I am trying to plot the Model accuracy and Model loss learning curves after I have successfully trained my LSTM model to see the pattern of the learning curves (to see if its overfitting or underfitting) . The problem is the error "TypeError: 'tuple' object is not callable" keeps popping up. I'm a beginner when it comes to this so I'll take any advice I can get. I am using Python 3.8.8 and Numpy 1.18.1.
LSTM Model
model=Sequential()
model.add(LSTM(32, return_sequences=True, input_shape = (n_time_steps, n_features),
kernel_regularizer = l2(0.000001), bias_regularizer = l2(0.000001), name='lstm_1'))
model.add(Flatten(name='flatten'))
model.add(Dense(64, activation='relu',kernel_regularizer = l2(0.000001), bias_regularizer = l2(0.000001), name='dense_1' ))
model.add(Dense(len(np.unique(y_train)), activation='softmax',
kernel_regularizer = l2(0.000001), bias_regularizer = l2(0.000001), name='output'))
model.summary()
# Compile the model
model.compile(loss='sparse_categorical_crossentropy', optimizer = Adam(), metrics=['accuracy'])
Model Fitting:
history = model.fit(train_gen, epochs = 5, validation_data= test_gen, callbacks=callbacks)
Learning Curves:
def plot_learningCurve(history, epochs):
# Plot training & validation accuracy values
epoch_range = range(1, epochs+1)
plt.plot(epoch_range, history.history['accuracy'])
plt.plot(epoch_range, (history.history['val_accuracy']))
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Val'], loc='upper left')
plt.show()
# Plot training & validation loss values
plt.plot(epoch_range, history.history['loss'])
plt.plot(epoch_range, history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Val'], loc='upper left')
plt.show()
plot_learningCurve(history,5)
The error:
TypeError Traceback (most recent call last)
<ipython-input-66-f70f77d7b751> in <module>
----> 1 plot_learningCurve(history,5)
<ipython-input-65-ece94f9461ab> in plot_learningCurve(history, epochs)
2 # Plot training & validation accuracy values
3 epoch_range = range(1, epochs+1)
----> 4 plt.plot(epoch_range, history.history['accuracy'])
5 plt.plot(epoch_range, (history.history['val_accuracy']))
6 plt.title('Model accuracy')
TypeError: 'tuple' object is not callable
