本節構建一個網路，將路透社新聞劃分為46個互斥的主題，也就是46分類
1. 載入資料集

from keras.datasets import reuters

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

將資料限定在10000個最常見出現的單詞，8982個訓練樣本和2264個測試樣本

len(train_data)

8982

len(test_data)

2246

train_data[10]

2. 將索引解碼為新聞文字

word_index = reuters.get_word_index()
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
# Note that our indices were offset by 3
# because 0, 1 and 2 are reserved indices for "padding", "start of sequence", and "unknown".
decoded_newswire = ' '.join([reverse_word_index.get(i - 3, '?') for i in train_data[0]])
 

train_labels[10]

3. 編碼資料

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

# 將訓練資料向量化
x_train = vectorize_sequences(train_data)
# 將測試資料向量化
x_test = vectorize_sequences(test_data)
 

# 將標籤向量化，將標籤轉化為one-hot
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_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)
 

4. 模型定義

from keras import models
from keras import layers

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'))

5. 編譯模型
對於這個例子，最好的損失函式是categorical_crossentropy（分類交叉熵），它用於衡量兩個概率分佈之間的距離

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

6. 留出驗證集
留出1000個樣本作為驗證集

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:]

7. 訓練模型

history = model.fit(partial_x_train, partial_y_train, epochs=20, batch_size = 512, validation_data = (x_val, y_val))

8. 繪製訓練損失和驗證損失

import matplotlib.pyplot as plt

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()

9. 繪製訓練精度和驗證精度

plt.clf()     # 清除影象
acc = history.history['acc']
val_acc = history.history['val_acc']

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

plt.show()

10. 從頭開始重新訓練一個模型
中間層有64個隱藏神經元

# 從頭開始訓練一個新的模型
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'])
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)

results

[0.981157986054119, 0.790739091745149]
這種方法可以得到79%的精度

import copy

test_labels_copy = copy.copy(test_labels)
np.random.shuffle(test_labels_copy)
float(np.sum(np.array(test_labels) == np.array(test_labels_copy))) / len(test_labels)

0.19011576135351738 完全隨機的精度約為19%

# 在新資料上生成預測結果
predictions = model.predict(x_test)
predictions[0].shape

np.sum(predictions[0])

np.argmax(predictions[0])

11. 處理標籤和損失的另一種方法

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

12. 中間層維度足夠大的重要性
最終輸出是46維的，本程式碼中間層只有4個隱藏單元，中間層的維度遠遠小於46

model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(10000,)))
model.add(layers.Dense(4, activation='relu'))
model.add(layers.Dense(46, activation='softmax'))

model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(partial_x_train, partial_y_train, epochs=20, batch_size = 128, validation_data = (x_val, y_val))

Epoch 20/20
7982/7982 [==============================] - 2s 274us/step - loss: 0.4369 - acc: 0.8779 - val_loss: 1.7934 - val_acc: 0.7160
驗證精度最大約為71%，比前面下降了8%。導致這一下降的主要原因在於，你試圖將大量資訊（這些資訊足夠回覆46個類別的分割超平面）壓縮到維度很小的中間空間
13. 實驗

1. 中間層32個

model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(10000,)))
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dense(46, activation='softmax'))

model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(partial_x_train, partial_y_train, epochs=20, batch_size = 128, validation_data = (x_val, y_val))
results = model.evaluate(x_test, one_hot_test_labels)
results

Epoch 20/20
7982/7982 [] - 2s 231us/step - loss: 0.1128 - acc: 0.9564 - val_loss: 1.1904 - val_acc: 0.7970
2246/2246 [] - 0s 157us/step
Out[29]:
[1.4285533854925303, 0.7773820125196835]
精度大約在77%

1. 中間層128個

model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(10000,)))
model.add(layers.Dense(128, activation='relu'))
model.add(layers.Dense(46, activation='softmax'))

model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(partial_x_train, partial_y_train, epochs=9, batch_size = 128, validation_data = (x_val, y_val))
results = model.evaluate(x_test, one_hot_test_labels)
results

Epoch 9/9
7982/7982 [] - 2s 237us/step - loss: 0.1593 - acc: 0.9536 - val_loss: 1.0186 - val_acc: 0.8060
2246/2246 [] - 0s 159us/step
Out[31]:
[1.126946303426211, 0.790293855743544]
精度大約在79%
嘗試了中間層128個，但是迭代20輪，準確率卻只有77%，說明迭代次數過高，出現了過擬合。