# Neural Network Early Stopping

## Preliminaries

```
/* Load libraries */
import numpy as np
from keras.datasets import imdb
from keras.preprocessing.text import Tokenizer
from keras import models
from keras import layers
from keras.callbacks import EarlyStopping, ModelCheckpoint
/* Set random seed */
np.random.seed(0)
```

```
Using TensorFlow backend.
```

## Load Movie Review Text Data

```
/* Set the number of features we want */
number_of_features = 1000
/* Load data and target vector from movie review data */
(train_data, train_target), (test_data, test_target) = imdb.load_data(num_words=number_of_features)
/* Convert movie review data to a one-hot encoded feature matrix */
tokenizer = Tokenizer(num_words=number_of_features)
train_features = tokenizer.sequences_to_matrix(train_data, mode='binary')
test_features = tokenizer.sequences_to_matrix(test_data, mode='binary')
```

## Create Neural Network Architecture

```
/* Start neural network */
network = models.Sequential()
/* Add fully connected layer with a ReLU activation function */
network.add(layers.Dense(units=16, activation='relu', input_shape=(number_of_features,)))
/* Add fully connected layer with a ReLU activation function */
network.add(layers.Dense(units=16, activation='relu'))
/* Add fully connected layer with a sigmoid activation function */
network.add(layers.Dense(units=1, activation='sigmoid'))
```

## Compile Neural Network

```
/* Compile neural network */
network.compile(loss='binary_crossentropy', # Cross-entropy
optimizer='rmsprop', # Root Mean Square Propagation
metrics=['accuracy']) # Accuracy performance metric
```

## Setup Early Stopping

In Keras, we can implement early stopping as a callback function. Callbacks are functions that can be applied at certain stages of the training process, such as at the end of each epoch. Specifically, in our solution, we included `EarlyStopping(monitor='val_loss', patience=2)`

to define that we wanted to monitor the test (validation) loss at each epoch and after the test loss has not improved after two epochs, training is interrupted. However, since we set `patience=2`

, we won’t get the best model, but the model two epochs after the best model. Therefore, optionally, we can include a second operation, `ModelCheckpoint`

which saves the model to a file after every checkpoint (which can be useful in case a multi-day training session is interrupted for some reason. Helpful for us, if we set `save_best_only=True`

then `ModelCheckpoint`

will only save the best model.

```
*/ Set callback functions to early stop training and save the best model so far */
callbacks = [EarlyStopping(monitor='val_loss', patience=2),
ModelCheckpoint(filepath='best_model.h5', monitor='val_loss', save_best_only=True)]
```

## Train Neural Network

```
/* Train neural network */
history = network.fit(train_features, # Features
train_target, # Target vector
epochs=20, # Number of epochs
callbacks=callbacks, # Early stopping
verbose=0, # Print description after each epoch
batch_size=100, # Number of observations per batch
validation_data=(test_features, test_target)) # Data for evaluation
```

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