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y_pred = model.predict_classes(X_test)
y_pred

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print(classification_report(y_true,y_pred))

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y_pred = model.predict_classes(X_test)
y_true = np.argmax(y_test, axis = 1)
cf=confusion_matrix(y_true, y_pred)
sns.heatmap(cf, annot=True)

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model.evaluate(X_test, y_test)

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plot_learningCurve(history, 400)

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def plot_learningCurve(history, epoch):
  # Plot training & validation accuracy values
  epoch_range = range(1, epoch+1)
  plt.plot(epoch_range, history.history['acc'])
  plt.plot(epoch_range, history.history['val_acc'])
  plt.title('Model accuracy')
  plt.ylabel('Accuracy')
  plt.xlabel('Epoch')
  plt.legend(['Train', 'Val'], loc='upper left')
  plt.show()

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history = model.fit(X_train, y_train,
                    epochs=400,
                    validation_data=(X_test, y_test),
                    verbose=1,
                   )

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model.summary()

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model.compile(loss='categorical_crossentropy',
              optimizer=Adam(0.0001),
              metrics=['acc'])

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model = tf.keras.models.Sequential([
       
    tf.keras.layers.Dense(256, input_shape=(9,), activation='relu'),
    tf.keras.layers.BatchNormalization(),
    tf.keras.layers.Dropout(0.3),
    
    
    tf.keras.layers.Dense(256, activation='relu'),
    tf.keras.layers.BatchNormalization(),
    tf.keras.layers.Dropout(0.3),