From 00d4d179271db8e0baedaec372c18da88436cc3c Mon Sep 17 00:00:00 2001
From: amilashanaka <dsa.amilashanaka@gmail.com>
Date: Thu, 5 Jan 2023 01:41:06 +0000
Subject: [PATCH] Update model
---
api/run.py | 32 ++++++++++----------------------
1 file changed, 10 insertions(+), 22 deletions(-)
diff --git a/api/run.py b/api/run.py
index 7efe0bb..eaad0e6 100644
--- a/api/run.py
+++ b/api/run.py
@@ -57,9 +57,13 @@ model = Sequential()
feature_length = 100
testing_records=50
batch_size = 5
-epochs = 100
+epochs = 10
accuracy=0
execute_time=0
+layer_1_units=100
+layer_2_units=50
+layer_3_units=50
+dense_units=1
# Scaler
scaler = MinMaxScaler()
@@ -254,18 +258,18 @@ def setup():
#Add First LSTM Layer
- model.add(LSTM(units = 100, activation = 'relu', input_shape = (time_steps, features), return_sequences=True))
+ model.add(LSTM(units = layer_1_units, activation = 'relu', input_shape = (time_steps, features), return_sequences=True))
# Adding the Second hidden layer and the LSTM layer
- model.add(LSTM(units = 50, activation = 'relu', input_shape = (time_steps, features), return_sequences=True))
+ model.add(LSTM(units = layer_2_units, activation = 'relu', input_shape = (time_steps, features), return_sequences=True))
# Adding the Third hidden layer and the LSTM layer
- model.add(LSTM(units = 50, activation = 'relu', return_sequences=False ))
+ model.add(LSTM(units = layer_3_units, activation = 'relu', return_sequences=False ))
# Adding the output layer
- model.add(Dense(units = 1))
+ model.add(Dense(units = dense_units))
# Compiling model
model.compile(optimizer = 'adam', loss = 'mean_squared_error')
@@ -302,29 +306,13 @@ def setup():
# Generating predictions on full data
TrainPredictions=scaler.inverse_transform(model.predict(x_train))
TestPredictions=scaler.inverse_transform(model.predict(x_test))
-
FullDataPredictions=np.append(TrainPredictions, TestPredictions)
- FullDataOrig=data[time_steps:]
-
- # plotting the full data
- plt.plot(FullDataPredictions, color = 'blue', label = 'Predicted Price')
- plt.plot(FullDataOrig , color = 'lightblue', label = 'Original Price')
-
- plt.title('Stock Price Predictions')
- plt.xlabel('Trading Date')
- plt.ylabel('Stock Price')
- plt.legend()
- fig=plt.gcf()
- fig.set_figwidth(20)
- fig.set_figheight(8)
- plt.show()
+ # Save data to result data set
result["Date"]=data_set.iloc[time_steps:]["Date"]
result["Predictions"]=FullDataPredictions
result["OrderDemand"]=data[time_steps:]
-
- print(data_set.tail(10))
# ====================================================================
# main function call
--
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