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本次demo主題是使用keras對IMDB影評進(jìn)行文本分類：

import tensorflow as tf from tensorflow import keras import numpy as npprint(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>"] = 3reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])#把數(shù)字序列轉(zhuǎn)化為相應(yīng)的字符串 def decode_review(text):return ' '.join([reverse_word_index.get(i, '?') for i in text])#顯示其中一個評價 decode_review(train_data[0])#pad填充使其長度一樣 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()) #對序列維度求平均，為每個示例返回固定長度的輸出向量 model.add(keras.layers.Dense(16, activation=tf.nn.relu)) model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))#顯示模型的概況 model.summary()model.compile(optimizer=tf.train.AdamOptimizer(),loss='binary_crossentropy',metrics=['accuracy'])#創(chuàng)建驗證集 x_val = train_data[:10000] partial_x_train = train_data[10000:]y_val = train_labels[:10000] partial_y_train = train_labels[10000:]#訓(xùn)練 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'])##顯示loss下降的圖 import matplotlib.pyplot as pltacc = 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()##顯示accuracy上升的圖 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()

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layers的概況

_________________________________________________________________

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

_________________________________________________________________

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轉(zhuǎn)載于:https://www.cnblogs.com/hotsnow/p/9506354.html

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