val_loss: 8.4973e-04 - val_accuracy: 0.5000
Epoch 57/100
60/60 [==============================] - 2s 40ms/step - loss: 5.9006e-04 - accuracy: 0.5258 - val_loss: 8.1935e-04 - val_accuracy: 0.5000
Epoch 58/100
60/60 [==============================] - 3s 43ms/step - loss: 7.8818e-04 - accuracy: 0.5216 - val_loss: 7.8448e-04 - val_accuracy: 0.5000
Epoch 59/100
60/60 [==============================] - 3s 42ms/step - loss: 9.2272e-04 - accuracy: 0.4472 - val_loss: 7.5098e-04 - val_accuracy: 0.5000
Epoch 60/100
60/60 [==============================] - 3s 42ms/step - loss: 0.0011 - accuracy: 0.5485 - val_loss: 7.2444e-04 - val_accuracy: 0.5000
Epoch 61/100
60/60 [==============================] - 2s 41ms/step - loss: 5.5459e-04 - accuracy: 0.4393 - val_loss: 7.1711e-04 - val_accuracy: 0.5000
Epoch 62/100
60/60 [==============================] - 3s 43ms/step - loss: 7.3943e-04 - accuracy: 0.6748 - val_loss: 7.0446e-04 - val_accuracy: 0.5000
Epoch 63/100
60/60 [==============================] - 2s 41ms/step - loss: 6.0513e-04 - accuracy: 0.4365 - val_loss: 6.5710e-04 - val_accuracy: 0.5000
Epoch 64/100
60/60 [==============================] - 3s 43ms/step - loss: 7.1400e-04 - accuracy: 0.5855 - val_loss: 6.3535e-04 - val_accuracy: 0.5000
Epoch 65/100
60/60 [==============================] - 2s 40ms/step - loss: 4.1557e-04 - accuracy: 0.4226 - val_loss: 6.1638e-04 - val_accuracy: 0.5000
Epoch 66/100
60/60 [==============================] - 2s 39ms/step - loss: 0.0010 - accuracy: 0.5130 - val_loss: 5.9961e-04 - val_accuracy: 0.5000
Epoch 67/100
60/60 [==============================] - 2s 40ms/step - loss: 4.2256e-04 - accuracy: 0.5745 - val_loss: 5.8452e-04 - val_accuracy: 0.5000
Epoch 68/100
60/60 [==============================] - 3s 44ms/step - loss: 4.6930e-04 - accuracy: 0.4256 - val_loss: 5.6929e-04 - val_accuracy: 0.5000
Epoch 69/100
60/60 [==============================] - 3s 43ms/step - loss: 5.0537e-04 - accuracy: 0.5201 - val_loss: 5.5308e-04 - val_accuracy: 0.5000
Epoch 70/100
60/60 [==============================] - 2s 40ms/step - loss: 4.2207e-04 - accuracy: 0.5162 - val_loss: 5.3811e-04 - val_accuracy: 0.5000
Epoch 71/100
60/60 [==============================] - 3s 42ms/step - loss: 4.2835e-04 - accuracy: 0.5187 - val_loss: 5.2421e-04 - val_accuracy: 0.5000
Epoch 72/100
60/60 [==============================] - 2s 41ms/step - loss: 6.9296e-04 - accuracy: 0.5396 - val_loss: 5.1115e-04 - val_accuracy: 0.5000
Epoch 73/100
60/60 [==============================] - 2s 42ms/step - loss: 6.4352e-04 - accuracy: 0.4772 - val_loss: 4.9949e-04 - val_accuracy: 0.5000
Epoch 74/100
60/60 [==============================] - 2s 41ms/step - loss: 4.0728e-04 - accuracy: 0.4406 - val_loss: 4.8785e-04 - val_accuracy: 0.5000
Epoch 75/100
60/60 [==============================] - 2s 41ms/step - loss: 6.5099e-04 - accuracy: 0.4769 - val_loss: 4.7489e-04 - val_accuracy: 0.5000
Epoch 76/100
60/60 [==============================] - 2s 40ms/step - loss: 5.3847e-04 - accuracy: 0.5610 - val_loss: 4.6401e-04 - val_accuracy: 0.5000
Epoch 77/100
60/60 [==============================] - 3s 43ms/step - loss: 3.2081e-04 - accuracy: 0.5025 - val_loss: 4.5471e-04 - val_accuracy: 0.5000
Epoch 78/100
60/60 [==============================] - 2s 41ms/step - loss: 4.1042e-04 - accuracy: 0.4055 - val_loss: 4.4509e-04 - val_accuracy: 0.5000
Epoch 79/100
60/60 [==============================] - 3s 46ms/step - loss: 4.0072e-04 - accuracy: 0.5982 - val_loss: 4.3807e-04 - val_accuracy: 0.5000
Epoch 80/100
60/60 [==============================] - 2s 40ms/step - loss: 3.6314e-04 - accuracy: 0.4305 - val_loss: 4.2492e-04 - val_accuracy: 0.5000
Epoch 81/100
60/60 [==============================] - 3s 42ms/step - loss: 4.9497e-04 - accuracy: 0.4644 - val_loss: 4.2099e-04 - val_accuracy: 0.5000
Epoch 82/100
60/60 [==============================] - 3s 42ms/step - loss: 4.3963e-04 - accuracy: 0.4163 - val_loss: 4.0970e-04 - val_accuracy: 0.5000
Epoch 83/100
60/60 [==============================] - 3s 42ms/step - loss: 2.3065e-04 - accuracy: 0.5292 - val_loss: 4.0007e-04 - val_accuracy: 0.5000
Epoch 84/100
60/60 [==============================] - 2s 40ms/step - loss: 3.6344e-04 - accuracy: 0.4781 - val_loss: 3.9164e-04 - val_accuracy: 0.5000
Epoch 85/100
60/60 [==============================] - 2s 41ms/step - loss: 3.2347e-04 - accuracy: 0.4355 - val_loss: 3.8515e-04 - val_accuracy: 0.5000

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想想一个激活softmax的神经元会产生什么……哦，现在我明白我应该使用乙状结肠激活。谢谢你的帮助