将维度为(3，50)的嵌入层连接到LSTM [英] connect embedding layer with dimension (3,50) to lstm
本文介绍了将维度为(3，50)的嵌入层连接到LSTM的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
维度为(3，50)的嵌入层如何接入LSTM？
数组(3，50)被馈送到输入
，其中存储有三个长度为50的数组的时间步长我尝试在重塑之前连接它，但它也不起作用。嵌入增加了维度，而LSTM不需要额外的维度。您必须将张量转换为Tf并手动处理张量，这很可怕。
layer_i_inp = Input(shape = (3,50), name = 'item')
layer_i_emb = Embedding(output_dim = EMBEDDING_DIM*2,
input_dim = us_it_count[0]+1,
input_length = (3,50),
name = 'item_embedding')(layer_i_inp)
layer_i_emb = Reshape([3,50, EMBEDDING_DIM*2])(layer_i_emb)
layer_i_emb = LSTM(MAX_FEATURES, dropout = 0.4, recurrent_dropout = 0.4, return_sequences = True)(layer_i_emb)
layer_i_emb = LSTM(MAX_FEATURES, dropout = 0.4, recurrent_dropout = 0.4, return_sequences = True)(layer_i_emb)
layer_i_emb = LSTM(MAX_FEATURES, dropout = 0.4, recurrent_dropout = 0.4)(layer_i_emb)
layer_i_emb = Flatten()(layer_i_emb)
推荐答案
问题是Embedding层正在输出3D张量，但LSTM层需要2D输入(不包括批次维度)。以下是您可以尝试的几个选项：
选项1
import tensorflow as tf
samples = 100
orders = 3
product_ids_per_order = 50
max_product_id = 120
data = tf.random.uniform((samples, orders, product_ids_per_order), maxval=max_product_id, dtype=tf.int32)
Y = tf.random.uniform((samples,), maxval=2, dtype=tf.int32)
EMBEDDING_DIM = 5
item_input = tf.keras.layers.Input(shape = (orders, product_ids_per_order), name = 'item')
embedding_layer = tf.keras.layers.Embedding(
max_product_id + 1,
output_dim = EMBEDDING_DIM,
input_length = product_ids_per_order,
name = 'item_embedding')
# Map each time step with 50 product ids to an embedding vector of size 5
outputs = []
for i in range(orders):
tensor = embedding_layer(item_input[:, i, :])
layer_i_emb = tf.keras.layers.LSTM(32, dropout = 0.4, recurrent_dropout = 0.4, return_sequences = True)(tensor)
layer_i_emb = tf.keras.layers.LSTM(32, dropout = 0.4, recurrent_dropout = 0.4, return_sequences = True)(layer_i_emb)
layer_i_emb = tf.keras.layers.LSTM(32, dropout = 0.4, recurrent_dropout = 0.4)(layer_i_emb)
outputs.append(layer_i_emb)
output = tf.keras.layers.Concatenate(axis=1)(outputs)
output = tf.keras.layers.Dense(1, activation='sigmoid')(layer_i_emb)
model = tf.keras.Model(item_input, output)
model.compile(optimizer='adam', loss=tf.keras.losses.BinaryCrossentropy())
model.fit(data, Y)
4/4 [==============================] - 15s 1s/step - loss: 0.6926
选项2
import tensorflow as tf
samples = 100
orders = 3
product_ids_per_order = 50
max_product_id = 120
EMBEDDING_DIM = 5
item_input = tf.keras.layers.Input(shape = (orders, product_ids_per_order), name = 'item')
embedding_layer = tf.keras.layers.Embedding(
max_product_id + 1,
output_dim = EMBEDDING_DIM,
input_length = product_ids_per_order,
name = 'item_embedding')
# Map each time step with 50 product ids to an embedding vector of size 5
inputs = []
for i in range(orders):
tensor = embedding_layer(item_input[:, i, :])
tensor = tf.keras.layers.Reshape([product_ids_per_order*EMBEDDING_DIM])(tensor)
tensor = tf.expand_dims(tensor, axis=1)
inputs.append(tensor)
embedding_inputs = tf.keras.layers.Concatenate(axis=1)(inputs)
layer_i_emb = tf.keras.layers.LSTM(32, dropout = 0.4, recurrent_dropout = 0.4, return_sequences = True)(embedding_inputs)
layer_i_emb = tf.keras.layers.LSTM(32, dropout = 0.4, recurrent_dropout = 0.4, return_sequences = True)(layer_i_emb)
layer_i_emb = tf.keras.layers.LSTM(32, dropout = 0.4, recurrent_dropout = 0.4)(layer_i_emb)
output = tf.keras.layers.Dense(1, activation='sigmoid')(layer_i_emb)
model = tf.keras.Model(item_input, output)
print(model.summary())
Model: "model_11"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
item (InputLayer) [(None, 3, 50)] 0 []
tf.__operators__.getitem_41 (S (None, 50) 0 ['item[0][0]']
licingOpLambda)
tf.__operators__.getitem_42 (S (None, 50) 0 ['item[0][0]']
licingOpLambda)
tf.__operators__.getitem_43 (S (None, 50) 0 ['item[0][0]']
licingOpLambda)
item_embedding (Embedding) (None, 50, 5) 605 ['tf.__operators__.getitem_41[0][
0]',
'tf.__operators__.getitem_42[0][
0]',
'tf.__operators__.getitem_43[0][
0]']
reshape_10 (Reshape) (None, 250) 0 ['item_embedding[0][0]']
reshape_11 (Reshape) (None, 250) 0 ['item_embedding[1][0]']
reshape_12 (Reshape) (None, 250) 0 ['item_embedding[2][0]']
tf.expand_dims_9 (TFOpLambda) (None, 1, 250) 0 ['reshape_10[0][0]']
tf.expand_dims_10 (TFOpLambda) (None, 1, 250) 0 ['reshape_11[0][0]']
tf.expand_dims_11 (TFOpLambda) (None, 1, 250) 0 ['reshape_12[0][0]']
concatenate_13 (Concatenate) (None, 3, 250) 0 ['tf.expand_dims_9[0][0]',
'tf.expand_dims_10[0][0]',
'tf.expand_dims_11[0][0]']
lstm_34 (LSTM) (None, 3, 32) 36224 ['concatenate_13[0][0]']
lstm_35 (LSTM) (None, 3, 32) 8320 ['lstm_34[0][0]']
lstm_36 (LSTM) (None, 32) 8320 ['lstm_35[0][0]']
dense_11 (Dense) (None, 1) 33 ['lstm_36[0][0]']
==================================================================================================
Total params: 53,502
Trainable params: 53,502
Non-trainable params: 0
__________________________________________________________________________________________________
None
这篇关于将维度为(3，50)的嵌入层连接到LSTM的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
查看全文
