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# Visualize Performance History

## 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
import matplotlib.pyplot as plt
/* Set random seed */
np.random.seed(0)
```

```
Using TensorFlow backend.
```

## Load Movie Review Data

```
/* Set the number of features we want */
number_of_features = 10000
/* 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
```

## Train Neural Network

```
/* Train neural network */
history = network.fit(train_features, /* Features */
train_target, /* Target */
epochs=15, /* Number of epochs */
verbose=0, /* No output */
batch_size=1000, /* Number of observations per batch */
validation_data=(test_features, test_target)) /* Data for evaluation */
```

## Visualize Neural Network Performance History

Specifically, we visualize the neural network’s accuracy score on training and test sets over each epoch.

```
/* Get training and test accuracy histories */
training_accuracy = history.history['acc']
test_accuracy = history.history['val_acc']
/* Create count of the number of epochs */
epoch_count = range(1, len(training_accuracy) + 1)
/* Visualize accuracy history */
plt.plot(epoch_count, training_accuracy, 'r--')
plt.plot(epoch_count, test_accuracy, 'b-')
plt.legend(['Training Accuracy', 'Test Accuracy'])
plt.xlabel('Epoch')
plt.ylabel('Accuracy Score')
plt.show();
```

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