Keras神经网络

Question

我正在用python学习神经网络。这是我在keras中训练的基本神经网络模型：

from keras.datasets import imdb
import numpy as np
from keras import models
from keras import layers
import matplotlib.pyplot as plt
(train_data,train_labels),(test_data,test_labels)=imdb.load_data(num_words=10000)
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)  
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=['accuracy'])
history = model.fit(partial_x_train, partial_y_train, epochs=5, batch_size=50, validation_data=(x_val, y_val))
history_dict = history.history
history_dict.keys()
history_dict = history.history
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']
epochs = range(1, len(loss_values) + 1)
plt.plot(epochs, loss_values, 'bo', label='Training loss')
plt.plot(epochs, val_loss_values, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()

我的火车损失始终为0.69，并且没有变化。我尝试调整批次大小和时间，但没有用。