What is early stopping rounds in keras How is it used?

What is early stopping rounds in keras How is it used?

What is early stopping rounds in keras How is it used?

This recipe explains what is early stopping rounds in keras How is it used


Recipe Objective

Early stopping rounds in keras? How is it used?

When we use too many epochs it leads to overfitting, too less epochs leads to underfitting of the model.This method allows us to specify a large number of training epochs and stop training once the model performance stops improving on a hold out validation dataset.

Early stopping is basically stopping the training once you reached the minimum of your losses or errors.

Step 1- Importing Libraries

#importing Libraries from keras.datasets import mnist import numpy as np from keras import models from keras import layers from keras.callbacks import EarlyStopping, ModelCheckpoint # Set random seed np.random.seed(0)

Step 2- Load the Datasets.

#Loading Dataset (X_train, y_train), (X_test, y_test) = mnist.load_data()

Step 3- Create the Neural Network

We will create the Neural Network model here with all the required parameters

# Start neural network model = Sequential() # Add fully connected layer with a ReLU activation function model.add(layers.Dense(512, activation='relu', input_shape=(10,))) # Add fully connected layer with a ReLU activation function model.add(layers.Dense(256, activation='relu')) # Add fully connected layer with a sigmoid activation function model.add(layers.Dense(128, activation='sigmoid'))

Step 4- Compile the neural Network

Compile neural network network.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])

Step 5- Instantiate the EarlyStopping and Model Checkpoints.

callbacks = [EarlyStopping(monitor='val_loss', patience=2), ModelCheckpoint(filepath='MNIST_pred', monitor='val_loss', save_best_only=True)] print(callbacks) # Train neural network history = network.fit(X_train, y_train, epochs=20, callbacks=callbacks, verbose=0, batch_size=100, validation_data=(X_test, y_test))

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