# Blog

## 5000+ Members

### MEETUPS

LEARN, CONNECT, SHARE

### JOB POSTINGS

INDEED POSTINGS

Browse through the latest deep learning, ai, machine learning postings from Indeed for the GTA.

### CONTACT

CONNECT WITH US

Are you looking to sponsor space, be a speaker, or volunteer, feel free to give us a shout.

# [D] how can I get a global minimum

 here is my code ​ ``for _ in range(10): K.clear_session() model = Sequential() model.add(LSTM(256, input_shape=(None, 1))) model.add(Dropout(0.2)) model.add(Dense(256)) model.add(Dropout(0.2)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') hist = model.fit(x_train, y_train, epochs=20, batch_size=64, verbose=0) p = model.predict(x_test) print(mean_squared_error(y_test, p)) plt.plot(y_test) plt.plot(p) plt.legend(['testY', 'p'], loc='upper right') plt.show() ... plt.plot(hist.history['loss']) `` ​ `Total params` : 330,241 `samples` : 2264 ​ and below is the result ​ https://i.redd.it/2gjgr2uonig31.png I haven’t changed anything. ​ I only ran for loop. ​ As you can see in the picture, the result of the MSE is huge, even though I have just run the for loop. ​ I think the fundamental reason for this problem is that the optimizer can not find global maximum and find local maximum and converge. The reason is that after checking all the loss graphs, the loss is no longer reduced significantly. (After 20 times) So in order to solve this problem, I have to find the global minimum. How should I do this? ​ I tried adjusting the number of batch_size, epoch. Also, I tried hidden layer size, LSTM unit, kerner_initializer addition, optimizer change, etc. but could not get any meaningful result. ​ ​ I wonder how can I solve this problem. ​ Your valuable opinions and thoughts will be very much appreciated. ​ if you want to see full source here is link https://gist.github.com/Lay4U/e1fc7d036356575f4d0799cdcebed90e submitted by /u/GoBacksIn [link] [comments]