add graph

backend/IT18218640/train.py

@@ -2,12 +2,13 @@ import json
 import numpy as np
 import tensorflow.keras as keras
 from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
 
 DATA_PATH = "data.json"
 SAVE_MODEL_PATH = "model.h5"
 
 LEARNING_RATE = 0.0001
-EPOCHS = 40
+EPOCHS = 60
 BATCH_SIZE = 32
 
 NUM_KEYWORDS = 10
@@ -77,6 +78,22 @@ def build_model(input_shape, learning_rate, error="sparse_categorical_crossentro
 
     return model
 
+def show_graph(history):
+
+    plt.plot(history.history['loss'], 'r', label='train loss')
+    plt.plot(history.history['val_loss'], 'g', label='validation loss')
+    plt.legend()
+    plt.xlabel('epochs')
+    plt.ylabel('Loss')
+    plt.show()
+
+    plt.subplot(1,2,1)
+    plt.ylabel('Accuracy')
+    plt.xlabel('Epochs')
+    plt.plot(history.history['accuracy'], label='Training Accuracy')
+    plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
+    plt.legend()
+    plt.show()
 
 def main():
 
@@ -88,7 +105,7 @@ def main():
     model = build_model(input_shape, LEARNING_RATE)
 
     # train the model
-    model.fit(X_train, y_train, epochs=EPOCHS, batch_size=BATCH_SIZE, validation_data=(X_validation, y_validation))
+    history = model.fit(X_train, y_train, epochs=EPOCHS, batch_size=BATCH_SIZE, validation_data=(X_validation, y_validation))
 
     # evaluate the model
     test_error, test_accuracy = model.evaluate(X_test, y_test)
@@ -97,5 +114,7 @@ def main():
     # save the model
     model.save(SAVE_MODEL_PATH)
 
+    show_graph(history)
+
 if __name__ == "__main__":
     main()
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