Keras is a high-level neural network API that runs on top of Tensorflow. It provides a simple and intuitive way to build and train neural networks. One of the most important things to do when training a neural network is to track its progress over time. This can be done by plotting the accuracy and loss of the model on the training and validation datasets.
Creating a Plot
To create a plot of accuracy and loss over time, we can use the plot_model function from the keras.utils module. This function takes a trained model as input, and it plots the model's accuracy and loss on the training and validation datasets.
The following code shows how to create a plot of accuracy and loss over time for a simple neural network:
import keras
from keras.utils import plot_model
# Define the model
model = keras.Sequential([
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10, activation='softmax')
])
# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# Train the model
model.fit(x_train, y_train, epochs=10)
# Plot the model's accuracy and loss
plot_model(model, to_file='model.png')
This code will first define a simple neural network with two hidden layers. The model will then be compiled using the Adam optimizer and the categorical crossentropy loss function. Finally, the model will be trained on the training dataset for 10 epochs.
Once the model has been trained, the plot_model function will be used to create a plot of the model's accuracy and loss on the training and validation datasets. This plot can be used to monitor the performance of the model during training, and to identify potential problems such as overfitting or underfitting.
To create a plot of accuracy and loss over time, you can use the following code:
import matplotlib.pyplot as plt
# Get the history of the model
history = model.history
# Plot the accuracy
plt.plot(history['accuracy'])
plt.title('Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
# Plot the loss
plt.plot(history['loss'])
plt.title('Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
# Show the plot
plt.show()
This code will create a plot with two lines, one for the accuracy and one for the loss. The x-axis of the plot will show the epoch number, and the y-axis will show the accuracy or loss for that epoch.
Interpreting the Plot
The plot can be used to track the progress of the model over time. As the model trains, the accuracy should increase and the loss should decrease. If the accuracy is not increasing or the loss is not decreasing, then the model may not be learning.
The plot can also be used to determine when to stop training the model. If the accuracy on the validation dataset is not increasing, then the model is likely overfitting the training dataset. In this case, you should stop training the model and evaluate it on the test dataset.
Plot the accuracy and loss curves
Finally, we can plot the accuracy and loss curves. This will allow us to visualize the performance of our model as it trains.
plt.plot(history.history['accuracy'])
plt.plot(history.history['loss'])
plt.title('Model Accuracy and Loss')
plt.xlabel('Epoch')
plt.ylabel('Accuracy/Loss')
plt.show()
The accuracy curve shows that the model's accuracy increases over time. The loss curve shows that the model's loss decreases over time. This indicates that the model is learning and improving as it trains.
