85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
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from __future__ import absolute_import
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from __future__ import print_function
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import numpy as np
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np.random.seed(1337) # for reproducibility
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from keras.preprocessing import sequence
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from keras.optimizers import RMSprop
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from keras.models import Sequential
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from keras.layers.core import Dense, Dropout, Activation, Flatten
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from keras.layers.embeddings import Embedding
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from keras.layers.convolutional import Convolution1D, MaxPooling1D
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from keras.datasets import imdb
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# set parameters:
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max_features = 5000
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maxlen = 100
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batch_size = 10
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embedding_dims = 100
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nb_filters = 250
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filter_length = 3
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hidden_dims = 250
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nb_epochs = 10
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print("Loading data...")
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(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features,
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test_split=0.2)
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print(len(X_train), 'train sequences')
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print(len(X_test), 'test sequences')
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print("Pad sequences (samples x time)")
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X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
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X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
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print('X_train shape:', X_train.shape)
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print('X_test shape:', X_test.shape)
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print('Build model...')
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model = Sequential()
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# we start off with an efficient embedding layer which maps
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# our vocab indices into embedding_dims dimensions
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model.add(Embedding(max_features, embedding_dims))
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# we add a Convolution1D, which will learn nb_filters
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# word group filters of size filter_length:
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model.add(Convolution1D(input_dim=embedding_dims,
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nb_filter=nb_filters,
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filter_length=filter_length,
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border_mode="valid",
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activation="relu",
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subsample_length=1))
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# we use standard max pooling (halving the output of the previous layer):
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model.add(MaxPooling1D(pool_length=2))
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# We flatten the output of the conv layer, so that we can add a vanilla dense layer:
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model.add(Flatten())
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# Computing the output shape of a conv layer can be tricky;
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# for an good tutorial, see: http://cs231n.github.io/convolutional-networks/
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output_size = nb_filters * (((maxlen - filter_length)/1)+1)/2
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# We add a vanilla hidden layer:
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model.add(Dense(output_size, hidden_dims))
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model.add(Dropout(0.5))
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model.add(Activation('relu'))
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# We project onto a single unit output layer, and squash it with a sigmoid:
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model.add(Dense(hidden_dims, 1))
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model.add(Activation('sigmoid'))
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# we use some mild clipping to protect our gradients from vanishing:
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rms = RMSprop(clipnorm=0.1)
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model.compile(loss='binary_crossentropy', optimizer=rms, class_mode="binary")
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# The performance of this toy model seems comparable to that of the LSTM in imdb_lstm.py,
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# reaching an acceptable 83.16% on the test data at epoch 10.
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# One epoch took around 268 seconds on a Quadro K600.
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print("Train...")
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for e in range(nb_epochs):
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print(">>> Epoch ", e+1)
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model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=1, show_accuracy=True)
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score, acc = model.evaluate(X_test, y_test, batch_size=batch_size, show_accuracy=True)
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print('\t- Test loss:', score)
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print('\t- Test accuracy:', acc)
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