Q1_test = X_test[:,0]
Q2_test = X_test[:,1]
# Define the model
question1 = Input(shape=(MAX_SEQUENCE_LENGTH,))
print(question1)
question2 = Input(shape=(MAX_SEQUENCE_LENGTH,))
print("word_embedding_matrix",word_embedding_matrix)
print(EMBEDDING_DIM)
print(question1)
q1 = Embedding(nb_words + 1,
EMBEDDING_DIM,
weights=[word_embedding_matrix],
input_length=MAX_SEQUENCE_LENGTH,
trainable=False)(question1)
print("q1.shape1",q1.get_shape())
q1 = TimeDistributed(Dense(EMBEDDING_DIM, activation='relu'))(q1)
print("q1.shape2",q1.get_shape())
q1 = Lambda(lambda x: K.max(x, axis=1), output_shape=(EMBEDDING_DIM, ))(q1)
print("q1.shape3",q1.get_shape())
q2 = Embedding(nb_words + 1,
EMBEDDING_DIM,
weights=[word_embedding_matrix],
input_length=MAX_SEQUENCE_LENGTH,
trainable=False)(question2)
q2 = TimeDistributed(Dense(EMBEDDING_DIM, activation='relu'))(q2)
q2 = Lambda(lambda x: K.max(x, axis=1), output_shape=(EMBEDDING_DIM, ))(q2)
print("q2 shape",q2.get_shape())
#在这里瞎搞一下，把两个向量做做运算看看。
rb = Flatten()(rb)
pb = Flatten()(pb)
rb = Dense(units=1, activation='relu')(rb)
pb = Dense(units=1, activation='relu')(pb)
add = Add()([pb, dot, rb])
# o=Flatten()(add)
out = Dense(units=1, activation='relu')(add)
# cat=Concatenate()([rb,pb,dot])
#out=Dense(units=1,activation='relu')(pb)
model = Model(inputs=[u, m], outputs=[add])
model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])
print(dot.get_shape())
print(rb.get_shape())
print(pb.get_shape())
model.fit([user, movie],
rate,
validation_data=([userv, moviev], ratev),
epochs=7,
batch_size=32,
callbacks=[
ModelCheckpoint('model',
monitor='val_acc',
save_best_only=True)
])
# model.save('model')
else:
model = load_model('model')
#def word_idx2embedding(idx):
# import keras.backend as be
# return be.gather(emb_vocab_tensor, tf.to_int64(idx))
word_idx_in = Input(shape=(input_len,))
#word_vecs = Lambda(word_idx2embedding,
# output_shape=(input_len, emb_model.vector_size+1))(word_idx_in)
word_vecs = Embedding(len(emb_vocab), 50, trainable=True)(word_idx_in)
grammar_features_in = Input(shape=(input_len, 62), name='grammar_features_in') #TODO: len(grammar_feature_extractor...)
clstm_in = Input(shape=(char_encoder.max_wlen,), name='clstm_in')
clstm_embed = Embedding(len(char_encoder.chars), len(char_encoder.chars), weights=[char_encoder.embeddings], trainable=False)(clstm_in)
print('clstm_embed initial -', clstm_embed.get_shape())
filter_length = [5, 3, 3]
nb_filter = [196, 196, 256]
pool_length = 2
for i in range(len(nb_filter)):
clstm_embed = Convolution1D(nb_filter=nb_filter[i],
filter_length=filter_length[i],
border_mode='same',
activation='relu',
init='glorot_normal',
subsample_length=1)(clstm_embed)
print('clstm_embed', i, '-', clstm_embed.get_shape())
clstm_embed = Dropout(0.1)(clstm_embed)
