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本文链接地址: Tensorflow 文本分类实例
实例学习
了解上文的Embedding和Pooling原理后，就可以理解Tensorflow的示例程序了：
1 加载测试数据IMDB imdb；
2 对每个样本进行padding操作，补齐到256长的数组（keras.preprocessing.sequence.pad_sequences），完成后为 mX256 的矩阵；
3 创建模型，先使用Embedding把输入的稀疏矩阵进行致密矩阵的映射，完成后为 mX256X16 的矩阵；
4 然后进行Pooling把矩阵降维，完成后为 mX16 的矩阵；
5 最后进行2个全连接操作，完成后分别为 mX16，mX1 的矩阵，由此得到每条评论的结果，好评或差评；
6 通过训练完成神经元参数的设置，然后可以对测试数据进行分类。
测试程序
from __future__ import absolute_import, division, print_function, unicode_literals import tensorflow as tf from tensorflow import keras 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))) # 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()]) def decode_review(text): return ' '.join([reverse_word_index.get(i, '?') for i in text]) for i in range(0,10): print(decode_review(train_data[i])) print(train_labels[i]) 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) vocab_size = 10000 model = keras.Sequential() model.add(keras.layers.Embedding(vocab_size, 16)) model.add(keras.layers.GlobalAveragePooling1D()) model.add(keras.layers.Dense(16, activation='relu')) model.add(keras.layers.Dense(1, activation='sigmoid')) model.summary() model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) x_val = train_data[:10000] partial_x_train = train_data[10000:] y_val = train_labels[:10000] partial_y_train = train_labels[10000:] 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 import matplotlib.pyplot as plt acc = history_dict['accuracy'] val_acc = history_dict['val_accuracy'] loss = history_dict['loss'] val_loss = history_dict['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() plt.clf() # clear figure 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() |
对Embedding和GlobalAveragePooling1D再做说明：
有如下的程序:
如果有2X10的文档输入矩阵，构建10X4的参数矩阵，Embedding后输出2X10X4的矩阵，经过GlobalAveragePooling1D后得到2X4的矩阵。这样就完成了从输入2X10的稀疏矩阵到2X4的致密矩阵转换。
params = tf.Variable(np.arange(40.0).reshape(10,4)) ids = tf.constant([[0,0,1,0,0,0,0,2,0,0], [0,0,1,0,0,0,0,2,0,0]]) batch_embedded_tensor = embedding_ops.embedding_lookup(params=params, ids=ids, partition_strategy='mod', name="embedding") print ("########## params #########") print(params) print ("########## ids #########") print(ids) print ("########## batch_embedded_tensor #########") print(batch_embedded_tensor) print ("########## math_ops.reduce_mean(input_tensor=batch_embedded_tensor, axis=[0,1], keepdims=True); #########") print(math_ops.reduce_mean(input_tensor=batch_embedded_tensor, axis=[1], keepdims=True)) ########## params ######### <tf.Variable 'Variable:0' shape=(10, 4) dtype=float64, numpy= array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], [12., 13., 14., 15.], [16., 17., 18., 19.], [20., 21., 22., 23.], [24., 25., 26., 27.], [28., 29., 30., 31.], [32., 33., 34., 35.], [36., 37., 38., 39.]])> ########## ids ######### tf.Tensor( [[0 0 1 0 0 0 0 2 0 0] [0 0 1 0 0 0 0 2 0 0]], shape=(2, 10), dtype=int32) ########## batch_embedded_tensor ######### tf.Tensor( [[[ 0. 1. 2. 3.] [ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 0. 1. 2. 3.] [ 0. 1. 2. 3.] [ 0. 1. 2. 3.] [ 0. 1. 2. 3.] [ 8. 9. 10. 11.] [ 0. 1. 2. 3.] [ 0. 1. 2. 3.]] [[ 0. 1. 2. 3.] [ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 0. 1. 2. 3.] [ 0. 1. 2. 3.] [ 0. 1. 2. 3.] [ 0. 1. 2. 3.] [ 8. 9. 10. 11.] [ 0. 1. 2. 3.] [ 0. 1. 2. 3.]]], shape=(2, 10, 4), dtype=float64) ########## math_ops.reduce_mean(input_tensor=batch_embedded_tensor, axis=[0,1], keepdims=True); ######### tf.Tensor( [[[1.2 2.2 3.2 4.2]] [[1.2 2.2 3.2 4.2]]], shape=(2, 1, 4), dtype=float64) |
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