Update app.py

app.py

@@ -26,14 +26,6 @@ import tempfile
 import matplotlib.pyplot as plt
 import seaborn as sns
 #------------------------------------------GRUModel-------------------------------------
-import numpy as np
-from sklearn.preprocessing import MinMaxScaler
-from sklearn.ensemble import RandomForestRegressor
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import GRU, Dense, BatchNormalization, Dropout, LeakyReLU
-from tensorflow.keras import regularizers
-from tensorflow.keras.optimizers import Adam
-from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping
 
 def GRUModel(trainX, trainy, testX=None, testy=None, epochs=1000, batch_size=64, learning_rate=0.0001, 
              l1_reg=0.001, l2_reg=0.001, dropout_rate=0.2, feature_selection=True, top_k=10):
@@ -60,31 +52,31 @@ def GRUModel(trainX, trainy, testX=None, testy=None, epochs=1000, batch_size=64,
     model.add(GRU(512, input_shape=(trainX.shape[1], trainX.shape[2]), return_sequences=False, 
                   kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
     
-    for units in [256, 128, 64, 32]:
-        model.add(Dense(256, kernel_initializer='he_normal', kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
-        model.add(BatchNormalization())
-        model.add(Dropout(dropout_rate))
-        model.add(LeakyReLU(alpha=0.1))
-
-        model.add(Dense(256, kernel_initializer='he_normal', kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
-        model.add(BatchNormalization())
-        model.add(Dropout(dropout_rate))
-        model.add(LeakyReLU(alpha=0.1))
-
-        model.add(Dense(128, kernel_initializer='he_normal', kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
-        model.add(BatchNormalization())
-        model.add(Dropout(dropout_rate))
-        model.add(LeakyReLU(alpha=0.1))
-
-        model.add(Dense(64, kernel_initializer='he_normal', kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
-        model.add(BatchNormalization())
-        model.add(Dropout(dropout_rate))
-        model.add(LeakyReLU(alpha=0.1))
-
-        model.add(Dense(32, kernel_initializer='he_normal', kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
-        model.add(BatchNormalization())
-        model.add(Dropout(dropout_rate))
-        model.add(LeakyReLU(alpha=0.1))
+    
+    model.add(Dense(512, kernel_initializer='he_normal', kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
+    model.add(BatchNormalization())
+    model.add(Dropout(dropout_rate))
+    model.add(LeakyReLU(alpha=0.1))
+
+    model.add(Dense(256, kernel_initializer='he_normal', kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
+    model.add(BatchNormalization())
+    model.add(Dropout(dropout_rate))
+    model.add(LeakyReLU(alpha=0.1))
+
+    model.add(Dense(128, kernel_initializer='he_normal', kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
+    model.add(BatchNormalization())
+    model.add(Dropout(dropout_rate))
+    model.add(LeakyReLU(alpha=0.1))
+
+    model.add(Dense(64, kernel_initializer='he_normal', kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
+    model.add(BatchNormalization())
+    model.add(Dropout(dropout_rate))
+    model.add(LeakyReLU(alpha=0.1))
+
+    model.add(Dense(32, kernel_initializer='he_normal', kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
+    model.add(BatchNormalization())
+    model.add(Dropout(dropout_rate))
+    model.add(LeakyReLU(alpha=0.1))
     
     model.add(Dense(1, activation="relu"))  # Output layer