# Feedforward Neural Network For Multiclass Classification

## Preliminaries

```
/* Load libraries */
import numpy as np
from keras.datasets import reuters
from keras.utils.np_utils import to_categorical
from keras.preprocessing.text import Tokenizer
from keras import models
from keras import layers
/* Set random seed */
np.random.seed(0)
```

```
Using TensorFlow backend.
```

## Load Movie Review Data

```
/* Set the number of features we want */
number_of_features = 5000
/* Load feature and target data */
(train_data, train_target_vector), (test_data, test_target_vector) = reuters.load_data(num_words=number_of_features)
/* Convert feature 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')
/* One-hot encode target vector to create a target matrix */
train_target = to_categorical(train_target_vector)
test_target = to_categorical(test_target_vector)
```

## Construct Neural Network Architecture

In this example we use a loss function suited to multi-class classification, the categorical cross-entropy loss function, `categorical_crossentropy`

.

```
/* Start neural network */
network = models.Sequential()
/* Add fully connected layer with a ReLU activation function */
network.add(layers.Dense(units=100, activation='relu', input_shape=(number_of_features,)))
/* Add fully connected layer with a ReLU activation function */
network.add(layers.Dense(units=100, activation='relu'))
/* Add fully connected layer with a softmax activation function */
network.add(layers.Dense(units=46, activation='softmax'))
```

## Compile Feedforward Neural Network

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

## Train Feedforward Neural Network

```
# Train neural network
history = network.fit(train_features, # Features
train_target, # Target vector
epochs=3, # Three epochs
verbose=0, # No output
batch_size=100, # Number of observations per batch
validation_data=(test_features, test_target)) # Data to use for evaluation
```

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