print(minist_model.history) Loss = minist_model.history['loss']val_loss = minist_model.history['val_loss'] accuracy = minist_model.historyl'accuracy']val_accuracy = minist_model.history['val_accuracy']print(loss) def draw_loss(loss, al_loss):

To draw the loss curve, you can use the matplotlib library in Python. Here's an example code snippet:

import matplotlib.pyplot as plt

def draw_loss(loss, val_loss):
    epochs = range(1, len(loss) + 1)
    plt.plot(epochs, loss, 'bo', label='Training loss')
    plt.plot(epochs, val_loss, 'b', label='Validation loss')
    plt.title('Training and validation loss')
    plt.xlabel('Epochs')
    plt.ylabel('Loss')
    plt.legend()
    plt.show()

You can then call this function with your `loss` and `val_loss` arrays to visualize the loss curve. For example:

draw_loss(loss, val_loss)

This will show a plot with the training loss and validation loss for each epoch.