def __init__(self, emb_dim, num_words, sentence_length, atten_sen_len,
class_dim, embedding_matrix, dropout_rate):
input_s = tf.keras.layers.Input(shape=(sentence_length, ),
dtype=tf.int32)
activations = tf.keras.layers.Embedding(
num_words,
embeddings_initializer=qc_emb.EmbeddingWeights(embedding_matrix),
output_dim=emb_dim)(input_s)
input_p = tf.keras.layers.Input(shape=(atten_sen_len, ),
dtype=tf.int32)
attention = tf.keras.layers.Embedding(
num_words,
embeddings_initializer=qc_emb.EmbeddingWeights(embedding_matrix),
output_dim=emb_dim)(input_p)
layer = attention = Attention()([activations, attention])
layer = tf.keras.layers.Conv1D(emb_dim, 3, activation="relu")(layer)
layer = tf.keras.layers.MaxPool1D(3, 1)(layer)
layer = tf.keras.layers.Dropout(dropout_rate)(layer)
layer = tf.keras.layers.Flatten()(layer)
output = tf.keras.layers.Dense(
class_dim,
activation="softmax",
kernel_regularizer=tf.keras.regularizers.l2(l=0.0))(layer)
super(ACNN, self).__init__(inputs=[input_s, input_p], outputs=[output])
def __init__(self, emb_dim, num_words, sentence_length, class_dim,
embedding_matrix, dropout_rate):
units = emb_dim
input_layer = tf.keras.layers.Input(shape=(sentence_length, ),
dtype=tf.int32)
embedding = tf.keras.layers.Embedding(
num_words,
embeddings_initializer=qc_emb.EmbeddingWeights(embedding_matrix),
output_dim=emb_dim,
mask_zero=False)(input_layer)
activations = tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(units,
return_sequences=True,
dropout=dropout_rate))(embedding)
attention = tf.keras.layers.Dense(1, activation="tanh")(activations)
attention = tf.keras.layers.Flatten()(attention)
attention = tf.keras.layers.Activation("softmax")(attention)
attention = tf.keras.layers.RepeatVector(units * 2)(attention)
attention = tf.keras.layers.Permute([2, 1])(attention)
qc = tf.keras.layers.Multiply()([activations, attention])
qc = tf.keras.layers.Lambda(lambda xin: K.sum(xin, axis=-2),
output_shape=(units * 2, ))(qc)
output = tf.keras.layers.Dense(
class_dim,
activation="softmax",
kernel_regularizer=tf.keras.regularizers.l2(l=0.0))(qc)
super(A_BLSTM, self).__init__(inputs=[input_layer], outputs=output)
def __init__(self, emb_dim, num_words, sentence_length, class_dim,
embedding_matrix, dropout_rate):
input_layer = tf.keras.layers.Input(shape=(sentence_length, ),
dtype=tf.int32)
layer = tf.keras.layers.Embedding(
num_words,
embeddings_initializer=qc_emb.EmbeddingWeights(embedding_matrix),
output_dim=emb_dim)(input_layer)
conv1D_layer_3 = tf.keras.layers.Conv1D(emb_dim, 3,
activation="relu")(layer)
conv1D_layer_3 = tf.keras.layers.MaxPool1D(3, 1)(conv1D_layer_3)
conv1D_layer_4 = tf.keras.layers.Conv1D(emb_dim, 4,
activation="relu")(layer)
conv1D_layer_4 = tf.keras.layers.MaxPool1D(4, 1)(conv1D_layer_4)
conv1D_layer_5 = tf.keras.layers.Conv1D(emb_dim, 5,
activation="relu")(layer)
conv1D_layer_5 = tf.keras.layers.MaxPool1D(5, 1)(conv1D_layer_5)
layer = tf.keras.layers.concatenate(
[conv1D_layer_3, conv1D_layer_4, conv1D_layer_5], axis=1)
layer = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(emb_dim *
2))(layer)
layer = tf.keras.layers.Dropout(dropout_rate)(layer)
output = tf.keras.layers.Dense(
class_dim,
activation="softmax",
kernel_regularizer=tf.keras.regularizers.l2(l=0.0))(layer)
super(CNN_BGRU, self).__init__(inputs=[input_layer], outputs=output)
def __init__(self, emb_dim, num_words, sentence_length,
class_dim, embedding_matrix, dropout_rate):
input_layer = tf.keras.layers.Input(shape=(sentence_length,), dtype=tf.int32)
layer = tf.keras.layers.Embedding(num_words,
embeddings_initializer=
qc_emb.EmbeddingWeights(embedding_matrix),
output_dim=emb_dim)(input_layer)
layer = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(emb_dim * 2))(layer)
layer = tf.keras.layers.Dropout(dropout_rate)(layer)
#layer = tf.keras.layers.Flatten()(layer)
output = tf.keras.layers.Dense(class_dim, activation="softmax",
kernel_regularizer = tf.keras.regularizers.l2(l=0.0) )(layer)
super(BGRU, self).__init__(inputs=[input_layer], outputs=output)