Я сделал модель классификации текста, используя LSTM со слоем внимания. Я сделал свою модель хорошо, она хорошо работает, но я не могу отобразить вес внимания и важность / внимание каждого слова в обзоре (входной текст).
Код, используемый для этой модели:
def dot_product(x, kernel):
if K.backend() == 'tensorflow':
return K.squeeze(K.dot(x, K.expand_dims(kernel)),axis=-1)
else:
return K.dot(x, kernel)
class AttentionWithContext(Layer):
"""
Attention operation, with a context/query vector, for temporal data.
"Hierarchical Attention Networks for Document Classification"
by using a context vector to assist the attention
# Input shape
3D tensor with shape: (samples, steps, features).
# Output shape
2D tensor with shape: (samples, features).
How to use:
Just put it on top of an RNN Layer (GRU/LSTM/SimpleRNN) with return_sequences=True.
The dimensions are inferred based on the output shape of the RNN.
Note: The layer has been tested with Keras 2.0.6
Example:
model.add(LSTM(64, return_sequences=True))
model.add(AttentionWithContext())
# next add a Dense layer (for classification/regression) or whatever
"""
def __init__(self,
W_regularizer=None, u_regularizer=None, b_regularizer=None,
W_constraint=None, u_constraint=None, b_constraint=None,
bias=True, **kwargs):
self.supports_masking = True
self.init = initializers.get('glorot_uniform')
self.W_regularizer = regularizers.get(W_regularizer)
self.u_regularizer = regularizers.get(u_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.u_constraint = constraints.get(u_constraint)
self.b_constraint = constraints.get(b_constraint)
self.bias = bias
super(AttentionWithContext, self).__init__(**kwargs)
def build(self, input_shape):
assert len(input_shape) == 3
self.W = self.add_weight((input_shape[-1], input_shape[-1],),
initializer=self.init,
name='{}_W'.format(self.name),
regularizer=self.W_regularizer,
constraint=self.W_constraint)
if self.bias:
self.b = self.add_weight((input_shape[-1],),
initializer='zero',
name='{}_b'.format(self.name),
regularizer=self.b_regularizer,
constraint=self.b_constraint)
self.u = self.add_weight((input_shape[-1],),
initializer=self.init,
name='{}_u'.format(self.name),
regularizer=self.u_regularizer,
constraint=self.u_constraint)
super(AttentionWithContext, self).build(input_shape)
def compute_mask(self, input, input_mask=None):
# do not pass the mask to the next layers
return None
def call(self, x, mask=None):
uit = dot_product(x, self.W)
if self.bias:
uit += self.b
uit = K.tanh(uit)
ait = dot_product(uit, self.u)
a = K.exp(ait)
# apply mask after the exp. will be re-normalized next
if mask is not None:
# Cast the mask to floatX to avoid float64 upcasting in theano
a *= K.cast(mask, K.floatx())
# in some cases especially in the early stages of training the sum may be almost zero
# and this results in NaN's. A workaround is to add a very small positive number ε to the sum.
# a /= K.cast(K.sum(a, axis=1, keepdims=True), K.floatx())
a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx())
a = K.expand_dims(a)
weighted_input = x * a
return K.sum(weighted_input, axis=1)
def compute_output_shape(self, input_shape):
return input_shape[0], input_shape[-1]
EMBEDDING_DIM=100
max_seq_len=118
bach_size = 256
num_epochs=50
from keras.models import Model
from keras.layers import Dense, Embedding, Input
from keras.layers import LSTM, Bidirectional, Dropout
def BidLstm():
#inp = Input(shape=(118,100))
#x = Embedding(max_features, embed_size, weights=[embedding_matrix],
#trainable=False)(inp)
model1=Sequential()
model1.add(Dense(512,input_shape=(118,100)))
model1.add(Activation('relu'))
#model1.add(Flatten())
#model1.add(BatchNormalization(input_shape=(100,)))
model1.add(Bidirectional(LSTM(100, activation="relu",return_sequences=True)))
model1.add(Dropout(0.1))
model1.add(TimeDistributed(Dense(200)))
model1.add(AttentionWithContext())
model1.add(Dropout(0.25))
model1.add(Dense(4, activation="softmax"))
model1.compile(loss='sparse_categorical_crossentropy', optimizer='adam',
metrics=['accuracy'])
model1.summary()
return model1