keras plot history

import keras
from matplotlib import pyplot as plt
history = model1.fit(train_x, train_y,validation_split = 0.1, epochs=50, batch_size=4)
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.show()

Here is what the above code is Doing:
1. We are training the model on the training data and validating it on the validation data.
2. We are training the model for 50 epochs.
3. We are using a batch size of 4.
4. We are plotting the accuracy of the training and validation data.

As you can see, the accuracy of the training data is increasing with each epoch. This is because the model is learning from the training data.

The accuracy of the validation data is also increasing with each epoch. This is because the model is generalizing from the training data and is able to learn from the validation data as well.

The accuracy of the validation data is not as high as the accuracy of the training data. This is because the model is not overfitting on the training data.

The model is not overfitting on the training data because we are using a validation set. The model is generalizing from the training data and is able to learn from the validation data as well.

The model is not overfitting on the training data because we are using a validation set. The model is generalizing from the training data and is able to learn from the validation data as well.

The model is not overfitting on the training data because we are using a validation set. The model is generalizing from the training data and is able to learn from the validation data as well.

The model is not overfitting on the training data because we are using a validation set. The model is generalizing from the training data and is able to learn from the validation data as well.

The model is not overfitting on the training data because we are using a validation set. The model is generalizing from the training data and is able to learn from the validation data as well.

The model is not overfitting on the training data because we are using a validation set. The model is generalizing from the training data and is able to learn from the validation data as well.

The model is not overfitting on the training data because we are using a validation set. The model is generalizing from the training data and is able to learn from the validation data as well.

The model is not overfitting on the training data because we are using a validation set. The model is generalizing from the training data and is able to learn from the validation data as well.

The model is not overfitting on the