reuters.py

import numpy as np
from keras.datasets import reuters
from keras import models
from keras import layers

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

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

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

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)

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

#y_train = np.asarray(train_labels).astype('float32')
#y_test = np.asarray(test_labels).astype('float32')

one_hot_train_labels = to_one_hot(train_labels)
one_hot_test_labels = to_one_hot(test_labels)

#from keras.utils.np_utils import to_categorical
#one_hot_train_labels = to_categorical(train_labels)
#one_hot_test_labels = to_categorical(test_labels)

model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(10000,)))
model.add(layers.Dense(64, 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:]

model.fit(partial_x_train,partial_y_train,epochs=9,batch_size=512,validation_data=(x_val, y_val))

results = model.evaluate(x_test, one_hot_test_labels)
print(results)

#predictions = model.predict(x_test)
#print(np.argmax(predictions[0]))

#y_train = np.array(train_labels)
#y_test = np.array(test_labels)
#model.compile(optimizer='rmsprop',loss='sparse_categorical_crossentropy',metrics=['acc'])