【507】NLP实战系列（四）—— 实现一个最简单的NN

　　完成前三节的基础准备，就可以先撸个最简单的 NN 网络。

1. 获取训练数据与测试数据

　　按照如下代码实现，具体说明可以参见第三部分。

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from keras.datasets import imdb from keras import preprocessing   # Number of words to consider as features max_features = 10000 # Cut texts after this number of words # (among top max_features most common words) maxlen = 20   # Load the data as lists of integers. (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)   # This turns our lists of integers # into a 2D integer tensor of shape `(samples, maxlen)` x_train = preprocessing.sequence.pad_sequences(x_train, maxlen=maxlen) x_test = preprocessing.sequence.pad_sequences(x_test, maxlen=maxlen)

2. 网络训练 　　

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from keras.models import Sequential from keras.layers import Flatten, Dense   model = Sequential() # We specify the maximum input length to our Embedding layer # so we can later flatten the embedded inputs model.add(Embedding(10000, 8, input_length=maxlen)) # After the Embedding layer, # our activations have shape `(samples, maxlen, 8)`.   # We flatten the 3D tensor of embeddings # into a 2D tensor of shape `(samples, maxlen * 8)` model.add(Flatten())   # We add the classifier on top model.add(Dense(1, activation='sigmoid')) model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc']) model.summary()   history = model.fit(x_train, y_train,                     epochs=10,                     batch_size=32,                     validation_split=0.2)

　　outputs:

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_2 (Embedding)      (None, 20, 8)             80000     
_________________________________________________________________
flatten_1 (Flatten)          (None, 160)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 161       
=================================================================
Total params: 80,161
Trainable params: 80,161
Non-trainable params: 0
_________________________________________________________________
Train on 20000 samples, validate on 5000 samples
Epoch 1/10
20000/20000 [==============================] - 2s 75us/step - loss: 0.6759 - acc: 0.6043 - val_loss: 0.6398 - val_acc: 0.6810
Epoch 2/10
20000/20000 [==============================] - 1s 52us/step - loss: 0.5657 - acc: 0.7428 - val_loss: 0.5467 - val_acc: 0.7206
Epoch 3/10
20000/20000 [==============================] - 1s 52us/step - loss: 0.4752 - acc: 0.7808 - val_loss: 0.5113 - val_acc: 0.7384
Epoch 4/10
20000/20000 [==============================] - 1s 52us/step - loss: 0.4263 - acc: 0.8079 - val_loss: 0.5008 - val_acc: 0.7454
Epoch 5/10
20000/20000 [==============================] - 1s 56us/step - loss: 0.3930 - acc: 0.8257 - val_loss: 0.4981 - val_acc: 0.7540
Epoch 6/10
20000/20000 [==============================] - 1s 71us/step - loss: 0.3668 - acc: 0.8394 - val_loss: 0.5013 - val_acc: 0.7534
Epoch 7/10
20000/20000 [==============================] - 1s 57us/step - loss: 0.3435 - acc: 0.8534 - val_loss: 0.5051 - val_acc: 0.7518
Epoch 8/10
20000/20000 [==============================] - 1s 60us/step - loss: 0.3223 - acc: 0.8658 - val_loss: 0.5132 - val_acc: 0.7484
Epoch 9/10
20000/20000 [==============================] - 2s 76us/step - loss: 0.3022 - acc: 0.8765 - val_loss: 0.5213 - val_acc: 0.7494
Epoch 10/10
20000/20000 [==============================] - 2s 84us/step - loss: 0.2839 - acc: 0.8860 - val_loss: 0.5302 - val_acc: 0.7468