tcsenpai/goldigger
1
0
Fork 0
mirror of https://github.com/tcsenpai/goldigger.git synced 2025-06-06 18:55:21 +00:00
Code Issues Packages Projects Releases Wiki Activity

Add dropout layers to LSTM and GRU models to reduce overfitting

Browse Source 
- Implement dropout layers with 0.2 rate after each LSTM/GRU layer
- Aim to improve model generalization and prediction accuracy
- Modify create_lstm_model and create_gru_model functions
This commit is contained in:
tcsenpai 2024-09-11 00:46:28 +02:00
parent 9bb011e52e
commit 7bcc3556ad
1 changed files with 5 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
goldigger.py
View File

@ -5,7 +5,7 @@ from sklearn.model_selection import TimeSeriesSplit, cross_val_score, Randomized
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error, r2_score
from tensorflow.keras.models import Sequential, clone_model as keras_clone_model
from tensorflow.keras.layers import LSTM, Dense, GRU
from tensorflow.keras.layers import LSTM, Dense, GRU, Dropout
from tensorflow.keras.callbacks import Callback, EarlyStopping
from datetime import datetime, timedelta
from tqdm.auto import tqdm
@ -96,7 +96,9 @@ def prepare_data(data, look_back=60):
def create_lstm_model(input_shape):
model = Sequential([
LSTM(units=64, return_sequences=True, input_shape=input_shape, kernel_regularizer=l1_l2(l1=1e-5, l2=1e-4)),
Dropout(0.2), # Add dropout layer
LSTM(units=32, kernel_regularizer=l1_l2(l1=1e-5, l2=1e-4)),
Dropout(0.2), # Add dropout layer
Dense(units=16, activation='relu', kernel_regularizer=l1_l2(l1=1e-5, l2=1e-4)),
Dense(units=1)
])
@ -107,7 +109,9 @@ def create_lstm_model(input_shape):
def create_gru_model(input_shape):
model = Sequential([
GRU(units=64, return_sequences=True, input_shape=input_shape, kernel_regularizer=l1_l2(l1=1e-5, l2=1e-4)),
Dropout(0.2), # Add dropout layer
GRU(units=32, kernel_regularizer=l1_l2(l1=1e-5, l2=1e-4)),
Dropout(0.2), # Add dropout layer
Dense(units=16, activation='relu', kernel_regularizer=l1_l2(l1=1e-5, l2=1e-4)),
Dense(units=1)
])