学习 利用Keras中的IMDB数据集，对评论进行二分类，有个疑问是：为什么预测的是正面情感？代码如下：
from keras.datasets import imdb
from keras import models
from keras import layers
import numpy as np
import matplotlib.pyplot as plt
def vectorize_sequences(sequences, dimension=10000):
results = np.zeros((len(sequences), dimension))
for i, sequence in enumerate(sequences):
results[i, sequence] = 1.
print('i=',i,'results[i]=',results[i])
return results
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
'''word_index = imdb.get_word_index()
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
decoded_review = ' '.join([reverse_word_index.get(i - 3, '?') for i in train_data[0]])
'''
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')
model = models.Sequential()
model.add(layers.Dense(16, activation='relu', input_shape=(10000,)))
model.add(layers.Dense(16,activation='relu'))
model.add(layers.Dense(1,activation='sigmoid'))
x_val = x_train[:10000]
partial_x_train = x_train[10000:]
y_val = y_train[:10000]
partial_y_train = y_train[10000:]
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
history = model.fit(partial_x_train, partial_y_train, epochs=20, batch_size=512,validation_data=(x_val, y_val))
history_dict = history.history
loss_value = history_dict['loss']
val_loss_value = history_dict['val_loss']
epochs = range(1,len(loss_value)+1)
plt.plot(epochs, loss_value, 'bo', label='Trianing Loss')
plt.plot(epochs, val_loss_value, 'b', label='Validation Loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
