ニュース分類(多分類問題)python深さ学習-元の例

ニュース分類-多分類問題

ロイター通信データセットのロード

from keras.datasets import reuters
(train_data, train_labels), (test_data, test_labels) = reuters.load_data(num_words=10000)

len(train_data)

len(test_data)

train_data[10]

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]])

コードデータ

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) # 

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)

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

ネットワークの構築

モデル定義

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'))

モデルのコンパイル

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

検証セットを残す

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:]

トレーニングモデル、20回

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

トレーニング損失の描画と損失の検証

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()

トレーニング精度と検証精度の描画

plt.clf() 
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()

フィットサイクルを確認し、モデルを最初から再訓練します。

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)

新しいデータで予測結果を生成

predictions = model.predict(x_test)