Why neural network predicts wrong on its own training data?
Question:
I made a LSTM (RNN) neural network with supervised learning for data stock prediction. The problem is why it predicts wrong on its own training data? (note: reproducible example below)
I created simple model to predict next 5 days stock price:
model = Sequential()
model.add(LSTM(32, activation='sigmoid', input_shape=(x_train.shape[1], x_train.shape[2])))
model.add(Dense(y_train.shape[1]))
model.compile(optimizer='adam', loss='mse')
es = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)
model.fit(x_train, y_train, batch_size=64, epochs=25, validation_data=(x_test, y_test), callbacks=[es])
The correct results are in y_test (5 values), so model trains, looking back 90 previous days and then restore weights from best (val_loss=0.0030) result with patience=3:
Train on 396 samples, validate on 1 samples
Epoch 1/25
396/396 [==============================] - 1s 2ms/step - loss: 0.1322 - val_loss: 0.0299
Epoch 2/25
396/396 [==============================] - 0s 402us/step - loss: 0.0478 - val_loss: 0.0129
Epoch 3/25
396/396 [==============================] - 0s 397us/step - loss: 0.0385 - val_loss: 0.0178
Epoch 4/25
396/396 [==============================] - 0s 399us/step - loss: 0.0398 - val_loss: 0.0078
Epoch 5/25
396/396 [==============================] - 0s 391us/step - loss: 0.0343 - val_loss: 0.0030
Epoch 6/25
396/396 [==============================] - 0s 391us/step - loss: 0.0318 - val_loss: 0.0047
Epoch 7/25
396/396 [==============================] - 0s 389us/step - loss: 0.0308 - val_loss: 0.0043
Epoch 8/25
396/396 [==============================] - 0s 393us/step - loss: 0.0292 - val_loss: 0.0056
Prediction result is pretty awesome, isn’t it?
That’s because algorithm restored best weights from #5 epoch. Okey, let’s now save this model to .h5 file, move back -10 days and predict last 5 days (at first example we made model and validate on 17-23 April including day off weekends, now let’s test on 2-8 April). Result:
It shows absolutely wrong direction. As we see that’s because model was trained and took #5 epoch best for validation set on 17-23 April, but not on 2-8. If I try train more, playing with what epoch to choose, whatever I do, there are always a lot of time intervals in the past that have wrong prediction.
Why does model show wrong results on its own trained data? I trained data, it must remember how to predict data on this piece of set, but predicts wrong. What I also tried:
- Use large data sets with 50k+ rows, 20 years stock prices, adding more or less features
- Create different types of model, like adding more hidden layers, different batch_sizes, different layers activations, dropouts, batchnormalization
- Create custom EarlyStopping callback, get average val_loss from many validation data sets and choose the best
Maybe I miss something? What can I improve?
Here is very simple and reproducible example. yfinance downloads S&P 500 stock data.
"""python 3.7.7
tensorflow 2.1.0
keras 2.3.1"""
import numpy as np
import pandas as pd
from keras.callbacks import EarlyStopping, Callback
from keras.models import Model, Sequential, load_model
from keras.layers import Dense, Dropout, LSTM, BatchNormalization
from sklearn.preprocessing import MinMaxScaler
import plotly.graph_objects as go
import yfinance as yf
np.random.seed(4)
num_prediction = 5
look_back = 90
new_s_h5 = True # change it to False when you created model and want test on other past dates
df = yf.download(tickers="^GSPC", start='2018-05-06', end='2020-04-24', interval="1d")
data = df.filter(['Close', 'High', 'Low', 'Volume'])
# drop last N days to validate saved model on past
df.drop(df.tail(0).index, inplace=True)
print(df)
class EarlyStoppingCust(Callback):
def __init__(self, patience=0, verbose=0, validation_sets=None, restore_best_weights=False):
super(EarlyStoppingCust, self).__init__()
self.patience = patience
self.verbose = verbose
self.wait = 0
self.stopped_epoch = 0
self.restore_best_weights = restore_best_weights
self.best_weights = None
self.validation_sets = validation_sets
def on_train_begin(self, logs=None):
self.wait = 0
self.stopped_epoch = 0
self.best_avg_loss = (np.Inf, 0)
def on_epoch_end(self, epoch, logs=None):
loss_ = 0
for i, validation_set in enumerate(self.validation_sets):
predicted = self.model.predict(validation_set[0])
loss = self.model.evaluate(validation_set[0], validation_set[1], verbose = 0)
loss_ += loss
if self.verbose > 0:
print('val' + str(i + 1) + '_loss: %.5f' % loss)
avg_loss = loss_ / len(self.validation_sets)
print('avg_loss: %.5f' % avg_loss)
if self.best_avg_loss[0] > avg_loss:
self.best_avg_loss = (avg_loss, epoch + 1)
self.wait = 0
if self.restore_best_weights:
print('new best epoch = %d' % (epoch + 1))
self.best_weights = self.model.get_weights()
else:
self.wait += 1
if self.wait >= self.patience or self.params['epochs'] == epoch + 1:
self.stopped_epoch = epoch
self.model.stop_training = True
if self.restore_best_weights:
if self.verbose > 0:
print('Restoring model weights from the end of the best epoch')
self.model.set_weights(self.best_weights)
def on_train_end(self, logs=None):
print('best_avg_loss: %.5f (#%d)' % (self.best_avg_loss[0], self.best_avg_loss[1]))
def multivariate_data(dataset, target, start_index, end_index, history_size, target_size, step, single_step=False):
data = []
labels = []
start_index = start_index + history_size
if end_index is None:
end_index = len(dataset) - target_size
for i in range(start_index, end_index):
indices = range(i-history_size, i, step)
data.append(dataset[indices])
if single_step:
labels.append(target[i+target_size])
else:
labels.append(target[i:i+target_size])
return np.array(data), np.array(labels)
def transform_predicted(pr):
pr = pr.reshape(pr.shape[1], -1)
z = np.zeros((pr.shape[0], x_train.shape[2] - 1), dtype=pr.dtype)
pr = np.append(pr, z, axis=1)
pr = scaler.inverse_transform(pr)
pr = pr[:, 0]
return pr
step = 1
# creating datasets with look back
scaler = MinMaxScaler()
df_normalized = scaler.fit_transform(df.values)
dataset = df_normalized[:-num_prediction]
x_train, y_train = multivariate_data(dataset, dataset[:, 0], 0,len(dataset) - num_prediction + 1, look_back, num_prediction, step)
indices = range(len(dataset)-look_back, len(dataset), step)
x_test = np.array(dataset[indices])
x_test = np.expand_dims(x_test, axis=0)
y_test = np.expand_dims(df_normalized[-num_prediction:, 0], axis=0)
# creating past datasets to validate with EarlyStoppingCust
number_validates = 50
step_past = 5
validation_sets = [(x_test, y_test)]
for i in range(1, number_validates * step_past + 1, step_past):
indices = range(len(dataset)-look_back-i, len(dataset)-i, step)
x_t = np.array(dataset[indices])
x_t = np.expand_dims(x_t, axis=0)
y_t = np.expand_dims(df_normalized[-num_prediction-i:len(df_normalized)-i, 0], axis=0)
validation_sets.append((x_t, y_t))
if new_s_h5:
model = Sequential()
model.add(LSTM(32, return_sequences=False, activation = 'sigmoid', input_shape=(x_train.shape[1], x_train.shape[2])))
# model.add(Dropout(0.2))
# model.add(BatchNormalization())
# model.add(LSTM(units = 16))
model.add(Dense(y_train.shape[1]))
model.compile(optimizer = 'adam', loss = 'mse')
# EarlyStoppingCust is custom callback to validate each validation_sets and get average
# it takes epoch with best "best_avg" value
# es = EarlyStoppingCust(patience = 3, restore_best_weights = True, validation_sets = validation_sets, verbose = 1)
# or there is keras extension with built-in EarlyStopping, but it validates only 1 set that you pass through fit()
es = EarlyStopping(monitor = 'val_loss', patience = 3, restore_best_weights = True)
model.fit(x_train, y_train, batch_size = 64, epochs = 25, shuffle = True, validation_data = (x_test, y_test), callbacks = [es])
model.save('s.h5')
else:
model = load_model('s.h5')
predicted = model.predict(x_test)
predicted = transform_predicted(predicted)
print('predicted', predicted)
print('real', df.iloc[-num_prediction:, 0].values)
print('val_loss: %.5f' % (model.evaluate(x_test, y_test, verbose=0)))
fig = go.Figure()
fig.add_trace(go.Scatter(
x = df.index[-60:],
y = df.iloc[-60:,0],
mode='lines+markers',
name='real',
line=dict(color='#ff9800', width=1)
))
fig.add_trace(go.Scatter(
x = df.index[-num_prediction:],
y = predicted,
mode='lines+markers',
name='predict',
line=dict(color='#2196f3', width=1)
))
fig.update_layout(template='plotly_dark', hovermode='x', spikedistance=-1, hoverlabel=dict(font_size=16))
fig.update_xaxes(showspikes=True)
fig.update_yaxes(showspikes=True)
fig.show()
Answers:
Why does model show wrong results on its own trained data? I trained data, it must remember how to predict data on this piece of set, but predicts wrong.
You want the model to learn the relationship between input and output instead of memorization. If a model memorizes the correct output for each input we can say it is over fitting the training data. Often you can force the model to overfit by using a small subset of the data, so if that’s the behavior you want to see you can try that.
Basically If you want to get better result for training data, your training accuracy should be high as possible. You should use better model with respect to data you have. Basically you should check whether your training accuracy for this purpose regardless of the test accuracy. This is also called as overfitting which gives better accuracy in training data rather than test data.
Early stopping might be affect for this scenario where the best test/validation accuracy is taken rather than training accuracy.
The short answer:
Set:
batch_size = 1
epochs = 200
shuffle = False
Intuition: You are describing the priority of high accuracy in the training data. This is describing overfitting. To do that, set the batch size to 1, the epochs high, and shuffling off.
Why does model show wrong results on its own trained data? I trained data, it must
remember how to predict data on this piece of set, but predicts wrong.
Look what are you doing:
- Building a model with some layers
- Training model with the training_data
- When you trained the model, all trainable parameters get trained (i.e., weights of model got saved)
- These weights now represent the relationship between input and output.
- When you predict the same training_data again, this time trained model uses weights to get the output.
- The quality of your model now decides the predictions and hence they are different from original results even though data is the same.
It’s under-fitting and to improve that I thing you need to add neurons into your hidden layers.!!
Another point is try activation function ‘relu’. Sigmoid doesn’t give good results. Also you need to define ‘softmax’ in your output layer.!
The OP postulates an interesting finding. Let me simplify the original question as follows.
If the model is trained on a particular time series, why can’t the model reconstruct previous time series data, which it was already trained on?
Well, the answer is embedded in the training progress itself. Since EarlyStopping is used here to avoid overfitting, the best model is saved at epoch=5, where val_loss=0.0030 as mentioned by the OP. At this instance, the training loss is equal to 0.0343, that is, the RMSE of training is 0.185. Since the dataset is scaled using MinMaxScalar, we need to undo the scaling of RMSE to understand what’s going on.
The minimum and maximum values of the time sequence are found to be 2290 and 3380. Therefore, having 0.185 as the RMSE of training means that, even for the training set, the predicted values may differ from the ground truth values by approximately 0.185*(3380-2290), that is ~200 units on average.
This explains why there is a big difference when predicting the training data itself at a previous time step.
What should I do to perfectly emulate training data?
I asked this question from myself. The simple answer is, make the training loss approaching 0, that is overfit the model.
After some training, I realized that a model with only 1 LSTM layer that has 32 cells is not complex enough to reconstruct the training data. Therefore, I have added another LSTM layer as follows.
model = Sequential()
model.add(LSTM(32, return_sequences=True, activation = 'sigmoid', input_shape=(x_train.shape[1], x_train.shape[2])))
# model.add(Dropout(0.2))
# model.add(BatchNormalization())
model.add(LSTM(units = 64, return_sequences=False,))
model.add(Dense(y_train.shape[1]))
model.compile(optimizer = 'adam', loss = 'mse')
And the model is trained for 1000 epochs without considering EarlyStopping.
model.fit(x_train, y_train, batch_size = 64, epochs = 1000, shuffle = True, validation_data = (x_test, y_test))
At the end of 1000th epoch we have a training loss of 0.00047 which is much lower than the training loss in your case. So we would expect the model to reconstruct the training data better. Following is the prediction plot for Apr 2-8.
A Final Note:
Training on a particular database does not necessarily mean that the model should be able to perfectly reconstruct the training data. Especially, when the methods such as early stopping, regularization and dropout are introduced to avoid overfitting, the model tends to be more generalizable rather than memorizing training data.
Suspect #1 – Regularization
Neural networks are great at overfitting the training data, actually there is an experiment replacing CIFAR10 (image classification task) labels (y values) by random labels on the training dataset and the network fits the random labels resulting in almost zero loss.
on the left side we can see that given enough epochs random labels
gets around 0 loss – perfect score (from understanding deep learning
requires re-thinking generalization by zhang et al 2016)
So why its not happening all the time? regularization.
regularization is (roughly) trying to solve harder problem than the optimization problem (the loss) we defined for the model.
some common regularizations methods in neural networks:
- early stopping
- dropout
- batch normalization
- weight decay (e.g. l1 l2 norms)
- data augmentation
- adding random/gaussian noise
these methods help reduce overfitting and usually result in better validation and test performance, but result in lower train performance (which doesnt matter actually as explained on the last paragraph).
train data performance are usually not so important and for that we use the validation set.
Suspect #2 – Model Size
you are using single LSTM layer with 32 units. thats pretty small.
try increase the size and even put two LSTM layers (or bidirectional one) and I’m sure the model and the optimizer will overfit your data as long as you let them – i.e. remove the early stopping, restore_last_weights and any other regularization specified above.
Note on Problem Complexity
trying to predict future stock prices just by looking at the history is not an easy task, and even if the model can (over)fit perfectly the training set it will probably wont do anything useful on the test set or in real world.
ML is not black magic, the x samples needs to be correlated in some way to the y tags, we usually assume that (x,y) are drawn from some distribution together.
A more intuitive way to think about it, when you need to tag an image manually for dog/cat class – that pretty straight forward. but can you manually “tag” the stock price by looking at the history of that stock alone?
Thats some intuition on how hard this problem is.
Note on Overfitting
One should not chase higher training performance its almost useless to try overfit the training data, as we usually try to perform well with a model on new unseen data with similar properties to the train data. the all idea is to try to generalize and learn the properties of the data and correlation with the target, thats what learning is 🙂
As others have said already, you should not expect a lot from this.
Nevertheless, I found the following in your code:
-
You are re-fitting the scaler every time during training and testing. You need to save the sacler and only transform data during testing, otherwise, the results will be slightly different:
from sklearn.externals import joblib
scaler_filename = "scaler.save"
if new_s_h5:
scaler = MinMaxScaler()
df_normalized = scaler.fit_transform(df.values)
joblib.dump(scaler, scaler_filename)
else:
scaler = joblib.load(scaler_filename)
df_normalized = scaler.transform(df.values)
-
Set shuffle=False. As you do need to keep the order of your dataset.
-
Set batch_size=1. As it will be less overfitting-prone and the learning will be more noisy and the error less averaged.
-
Set epochs=50 or more.
With above-mentioned settings, the model achieved loss: 0.0037 - val_loss: 3.7329e-04.
Check the following samples of prediction:
From 17/04/2020 –> 23/04/2020:
From 02/04/2020 –> 08/04/2020:
From 25/03/2020 –> 31/03/2020:
I made a LSTM (RNN) neural network with supervised learning for data stock prediction. The problem is why it predicts wrong on its own training data? (note: reproducible example below)
I created simple model to predict next 5 days stock price:
model = Sequential()
model.add(LSTM(32, activation='sigmoid', input_shape=(x_train.shape[1], x_train.shape[2])))
model.add(Dense(y_train.shape[1]))
model.compile(optimizer='adam', loss='mse')
es = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)
model.fit(x_train, y_train, batch_size=64, epochs=25, validation_data=(x_test, y_test), callbacks=[es])
The correct results are in y_test (5 values), so model trains, looking back 90 previous days and then restore weights from best (val_loss=0.0030) result with patience=3:
Train on 396 samples, validate on 1 samples
Epoch 1/25
396/396 [==============================] - 1s 2ms/step - loss: 0.1322 - val_loss: 0.0299
Epoch 2/25
396/396 [==============================] - 0s 402us/step - loss: 0.0478 - val_loss: 0.0129
Epoch 3/25
396/396 [==============================] - 0s 397us/step - loss: 0.0385 - val_loss: 0.0178
Epoch 4/25
396/396 [==============================] - 0s 399us/step - loss: 0.0398 - val_loss: 0.0078
Epoch 5/25
396/396 [==============================] - 0s 391us/step - loss: 0.0343 - val_loss: 0.0030
Epoch 6/25
396/396 [==============================] - 0s 391us/step - loss: 0.0318 - val_loss: 0.0047
Epoch 7/25
396/396 [==============================] - 0s 389us/step - loss: 0.0308 - val_loss: 0.0043
Epoch 8/25
396/396 [==============================] - 0s 393us/step - loss: 0.0292 - val_loss: 0.0056
Prediction result is pretty awesome, isn’t it?
That’s because algorithm restored best weights from #5 epoch. Okey, let’s now save this model to .h5 file, move back -10 days and predict last 5 days (at first example we made model and validate on 17-23 April including day off weekends, now let’s test on 2-8 April). Result:
It shows absolutely wrong direction. As we see that’s because model was trained and took #5 epoch best for validation set on 17-23 April, but not on 2-8. If I try train more, playing with what epoch to choose, whatever I do, there are always a lot of time intervals in the past that have wrong prediction.
Why does model show wrong results on its own trained data? I trained data, it must remember how to predict data on this piece of set, but predicts wrong. What I also tried:
- Use large data sets with 50k+ rows, 20 years stock prices, adding more or less features
- Create different types of model, like adding more hidden layers, different batch_sizes, different layers activations, dropouts, batchnormalization
- Create custom EarlyStopping callback, get average val_loss from many validation data sets and choose the best
Maybe I miss something? What can I improve?
Here is very simple and reproducible example. yfinance downloads S&P 500 stock data.
"""python 3.7.7
tensorflow 2.1.0
keras 2.3.1"""
import numpy as np
import pandas as pd
from keras.callbacks import EarlyStopping, Callback
from keras.models import Model, Sequential, load_model
from keras.layers import Dense, Dropout, LSTM, BatchNormalization
from sklearn.preprocessing import MinMaxScaler
import plotly.graph_objects as go
import yfinance as yf
np.random.seed(4)
num_prediction = 5
look_back = 90
new_s_h5 = True # change it to False when you created model and want test on other past dates
df = yf.download(tickers="^GSPC", start='2018-05-06', end='2020-04-24', interval="1d")
data = df.filter(['Close', 'High', 'Low', 'Volume'])
# drop last N days to validate saved model on past
df.drop(df.tail(0).index, inplace=True)
print(df)
class EarlyStoppingCust(Callback):
def __init__(self, patience=0, verbose=0, validation_sets=None, restore_best_weights=False):
super(EarlyStoppingCust, self).__init__()
self.patience = patience
self.verbose = verbose
self.wait = 0
self.stopped_epoch = 0
self.restore_best_weights = restore_best_weights
self.best_weights = None
self.validation_sets = validation_sets
def on_train_begin(self, logs=None):
self.wait = 0
self.stopped_epoch = 0
self.best_avg_loss = (np.Inf, 0)
def on_epoch_end(self, epoch, logs=None):
loss_ = 0
for i, validation_set in enumerate(self.validation_sets):
predicted = self.model.predict(validation_set[0])
loss = self.model.evaluate(validation_set[0], validation_set[1], verbose = 0)
loss_ += loss
if self.verbose > 0:
print('val' + str(i + 1) + '_loss: %.5f' % loss)
avg_loss = loss_ / len(self.validation_sets)
print('avg_loss: %.5f' % avg_loss)
if self.best_avg_loss[0] > avg_loss:
self.best_avg_loss = (avg_loss, epoch + 1)
self.wait = 0
if self.restore_best_weights:
print('new best epoch = %d' % (epoch + 1))
self.best_weights = self.model.get_weights()
else:
self.wait += 1
if self.wait >= self.patience or self.params['epochs'] == epoch + 1:
self.stopped_epoch = epoch
self.model.stop_training = True
if self.restore_best_weights:
if self.verbose > 0:
print('Restoring model weights from the end of the best epoch')
self.model.set_weights(self.best_weights)
def on_train_end(self, logs=None):
print('best_avg_loss: %.5f (#%d)' % (self.best_avg_loss[0], self.best_avg_loss[1]))
def multivariate_data(dataset, target, start_index, end_index, history_size, target_size, step, single_step=False):
data = []
labels = []
start_index = start_index + history_size
if end_index is None:
end_index = len(dataset) - target_size
for i in range(start_index, end_index):
indices = range(i-history_size, i, step)
data.append(dataset[indices])
if single_step:
labels.append(target[i+target_size])
else:
labels.append(target[i:i+target_size])
return np.array(data), np.array(labels)
def transform_predicted(pr):
pr = pr.reshape(pr.shape[1], -1)
z = np.zeros((pr.shape[0], x_train.shape[2] - 1), dtype=pr.dtype)
pr = np.append(pr, z, axis=1)
pr = scaler.inverse_transform(pr)
pr = pr[:, 0]
return pr
step = 1
# creating datasets with look back
scaler = MinMaxScaler()
df_normalized = scaler.fit_transform(df.values)
dataset = df_normalized[:-num_prediction]
x_train, y_train = multivariate_data(dataset, dataset[:, 0], 0,len(dataset) - num_prediction + 1, look_back, num_prediction, step)
indices = range(len(dataset)-look_back, len(dataset), step)
x_test = np.array(dataset[indices])
x_test = np.expand_dims(x_test, axis=0)
y_test = np.expand_dims(df_normalized[-num_prediction:, 0], axis=0)
# creating past datasets to validate with EarlyStoppingCust
number_validates = 50
step_past = 5
validation_sets = [(x_test, y_test)]
for i in range(1, number_validates * step_past + 1, step_past):
indices = range(len(dataset)-look_back-i, len(dataset)-i, step)
x_t = np.array(dataset[indices])
x_t = np.expand_dims(x_t, axis=0)
y_t = np.expand_dims(df_normalized[-num_prediction-i:len(df_normalized)-i, 0], axis=0)
validation_sets.append((x_t, y_t))
if new_s_h5:
model = Sequential()
model.add(LSTM(32, return_sequences=False, activation = 'sigmoid', input_shape=(x_train.shape[1], x_train.shape[2])))
# model.add(Dropout(0.2))
# model.add(BatchNormalization())
# model.add(LSTM(units = 16))
model.add(Dense(y_train.shape[1]))
model.compile(optimizer = 'adam', loss = 'mse')
# EarlyStoppingCust is custom callback to validate each validation_sets and get average
# it takes epoch with best "best_avg" value
# es = EarlyStoppingCust(patience = 3, restore_best_weights = True, validation_sets = validation_sets, verbose = 1)
# or there is keras extension with built-in EarlyStopping, but it validates only 1 set that you pass through fit()
es = EarlyStopping(monitor = 'val_loss', patience = 3, restore_best_weights = True)
model.fit(x_train, y_train, batch_size = 64, epochs = 25, shuffle = True, validation_data = (x_test, y_test), callbacks = [es])
model.save('s.h5')
else:
model = load_model('s.h5')
predicted = model.predict(x_test)
predicted = transform_predicted(predicted)
print('predicted', predicted)
print('real', df.iloc[-num_prediction:, 0].values)
print('val_loss: %.5f' % (model.evaluate(x_test, y_test, verbose=0)))
fig = go.Figure()
fig.add_trace(go.Scatter(
x = df.index[-60:],
y = df.iloc[-60:,0],
mode='lines+markers',
name='real',
line=dict(color='#ff9800', width=1)
))
fig.add_trace(go.Scatter(
x = df.index[-num_prediction:],
y = predicted,
mode='lines+markers',
name='predict',
line=dict(color='#2196f3', width=1)
))
fig.update_layout(template='plotly_dark', hovermode='x', spikedistance=-1, hoverlabel=dict(font_size=16))
fig.update_xaxes(showspikes=True)
fig.update_yaxes(showspikes=True)
fig.show()
Why does model show wrong results on its own trained data? I trained data, it must remember how to predict data on this piece of set, but predicts wrong.
You want the model to learn the relationship between input and output instead of memorization. If a model memorizes the correct output for each input we can say it is over fitting the training data. Often you can force the model to overfit by using a small subset of the data, so if that’s the behavior you want to see you can try that.
Basically If you want to get better result for training data, your training accuracy should be high as possible. You should use better model with respect to data you have. Basically you should check whether your training accuracy for this purpose regardless of the test accuracy. This is also called as overfitting which gives better accuracy in training data rather than test data.
Early stopping might be affect for this scenario where the best test/validation accuracy is taken rather than training accuracy.
The short answer:
Set:
batch_size = 1
epochs = 200
shuffle = False
Intuition: You are describing the priority of high accuracy in the training data. This is describing overfitting. To do that, set the batch size to 1, the epochs high, and shuffling off.
Why does model show wrong results on its own trained data? I trained data, it must
remember how to predict data on this piece of set, but predicts wrong.
Look what are you doing:
- Building a model with some layers
- Training model with the training_data
- When you trained the model, all trainable parameters get trained (i.e., weights of model got saved)
- These weights now represent the relationship between input and output.
- When you predict the same training_data again, this time trained model uses weights to get the output.
- The quality of your model now decides the predictions and hence they are different from original results even though data is the same.
It’s under-fitting and to improve that I thing you need to add neurons into your hidden layers.!!
Another point is try activation function ‘relu’. Sigmoid doesn’t give good results. Also you need to define ‘softmax’ in your output layer.!
The OP postulates an interesting finding. Let me simplify the original question as follows.
If the model is trained on a particular time series, why can’t the model reconstruct previous time series data, which it was already trained on?
Well, the answer is embedded in the training progress itself. Since EarlyStopping is used here to avoid overfitting, the best model is saved at epoch=5, where val_loss=0.0030 as mentioned by the OP. At this instance, the training loss is equal to 0.0343, that is, the RMSE of training is 0.185. Since the dataset is scaled using MinMaxScalar, we need to undo the scaling of RMSE to understand what’s going on.
The minimum and maximum values of the time sequence are found to be 2290 and 3380. Therefore, having 0.185 as the RMSE of training means that, even for the training set, the predicted values may differ from the ground truth values by approximately 0.185*(3380-2290), that is ~200 units on average.
This explains why there is a big difference when predicting the training data itself at a previous time step.
What should I do to perfectly emulate training data?
I asked this question from myself. The simple answer is, make the training loss approaching 0, that is overfit the model.
After some training, I realized that a model with only 1 LSTM layer that has 32 cells is not complex enough to reconstruct the training data. Therefore, I have added another LSTM layer as follows.
model = Sequential()
model.add(LSTM(32, return_sequences=True, activation = 'sigmoid', input_shape=(x_train.shape[1], x_train.shape[2])))
# model.add(Dropout(0.2))
# model.add(BatchNormalization())
model.add(LSTM(units = 64, return_sequences=False,))
model.add(Dense(y_train.shape[1]))
model.compile(optimizer = 'adam', loss = 'mse')
And the model is trained for 1000 epochs without considering EarlyStopping.
model.fit(x_train, y_train, batch_size = 64, epochs = 1000, shuffle = True, validation_data = (x_test, y_test))
At the end of 1000th epoch we have a training loss of 0.00047 which is much lower than the training loss in your case. So we would expect the model to reconstruct the training data better. Following is the prediction plot for Apr 2-8.
A Final Note:
Training on a particular database does not necessarily mean that the model should be able to perfectly reconstruct the training data. Especially, when the methods such as early stopping, regularization and dropout are introduced to avoid overfitting, the model tends to be more generalizable rather than memorizing training data.
Suspect #1 – Regularization
Neural networks are great at overfitting the training data, actually there is an experiment replacing CIFAR10 (image classification task) labels (y values) by random labels on the training dataset and the network fits the random labels resulting in almost zero loss.
on the left side we can see that given enough epochs random labels
gets around 0 loss – perfect score (from understanding deep learning
requires re-thinking generalization by zhang et al 2016)
So why its not happening all the time? regularization.
regularization is (roughly) trying to solve harder problem than the optimization problem (the loss) we defined for the model.
some common regularizations methods in neural networks:
- early stopping
- dropout
- batch normalization
- weight decay (e.g. l1 l2 norms)
- data augmentation
- adding random/gaussian noise
these methods help reduce overfitting and usually result in better validation and test performance, but result in lower train performance (which doesnt matter actually as explained on the last paragraph).
train data performance are usually not so important and for that we use the validation set.
Suspect #2 – Model Size
you are using single LSTM layer with 32 units. thats pretty small.
try increase the size and even put two LSTM layers (or bidirectional one) and I’m sure the model and the optimizer will overfit your data as long as you let them – i.e. remove the early stopping, restore_last_weights and any other regularization specified above.
Note on Problem Complexity
trying to predict future stock prices just by looking at the history is not an easy task, and even if the model can (over)fit perfectly the training set it will probably wont do anything useful on the test set or in real world.
ML is not black magic, the x samples needs to be correlated in some way to the y tags, we usually assume that (x,y) are drawn from some distribution together.
A more intuitive way to think about it, when you need to tag an image manually for dog/cat class – that pretty straight forward. but can you manually “tag” the stock price by looking at the history of that stock alone?
Thats some intuition on how hard this problem is.
Note on Overfitting
One should not chase higher training performance its almost useless to try overfit the training data, as we usually try to perform well with a model on new unseen data with similar properties to the train data. the all idea is to try to generalize and learn the properties of the data and correlation with the target, thats what learning is 🙂
As others have said already, you should not expect a lot from this.
Nevertheless, I found the following in your code:
-
You are re-fitting the scaler every time during training and testing. You need to save the sacler and only transform data during testing, otherwise, the results will be slightly different:
from sklearn.externals import joblib scaler_filename = "scaler.save" if new_s_h5: scaler = MinMaxScaler() df_normalized = scaler.fit_transform(df.values) joblib.dump(scaler, scaler_filename) else: scaler = joblib.load(scaler_filename) df_normalized = scaler.transform(df.values) -
Set
shuffle=False. As you do need to keep the order of your dataset. -
Set
batch_size=1. As it will be less overfitting-prone and the learning will be more noisy and the error less averaged. -
Set
epochs=50or more.
With above-mentioned settings, the model achieved loss: 0.0037 - val_loss: 3.7329e-04.
Check the following samples of prediction:
From 17/04/2020 –> 23/04/2020:
From 02/04/2020 –> 08/04/2020:
From 25/03/2020 –> 31/03/2020: