本次demo主题是使用keras对IMDB影评进行文本分类：

import tensorflow as tf
from tensorflow import keras
import numpy as np

print(tf.__version__)

imdb = keras.datasets.imdb

(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
print("Training entries: {}, labels: {}".format(len(train_data), len(train_labels)))
print(train_data[0])
len(train_data[0]), len(train_data[1])

# A dictionary mapping words to an integer index
word_index = imdb.get_word_index()

# The first indices are reserved
word_index = {k:(v+3) for k,v in word_index.items()} 
word_index["<PAD>"] = 0
word_index["<START>"] = 1
word_index["<UNK>"] = 2  # unknown
word_index["<UNUSED>"] = 3

reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])

#把数字序列转化为相应的字符串
def decode_review(text):
    return ' '.join([reverse_word_index.get(i, '?') for i in text])

#显示其中一个评价
decode_review(train_data[0])

#pad填充使其长度一样
train_data = keras.preprocessing.sequence.pad_sequences(train_data,
                                                        value=word_index["<PAD>"],
                                                        padding='post',
                                                        maxlen=256)

test_data = keras.preprocessing.sequence.pad_sequences(test_data,
                                                       value=word_index["<PAD>"],
                                                       padding='post',
                                                       maxlen=256)

len(train_data[0]), len(train_data[1])
print(train_data[0])

# input shape is the vocabulary count used for the movie reviews (10,000 words)
vocab_size = 10000
#建立模型
model = keras.Sequential()
model.add(keras.layers.Embedding(vocab_size, 16))
model.add(keras.layers.GlobalAveragePooling1D())  #对序列维度求平均，为每个示例返回固定长度的输出向量
model.add(keras.layers.Dense(16, activation=tf.nn.relu))
model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))

#显示模型的概况
model.summary()

model.compile(optimizer=tf.train.AdamOptimizer(),
              loss='binary_crossentropy',
              metrics=['accuracy'])

#创建验证集
x_val = train_data[:10000]
partial_x_train = train_data[10000:]

y_val = train_labels[:10000]
partial_y_train = train_labels[10000:]

#训练
history = model.fit(partial_x_train,
                    partial_y_train,
                    epochs=40,
                    batch_size=512,
                    validation_data=(x_val, y_val),
                    verbose=1)

results = model.evaluate(test_data, test_labels)
print(results)

history_dict = history.history
history_dict.keys()
##out：dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])


##显示loss下降的图
import matplotlib.pyplot as plt

acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(1, len(acc) + 1)

# "bo" is for "blue dot"
plt.plot(epochs, loss, 'bo', label='Training loss')
# b is for "solid blue line"
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()


##显示accuracy上升的图
plt.clf()   # clear figure
acc_values = history_dict['acc']
val_acc_values = history_dict['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()

layers的概况

_________________________________________________________________

Layer (type) 　　　　　　　　　　Output Shape 　　　　　　　　　　Param

# =================================================================

embedding (Embedding) 　　　　　　(None, None, 16) 　　　　　　　　160000

_________________________________________________________________

global_average_pooling1d (Gl 　　　　(None, 16) 　　　　　　　　　　　　0

_________________________________________________________________

dense (Dense) 　　　　　　　　　　　(None, 16) 　　　　　　　　　　　　272

_________________________________________________________________

dense_1 (Dense)　　　　　　　　　　 (None, 1) 　　　　　　　　　　　　 17

=================================================================

Total params: 160,289

Trainable params: 160,289

Non-trainable params: 0

_________________________________________________________________