I need to plot the training and validation graphs, and trarining and validation loss for my model.
model.compile(loss=tf.keras.losses.binary_crossentropy, optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate), metrics=['accuracy']) history = model.fit(X_train, y_train, batch_size=batch_size, epochs=no_epochs, verbose=verbosity, validation_split=validation_split) loss, accuracy = model.evaluate(X_test, y_test, verbose=1)
history object contains both accuracy and loss for both the training as well as the validation set. We can use matplotlib to plot from that.
In these plots x-axis is no_of_epochs and the y-axis is accuracy and loss value. Below is one basic implementation to achieve that, it can easily be customized according to requirements.
import matplotlib.pyplot as plt def plot_history(history): acc = history.history["accuracy"] loss = history.history["loss"] val_loss = history.history["val_loss"] val_accuracy = history.history["val_accuracy"] x = range(1, len(acc) + 1) plt.figure(figsize=(12,5)) plt.subplot(1, 2, 1) plt.plot(x, acc, "b", label="traning_acc") plt.plot(x, val_accuracy, "r", label="traning_acc") plt.title("Accuracy") plt.subplot(1, 2, 2) plt.plot(x, loss, "b", label="traning_acc") plt.plot(x, val_loss, "r", label="traning_acc") plt.title("Loss") plot_history(history)
Plot would look like below:
Answered By – arunesh