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.
#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)
#Loading Dataset (X_train, y_train), (X_test, y_test) = mnist.load_data()
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'))
Compile neural network network.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])
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))