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model building
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| # model developing | |
| text_input_layer = Input(shape=(500,)) | |
| embedding_layer = Embedding(max_words, 50)(text_input_layer) | |
| text_layer = Conv1D(256, 3, activation='relu')(embedding_layer) | |
| text_layer = MaxPooling1D(3)(text_layer) | |
| text_layer = Conv1D(256, 3, activation='relu')(text_layer) | |
| text_layer = MaxPooling1D(3)(text_layer) | |
| text_layer = Conv1D(256, 3, activation='relu')(text_layer) | |
| text_layer = MaxPooling1D(3)(text_layer) | |
| text_layer = Conv1D(256, 3, activation='relu')(text_layer) | |
| text_layer = MaxPooling1D(3)(text_layer) | |
| text_layer = Conv1D(256, 3, activation='relu')(text_layer) | |
| text_layer = MaxPooling1D(3)(text_layer) | |
| text_layer = GlobalMaxPooling1D()(text_layer) | |
| text_layer = Dense(256, activation='relu')(text_layer) | |
| output_layer = Dense(1, activation='sigmoid')(text_layer) | |
| model = Model(text_input_layer, output_layer) | |
| model.summary() | |
| model.compile(optimizer=RMSprop(lr=0.001), | |
| loss='binary_crossentropy', | |
| metrics=['acc']) |
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