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mtreviso/cnn_rnn_model.py

Last active October 12, 2017 14:34
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  1. mtreviso revised this gist Jul 23, 2016. 1 changed file with 25 additions and 2 deletions.
    27 changes: 25 additions & 2 deletions cnn_rnn_model.py
    View file
    Original file line number Diff line number Diff line change
    @@ -21,7 +21,30 @@

    rnn_text = RNN(self.nb_hidden, return_sequences=True, activation='relu')(cnn1d_text)
    drop_text = Dropout(0.5)(rnn_text)
    output_text = TimeDistributed(Dense(output_dim=nb_classes, activation='softmax'))(drop_text)
    output_text = TimeDistributed(Dense(output_dim=nb_classes, activation='softmax'), name='output_source')(drop_text)

    text_model = Model(input=[sequence_text], output=output_text)
    text_model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
    text_model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

    def get_batch(data_in, data_out):
    lengths = list(set(map(len, data_in)))
    data_by_length = {}
    for i, l in enumerate(map(len, data_in)):
    if l not in data_by_length:
    data_by_length[l] = []
    data_by_length[l].append(i)

    while True: # a new epoch
    np.random.shuffle(lengths)
    for length in lengths:
    indexes = data_by_length[length]
    np.random.shuffle(indexes)
    input_data = {'input_source': np.array([data_in[i] for i in indexes])}
    output_data = {'output_source': np.array([data_out[i] for i in indexes])}
    yield (input_data, output_data)

    text_model.fit_generator(get_batch(X_train, Y_train),
    samples_per_epoch=len(X_train),
    validation_data=get_batch(X_test, Y_train),
    nb_val_samples=len(X_test),
    nb_epoch=10)
  2. mtreviso revised this gist Jul 23, 2016. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion cnn_rnn_model.py
    View file
    Original file line number Diff line number Diff line change
    @@ -17,7 +17,7 @@

    cnn1d_text_pad = ZeroPadding1D(padding)(embedded_text)
    cnn1d_text = Convolution1D(nb_filter=nb_filter, filter_length=filter_length,
    border_mode=border_mode, subsample_length=stride, activation='relu')(cnn1d_text_pad)
    border_mode=border_mode, subsample_length=stride, activation='relu')(cnn1d_text_pad)

    rnn_text = RNN(self.nb_hidden, return_sequences=True, activation='relu')(cnn1d_text)
    drop_text = Dropout(0.5)(rnn_text)
  3. mtreviso created this gist Jul 23, 2016.
    27 changes: 27 additions & 0 deletions cnn_rnn_model.py
    View file
    Original file line number Diff line number Diff line change
    @@ -0,0 +1,27 @@
    # vocabulary = OrderedDict()
    input_length = None
    vocabulary_size = max(vocabulary.values()) + 1
    weights_w2v = list(map(Word2Vec.__getitem__, vocabulary.keys()))
    embedding_size len(weights_w2v[0])
    nb_classes = 5

    # CNN hyperparms
    nb_filter = 64
    filter_length = 5
    border_mode = 'valid'
    padding = filter_length // 2
    stride = 1

    sequence_text = Input(name='input_source', shape=(input_length,), dtype='int32')
    embedded_text = Embedding(vocabulary_size, embedding_size, input_length=input_length, weights=[weights_w2v])(sequence_text)

    cnn1d_text_pad = ZeroPadding1D(padding)(embedded_text)
    cnn1d_text = Convolution1D(nb_filter=nb_filter, filter_length=filter_length,
    border_mode=border_mode, subsample_length=stride, activation='relu')(cnn1d_text_pad)

    rnn_text = RNN(self.nb_hidden, return_sequences=True, activation='relu')(cnn1d_text)
    drop_text = Dropout(0.5)(rnn_text)
    output_text = TimeDistributed(Dense(output_dim=nb_classes, activation='softmax'))(drop_text)

    text_model = Model(input=[sequence_text], output=output_text)
    text_model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])