keras LSTM poor prediction

Question

i am trying to predict the value based on some sequence (i have 5 values like 1,2,3,4,5 and want preditc the next one - 6). I am using LSTM keras for that.

creating training data:

import numpy as np 
from keras.models import Sequential
from keras.layers import LSTM,Dense
a = [float(i) for i in range(1,100)]
a = np.array(a)

data_train = a[:int(len(a)*0.9)]
data_test = a[int(len(a)*0.9):]

x = 5
y = 1
z = 0

train_x = []
train_y = []
for i in data_train:
    t = data_train[z:x]
    r = data_train[x:x+y]
    if len(r) == 0:
        break
    else:
        train_x.append(t)
        train_y.append(r)
        z = z + 1
        x = x+1

train_x = np.array(train_x)
train_y = np.array(train_y)

x = 5
y = 1
z = 0

test_x = []
test_y = []
for i in data_test:
    t = data_test[z:x]
    r = data_test[x:x+y]
    if len(r) == 0:
        break
    else:
        test_x.append(t)
        test_y.append(r)
        z = z + 1
        x = x+1

test_x = np.array(test_x)
test_y = np.array(test_y)

print(train_x.shape,train_y.shape)
print(test_x.shape,test_y.shape)

transform it into LSTM freandly shape:

train_x_1 = train_x.reshape(train_x.shape[0],len(train_x[0]),1)
train_y_1 = train_y.reshape(train_y.shape[0],1)
test_x_1 = test_x.reshape(test_x.shape[0],len(test_x[0]),1)
test_y_1 = test_y.reshape(test_y.shape[0],1)


print(train_x_1.shape, train_y_1.shape)
print(test_x_1.shape, test_y_1.shape)

build and train model:

model = Sequential()
model.add(LSTM(32,return_sequences = False,input_shape=(trein_x_1.shape[1],1)))
model.add(Dense(1))

model.compile(loss='mse',  optimizer='adam', metrics=['accuracy'])
history = model.fit(train_x_1,
                    train_y_1,
                    epochs=20,
                    shuffle=False, 
                    batch_size=1, 
                    verbose=2, 
                    validation_data=(test_x_1,test_y_1))

but I get a realy bad result, can somebody explaine me what I am doing wrong.

pred = model.predict(test_x_1)
for i,a in enumerate(pred):
    print(pred[i],test_y_1[i])
[89.71895] [95.]
[89.87877] [96.]
[90.03465] [97.]
[90.18714] [98.]
[90.337006] [99.]

Thenks.

From Review: I don't see a question here. Please edit your post and add one. — sɐunıɔןɐqɐp

sietschie sietschie · Accepted Answer · 2018-10-29T15:30:15

You expect the network to extrapolate from the data you used for training. Neural networks are not good at this. You could try to normalize your data so that you are not extrapolating anymore by, for example, using relative values instead of absolute values. That would make this example of course very trivial.

keras LSTM poor prediction

1 Answers