我的神经网络 model 准确率始终为 50%

Question

After training my model, the test accuracy is always 50%. What is wrong in my code below?

0~4000 normal signal data, 4001~6000 abnormal signal data for binary classification. data dimension is (6000, 8000)

data = np.load('data.npy') 
label = []
for i in range(len(data)): ## labeling
    if i < 4000:
        label.append(1)
    else:
        label.append(0)

label = np.array(label)

## each 100 data was extracted for test
test_data =  np.concatenate((data[:100], data[4001:4101]), axis=0)  
test_label = np.concatenate((label[:100], label[4001:4101]), axis=0)
train_data = np.concatenate((data[100:4001], data[4101:]))
train_label = np.concatenate((label[100:4001], label[4101:]))

## data shuffleing
tmp = [[x,y]for x, y in zip(train_data, train_label)]
tmp1 = [[x,y]for x, y in zip(test_data, test_label)]
random.shuffle(tmp)
random.shuffle(tmp1) 
train_data = [n[0] for n in tmp]
train_label = [n[1] for n in tmp]
train_data = np.array(train_data)
train_label = np.array(train_label)
teet_data = [n[0] for n in tmp1]
test_label = [n[1] for n in tmp1]
test_data = np.array(test_data)
test_label = np.array(test_label)

## scaling
mean = train_data.mean(axis=0)
std = train_data.std(axis=0)

train_data -= mean
train_data /= std
test_data -= mean
test_data /= std

model = models.Sequential()
model.add(layers.Dense(128, activation='relu', input_shape=(8000,)))
model.add(layers.Dense(128, activation='relu'))
model.add(layers.Dense(128, activation='relu'))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

model.compile(optimizer='Adam',
             loss='binary_crossentropy',
             metrics=['acc'])

history = model.fit(train_data,
                    train_label,
                    epochs=60,
                    batch_size=128,
                    shuffle=True,
                    validation_split=0.2)

loss curve

loss, acc = model.evaluate(test_data, test_label)

200/200 [==============================] - 0s 140us/step

print(acc)

0.5

Answer 1

It seems very likely that your model is predicting only one class for your test data.

It may be caused by your feature scaling approach. You should standardize your test data from the statistics extracted from the training set.

Answer 2

your model is too weak/small for that amount of features. just in your first layer, you are destroying all the information by converting 8000 features into 8,. use more units. a lot more than that and let it learn something instead of destroying your dataset. your model is not able to predict any better than random right now.

Answer 3

Here is my signal data.

import sounddevice as sd
import numpy as np
from math import pi

fs = 4000

n = np.arange(0, 2, 1/fs)

f = 13000 # x 
f1 = 1310 # x1
f2 = 175  # x 2
f3 = 45 # x3
'''
(8000,)
'''

x = np.sin(2*pi*f*n)
x1 = np.sin(2*pi*f1*n)
x2 = np.sin(2*pi*f2*n)
y = np.random.rand(len(x))
x3 = np.sin(2*pi*f3*n)
y = np.random.rand(len(x))

fault =  y*0.2 + (x1+x2 + x3) + 0.15
normal =  y*0.2 +(x1 + x2) +2

y = np.random.rand(len(x))
normal = normal
normal.shape
fault.shape

(8000,)

normal_data=[]
for i in range (4000):
    y = np.random.rand(len(x))
    normal = 2*y*(x1 + x2)
    normal_data.append(normal)

normal_data = np.array(normal_data)
normal_data.shape

(4000, 8000)

fault_data=[]
for i in range (2000):
    y = np.random.rand(len(x))
    fault = 2*y*(x1 + x2)
    fault_data.append(fault)

fault_data = np.array(fault_data)
fault_data.shape

(2000, 8000)

## Final signal data
data = np.concatenate((normal_data, fault_data))
data.shape

(6000, 8000)