def cnn_Chinese(text=None):
########################### file_path ##############################
embedding_matrix = './data/tmp/embedding_matrix_CVAT.p'
word_idx_map = './data/tmp/word_idx_map_CVAT.p'
cnn_model_weights_Valence = './data/tmp/CVAT_cnn_model_weights_Valence.hdf5'
cnn_model_weights_Arousal = './data/tmp/CVAT_cnn_model_weights_Arousal.hdf5'
####################################################################
request_text = text
W = load_pickle(embedding_matrix)
# print(len(W[1]))
if request_text is None:
request_text = '中文斷詞前言自然語言處理的其中一個重要環節就是中文斷詞的'
# request_text = clean_str(request_text)
# print(request_text)
request_text = list(jieba.cut(request_text))
word_idx_map = load_pickle(word_idx_map)
idx_request_text = get_idx_from_sent(request_text, word_idx_map)
print(idx_request_text) # type: list
max_len = len(idx_request_text)
idx_request_text = np.array(idx_request_text).reshape((1, max_len))
print(idx_request_text.shape)
def cnn_model():
N_fm = 400 # number of filters
kernel_size = 8
conv_input_height, conv_input_width = max_len, len(W[1])
model = Sequential()
model.add(Embedding(input_dim=W.shape[0], output_dim=W.shape[1], weights=[W], W_constraint=unitnorm()))
model.add(Reshape(dims=(1, conv_input_height, conv_input_width)))
model.add(Convolution2D(nb_filter=N_fm,
nb_row=kernel_size,
nb_col=conv_input_width,
border_mode='valid',
W_regularizer=l2(0.0001)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(conv_input_height - kernel_size + 1, 1), ignore_border=True))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('linear'))
model.compile(loss='mse', optimizer='adagrad')
return model
model = cnn_model()
model.load_weights(cnn_model_weights_Valence)
valence = model.predict(idx_request_text)
model.load_weights(cnn_model_weights_Arousal)
arousal = model.predict(idx_request_text)
return [valence[0], arousal[0]]
def cnn(text=None):
request_text = text
# Test
[idx_data, ratings] = load_pickle('./data/corpus/vader/vader_processed_data_tweets.p')
# print(idx_data[2])
# print(ratings[2])
W = load_pickle('./data/corpus/vader/embedding_matrix_tweets.p')
# print(len(W[1]))
if request_text is None:
request_text = 'why you are not happy'
request_text = clean_str(request_text)
# print(request_text)
word_idx_map = load_pickle('./data/corpus/vader/word_idx_map_tweets.p')
idx_request_text = get_idx_from_sent(request_text, word_idx_map)
# print(idx_request_text) # type: list
max_len = len(idx_request_text)
idx_request_text = np.array(idx_request_text).reshape((1,max_len))
# print(idx_request_text.shape)
def cnn_model():
N_fm = 100 # number of filters
kernel_size = 5
conv_input_height, conv_input_width = max_len, len(W[1])
model = Sequential()
model.add(Embedding(input_dim=W.shape[0], output_dim=W.shape[1], weights=[W], W_constraint=unitnorm()))
model.add(Reshape(dims=(1, conv_input_height, conv_input_width)))
model.add(Convolution2D(nb_filter=N_fm,
nb_row=kernel_size,
nb_col=conv_input_width,
border_mode='valid',
W_regularizer=l2(0.0001)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(conv_input_height - kernel_size + 1, 1), ignore_border=True))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('linear'))
sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mse', optimizer='adagrad')
return model
model = cnn_model()
model.load_weights('./data/corpus/vader/cnn_model_weights.hdf5')
predict_value = model.predict(idx_request_text)
return [predict_value[0], 5.0]
