Update app.py

app.py

@@ -60,8 +60,31 @@ for i in range(100, data_training_arr.shape[0]):
     y_train.append(data_training_arr[i, 0])
 x_train, y_train = np.array(x_train), np.array(y_train)
 
+from keras.layers import Dense, Dropout, LSTM
+from keras.models import Sequential
+
+model = Sequential()
+model.add(LSTM(units=50, activation="linear", return_sequences=True, input_shape = (x_train.shape[1],1)))
+model.add(Dropout(0.2))
+
+model.add(LSTM(units=60, activation="linear", return_sequences=True))
+model.add(Dropout(0.3))
+
+model.add(LSTM(units=80, activation="linear", return_sequences=True))
+model.add(Dropout(0.4))
+
+model.add(LSTM(units=120, activation="linear"))
+model.add(Dropout(0.5))
+
+model.add(Dense(units=1))
+
+model.compile(optimizer="adam", loss="mean_squared_error")
+model.fit(x_train, y_train, epochs=50)
+
+model.save('keras_model1.h5')
+
 #Load the model
-model = load_model("keras_model1.h5")
+#model = load_model("keras_model1.h5")
 
 past_100_days = data_training.tail(100)
 final_test_df = past_100_days._append(data_testing, ignore_index=True)