xx

run.py

@@ -16,8 +16,8 @@ test_A = pd.read_csv("test-A/in.tsv", header=None, sep="\t")
 
 
 def preprocess_data(df_to_process, main_df=None):
-    final_df = pd.get_dummies(df_to_process, columns=[0, 1, 2, 3, 4])
-    # final_df.drop(columns=[0], inplace=True)
+    final_df = pd.get_dummies(df_to_process, columns=[1, 2, 3, 4])
+    final_df.drop(columns=[0], inplace=True)
     # numeric = [3]
     # sc = StandardScaler()
     # final_df[numeric] = sc.fit_transform(final_df[numeric])
@@ -42,10 +42,16 @@ y = train_expected[0]
 
 # Define model
 # model = Sequential()
+# model = Sequential()
+# model.add(Dense(20, input_dim=76, kernel_initializer='normal', activation='relu'))
+# model.add(Dense(1, kernel_initializer='normal'))
+# Compile model
+# model.compile(loss='mean_squared_error', optimizer='adam')
+# evaluate model with stand
 model = tensorflow.keras.models.Sequential(
     [
         tensorflow.keras.layers.Dense(
-            128, activation="relu", input_dim=97, kernel_regularizer="l2"
+            128, activation="relu", input_dim=76, kernel_regularizer="l2"
         ),
         tensorflow.keras.layers.Dropout(0.5),
         tensorflow.keras.layers.Dense(128 // 2, activation="relu", kernel_regularizer="l2"),
@@ -63,7 +69,7 @@ model = tensorflow.keras.models.Sequential(
 #model.summary() #Print model Summary
 
 model.compile(loss= "mean_squared_error" , optimizer="adam", metrics=["mean_squared_error"])
-model.fit(train_df, y, epochs=20)
+model.fit(train_df, y, epochs=100)
 
 
 # param_grid = {