我生成图像数据的np数组，然后将其用于训练模型，但是模型的精度低于10％，这意味着我的模型没有学习。我只是想知道是否有人可以看一下我用来生成图像数据的代码和我用来训练模型的代码，并帮助我改进代码以使准确性达到80％到90％。
我尝试更改优化器和损失函数，但我所做的任何更改都无法对其进行改进。
'''此代码使用函数生成图像，然后将输出数据另存为PC上的np阵列''' ＃--编码：utf-8-- “” 创建于2019年6月20日星期四14:41:03
@author: franc
"""
import numpy as np
import matplotlib.pyplot as plt
import os
import math
#import cv2
A = 1
xctr= 16
yctr= 16
y=(np.repeat(np.arange(0,33,1),33,axis=0)).reshape(33,33)
x=np.transpose(y)
datalist=[]
testlist=[]
infolist=[]
wavelengthList = []
thetaList = []
phiList = []
ampList = []
for lamb in np.arange (8,24,1.0):
for theta in np.arange(0.0, 3.14159,.167):
kx=float((6.28318/lamb)*(math.cos(theta)))
ky=float((6.28318/lamb)*(math.sin(theta)))
for phi in np.arange(0.0,6.28318,.333):
datalist.append(A * np.cos((kx * (x-xctr))+(ky * (y-yctr)) + phi))
wavelengthList.append(lamb)
thetaList.append(theta)
phiList.append(phi)
np.save(os.path.join('C:\\Users\\franc\\Desktop\\Tdata','training data'), datalist)
np.save(os.path.join('C:\\Users\\franc\\Desktop\\Tlabel','training wavelengthList'), wavelengthList)
np.save(os.path.join('C:\\Users\\franc\\Desktop\\Tlabel','training thetaList'),thetaList )
np.save(os.path.join('C:\\Users\\franc\\Desktop\\Tlabel','training phiList'), phiList)
plt.imshow(datalist[1], cmap='gray')
plt.show()
plt.imshow(datalist[200], cmap='gray')
plt.show()
'''此代码训练模型'''
import tensorflow as tf
import numpy as np
from tensorflow.keras.callbacks import TensorBoard
import time
"""Build data and label arrays to feed neural network"""
data = np.array(np.load('C:\\Users\\franc\\Desktop\\Tdata\\training data.npy'))
wavelengthArray = np.array(np.load('C:\\Users\\franc\\Desktop\\Tlabel\\training wavelengthList.npy'))
thetaArray= np.array(np.load('C:\\Users\\franc\\Desktop\\Tlabel\\training thetaList.npy'))
phiArray= np.array(np.load('C:\\Users\\franc\\Desktop\\Tlabel\\training phiList.npy'))
NAME = "Trained-Model-{}".format(int(time.time()))
#Testing data
#testData=np.array(np.load('C:\\Users\\franc\\Desktop\\testData\\testing data.npy'))
"""Train neural network"""
inputLayer = tf.keras.layers.Input(shape=(33,33))
flatInput = tf.keras.layers.Flatten()(inputLayer)
x = tf.keras.layers.Dense(64, activation='relu')(flatInput)
x = tf.keras.layers.Dense(64, activation='relu')(x)
x = tf.keras.layers.Dense(64, activation='relu')(x)
x = tf.keras.layers.Dense(64, activation='relu')(x)
wavelengthLayer = tf.keras.layers.Dense(1, activation='linear')(x)
thetaLayer = tf.keras.layers.Dense(1, activation='linear')(x)
phiLayer = tf.keras.layers.Dense(1, activation='linear')(x)
#ampLayer = tf.keras.layers.Dense(1, activation='linear')(x)
model = tf.keras.models.Model(inputs=inputLayer, outputs=[wavelengthLayer,
thetaLayer, phiLayer])
tensorboard = TensorBoard(log_dir="logs/{}".format(NAME))
model.compile(optimizer='rmsprop', loss='mse',metrics=['accuracy'])
model.fit(data, [wavelengthArray, thetaArray, phiArray],epochs=100,batch_size=10, shuffle=True, callbacks=[tensorboard])
model.save("Wave Model.model")
'''score = model.evaluate(data, [wavelengthArray, thetaArray, phiArray], verbose=2)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
print('Test accur:', score[2])
print('Test accur:', score[3])'''
史诗1/100
5776/5776 [==============================]-3s 555us / sample-损耗：8.9422-density_4_loss：6.5498-density_5_loss ：0.5684-density_6_acc：0.00203e-00-density_5_acc：0.0097-density_6_acc：0.0016
时代2/100
5776/5776 [==============================]-2s 382us / sample-损耗：2.4699-density_4_loss：1.4102-density_5_loss ：0.1807-density_6_acc：0.00795-density_4_acc：0.0000e + 00-density_5_acc：0.0232-density_6_acc：0.0024
时代3/100
5776/5776 [==============================]-2s 332us / sample-损耗：1.8800-density_4_loss：1.0962-density_5_loss ：0.1332-density_6_acc：0.00500e + 00-density_5_acc：0.0296-density_6_acc：0.0064
时代4/100
5776/5776 [===============================]-2s 352us / sample-损耗：1.4607-density_4_loss：0.8816-density_5_loss ：0.1081-density_6_acc：0.0121-density_4_acc：0.0000e + 00-density_5_acc：0.0364-density_6_acc：0.0151
时代5/100
5776/5776 [===============================]-2s 329us / sample-损耗：1.2339-density_4_loss：0.7418-density_5_loss ：0.0964-density_6_acc：0.0959-density_6_acc：0.0223-density_4_acc：0.0000e + 00-density_5_acc：0.0369-
时代6/100
5776/5776 [===============================]-2s 332us / sample-损耗：1.0370-density_4_loss：0.6581-density_5_loss ：0.0794-density_6_acc：0.0990-density_4_acc：0.0000e + 00-density_5_acc：0.0431-density_6_acc：0.0272
时代7/100
5776/5776 [==============================]-2s 328us / sample-损耗：0.9456-density_4_loss：0.6048-density_5_loss ：0.0713-density_6_acc：0.0692-density_4_acc：0.0000e + 00-density_5_acc：0.0436-density_6_acc：0.0312
时代8/100
5776/5776 [==============================]-2s 417us / sample-损耗：0.8453-density_4_loss：0.5664-density_5_loss ：0.0611-density_6_acc：0.0237-density_6_acc：0.0370-density_4_acc：0.0000e + 00-density_5_acc：0.0462-
时代9/100
5776/5776 [===============================]-2s 415us / sample-损耗：0.7624-density_4_loss：0.5120-density_5_loss ：0.0602-density_6_acc：0.0900-density_6_acc：0.0358-density_6_acc：0.0000e + 00-density_5_acc：0.0469-
时代10/100
5776/5776 [===============================]-2s 338us / sample-损耗：0.6950-density_4_loss：0.4702-density_5_loss ：0.0510-density_6_acc：0.0741-density_6_acc：0.0414-density_4_acc：0.0000e + 00-density_5_acc：0.0495
时代11/100
5776/5776 [===============================]-2s 311us / sample-损耗：0.6295-density_4_loss：0.4350-density_5_loss ：0.0486-density_6_acc：0.0458-density_6_acc：0.0409-density_4_acc：0.0000e + 00-density_5_acc：0.0483-
时代12/100
5776/5776 [===============================]-2s 321us / sample-损耗：0.5678-density_4_loss：0.3932-density_5_loss ：0.0441-density_6_acc：0.0304-density_4_acc：0.0000e + 00-density_5_acc：0.0488-density_6_acc：0.0433
时代13/100
5776/5776 [==============================]-2s 330us / sample-损耗：0.5265-density_4_loss：0.3695-density_5_loss ：0.0404-density_6_acc：0.0165-density_4_acc：0.0000e + 00-density_5_acc：0.0504-density_6_acc：0.0433
时代14/100
5776/5776 [===============================]-2s 324us / sample-损耗：0.5042-density_4_loss：0.3581-density_5_loss ：0.0397-density_6_acc：0.0064-density_4_acc：0.0000e + 00-density_5_acc：0.0509-density_6_acc：0.0452
时代15/100
5776/5776 [==============================]-2s 327us / sample-损耗：0.4659-density_4_loss：0.3311-density_5_loss ：0.0345-density_6_acc：0.0003-density_4_acc：0.0000e + 00-density_5_acc：0.0509-density_6_acc：0.0452
时代16/100
5776/5776 [==============================]-2s 343us / sample-损耗：0.4402-density_4_loss：0.3126-density_5_loss ：0.0356-density_6_acc：0.0918-density_4_acc：0.0000e + 00-density_5_acc：0.0500-density_6_acc：0.0461
时代17/100
5776/5776 [===============================]-2s 331us / sample-损耗：0.4245-density_4_loss：0.2960-density_5_loss ：0.0342-density_6_acc：0.0941-density_4_acc：0.0000e + 00-density_5_acc：0.0512-density_6_acc：0.0476
时代18/100
5776/5776 [==============================]-2s 334us / sample-损耗：0.3903-density_4_loss：0.2805-density_5_loss ：0.0319-density_6_acc：0.0780-density_4_acc：0.0000e + 00-density_5_acc：0.0512-density_6_acc：0.0481
时代19/100
5776/5776 [===============================]-2s 331us / sample-损失：0.3696-density_4_loss：0.2774-density_5_loss ：0.0310-density_6_acc：0.0613-density_4_acc：0.0000e + 00-density_5_acc：0.0516-density_6_acc：0.0497
时代20/100
5776/5776 [===============================]-2s 425us / sample-损耗：0.3568-density_4_loss：0.2631-density_5_loss ：0.0281-density_6_acc：0.0654-density_4_acc：0.0000e + 00-density_5_acc：0.0516-density_6_acc：0.0488
时代21/100
5776/5776 [==============================]-2s 357us / sample-损耗：0.3422-density_4_loss：0.2448-density_5_loss ：0.0264-density_6_acc：0.0709-density_4_acc：0.0000e + 00-density_5_acc：0.0516-density_6_acc：0.0478