news.py

print('Newswire classification')

from hack import hack

hack()


from keras.datasets import reuters

(train_data, train_labels), (test_data, test_labels) = reuters.load_data(num_words=10000)

print('train data: ' + str(len(train_data)))
print('test data: ' + str(len(test_data)))

print(train_data[10])

# -- if you want to see inside the train_data, here's an example:
# word_index = reuters.get_word_index()
# reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
# decoded_newswire = ' '.join([reverse_word_index.get(i - 3, '?') for i in train_data[0]])
# print(decoded_newswire)

import numpy as np 

def vectorize_sequences(sequences, dimension=10000):
    results = np.zeros((len(sequences), dimension))
    for i, sequence in enumerate(sequences):
      results[i, sequence] = 1.
    return results

x_train = vectorize_sequences(train_data)
x_test = vectorize_sequences(test_data)

# create a one-hot (categorical) encoding
def to_one_hot(labels, dimension=46):
    results = np.zeros((len(labels), dimension))
    for i, label in enumerate(labels):
        results[i, label] = 1.
    return results

one_hot_train_lables = to_one_hot(train_labels)
one_hot_test_labels = to_one_hot(test_labels)


# or more-simply use the pre-built keras method
from keras.utils.np_utils import to_categorical

one_hot_train_labels = to_categorical(train_labels)
one_hot_test_labels = to_categorical(test_labels)


from keras import models
from keras import layers

model = models.Sequential()

model.add(layers.Dense(46, activation='relu', input_shape=(10000,)))
model.add(layers.Dense(46, activation='relu'))
model.add(layers.Dense(46, activation='softmax'))

model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

x_val = x_train[:1000]
partial_x_train = x_train[1000:]

y_val = one_hot_train_labels[:1000]
partial_y_train = one_hot_train_labels[1000:]

history = model.fit(partial_x_train,
                    partial_y_train,
                    epochs=20,
                    batch_size=516,
                    validation_data=(x_val, y_val))

import matplotlib.pyplot as plt # pylint disable=E0401

loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(1, len(loss) + 1)

plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()

plt.show()

plt.clf()

accuracy = history.history['acc']
val_acc = history.history['val_acc']

plt.plot(epochs, accuracy, 'bo', label='Accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()

plt.show()