first commit

source/cnn_network/cnn_classification.py

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+import keras.backend as K
+from keras.models import Model
+from keras.preprocessing import sequence
+from keras.layers import Input
+from keras.layers import Dense, Activation
+from keras.layers import Embedding
+from keras.layers import Conv1D, GlobalMaxPooling1D
+from keras.datasets import imdb
+
+
+# set parameters:
+batch_size = 32
+embedding_dims = 200
+filters = 250
+kernel_size = 3
+epochs = 2
+
+# load the dataset but only keep the top n words, zero the rest
+top_words = 5000
+(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=top_words)
+# pad dataset to a maximum review length in words
+max_words = 500
+x_train = sequence.pad_sequences(x_train, maxlen=max_words)
+x_test = sequence.pad_sequences(x_test, maxlen=max_words)
+
+# Build model
+sentence = Input(batch_shape=(None, max_words), dtype='int32', name='sentence')
+embedding_layer = Embedding(top_words, embedding_dims, input_length=max_words)
+sent_embed = embedding_layer(sentence)
+conv_layer = Conv1D(filters, kernel_size, padding='valid', activation='relu')
+sent_conv = conv_layer(sent_embed)
+sent_pooling = GlobalMaxPooling1D()(sent_conv)
+sent_repre = Dense(250)(sent_pooling)
+sent_repre = Activation('relu')(sent_repre)
+sent_repre = Dense(1)(sent_repre)
+pred = Activation('sigmoid')(sent_repre)
+model = Model(inputs=sentence, outputs=pred)
+model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+# fit the model
+model.fit(x_train, y_train, batch_size=batch_size,
+          epochs=epochs, verbose=1,validation_data=(x_test, y_test))
+scores = model.evaluate(x_test, y_test, verbose=0)
+print("Accuracy: %.2f%%" % (scores[1]*100))

source/cnn_network/multi_window_cnn.py

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+import keras.backend as K
+from keras.models import Model
+from keras.preprocessing import sequence
+from keras.layers import Input, concatenate
+from keras.layers import Dense, Activation
+from keras.layers import Embedding
+from keras.layers import Conv1D, GlobalMaxPooling1D
+from keras.datasets import imdb
+from keras import optimizers
+
+# set parameters:
+batch_size = 32
+embedding_dims = 200
+filters = 250
+kernel_size = 3
+epochs = 2
+kernel_size_list = [2, 3, 4, 5]
+
+# load the dataset but only keep the top n words, zero the rest
+top_words = 5000
+(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=top_words)
+# pad dataset to a maximum review length in words
+max_words = 500
+x_train = sequence.pad_sequences(x_train, maxlen=max_words)
+x_test = sequence.pad_sequences(x_test, maxlen=max_words)
+
+# Build model
+sentence = Input(batch_shape=(None, max_words), dtype='int32', name='sentence')
+embedding_layer = Embedding(top_words, embedding_dims, input_length=max_words)
+sent_embed = embedding_layer(sentence)
+
+# use multi window-size cnn
+cnn_result = []
+for kernel_size in kernel_size_list:
+    conv_layer = Conv1D(filters, kernel_size, padding='valid', activation='relu')
+    sent_conv = conv_layer(sent_embed)
+    sent_pooling = GlobalMaxPooling1D()(sent_conv)
+    cnn_result.append(sent_pooling)
+cnn_result = concatenate(cnn_result)
+
+sent_repre = Dense(250)(cnn_result)
+sent_repre = Activation('relu')(sent_repre)
+sent_repre = Dense(1)(sent_repre)
+pred = Activation('sigmoid')(sent_repre)
+model = Model(inputs=sentence, outputs=pred)
+#adam = optimizers.adam(lr=0.0005)
+model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+# fit the model
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=epochs,
+          verbose=1,
+          validation_data=(x_test, y_test))