Accuracy and loss graphics in Emotion Recognition

https://github.com/atulapra/Emotion-detection

I build the code above and I’m predicting emotions from faces..
An accuracy and loss graphics were shared in the "Read me" section of the code.

I don’t know how this graph was created.
I found the following link on stackoverflow how graph drawing:
Keras – Plot training, validation and test set accuracy

best answer is show :

history = model1.fit(train_x, train_y,validation_split = 0.1, epochs=50, batch_size=4)

I couldn’t figure out where will they come values for trainx trainy according to the github code.

dataset_prepare.py here separates data into train and test. We’ll take it from here I think.. but what will we take?

How to draw such loss and accuracy graphs?

Where will I add to code in emotion.py?

if I add the code where the model is predicted there is a for loop there and it will result in countless graphs.

>Solution :

Quickly looking at the repo you provided, line 98 in emotion.py is plot_model_history(model_info). The function is defined starting at line 20 in the same file:

def plot_model_history(model_history):
    """
    Plot Accuracy and Loss curves given the model_history
    """
    fig, axs = plt.subplots(1,2,figsize=(15,5))
    # summarize history for accuracy
    axs[0].plot(range(1,len(model_history.history['accuracy'])+1),model_history.history['accuracy'])
    axs[0].plot(range(1,len(model_history.history['val_accuracy'])+1),model_history.history['val_accuracy'])
    axs[0].set_title('Model Accuracy')
    axs[0].set_ylabel('Accuracy')
    axs[0].set_xlabel('Epoch')
    axs[0].set_xticks(np.arange(1,len(model_history.history['accuracy'])+1),len(model_history.history['accuracy'])/10)
    axs[0].legend(['train', 'val'], loc='best')
    # summarize history for loss
    axs[1].plot(range(1,len(model_history.history['loss'])+1),model_history.history['loss'])
    axs[1].plot(range(1,len(model_history.history['val_loss'])+1),model_history.history['val_loss'])
    axs[1].set_title('Model Loss')
    axs[1].set_ylabel('Loss')
    axs[1].set_xlabel('Epoch')
    axs[1].set_xticks(np.arange(1,len(model_history.history['loss'])+1),len(model_history.history['loss'])/10)
    axs[1].legend(['train', 'val'], loc='best')
    fig.savefig('plot.png')
    plt.show()

So training the model using python emotions.py --mode train should plot, and write the curves to plot.png.