我有個問題。我想在我的 1D-CNN 中使用 LSTM 來改進我的 NLP 任務。問題是我不知道將 LSTM 放在哪里。我發現了以下內容。
A CNN LSTM can be defined by adding CNN layers on the front end followed by LSTM layers with a Dense layer on the output.
（來源：https ://machinelearningmastery.com/cnn-long-short-term-memory-networks/ ）
但是，如果我這樣設定（參見下面的代碼），我會收到以下錯誤
ValueError: Input 0 of layer "lstm_4" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 128)
這是因為 LSTM 需要一個 3D 輸入陣列。是否有解決此錯誤并在此位置使用 LSTM 的選項？還是應該在其他地方？
from keras.models import Sequential
from keras.layers import Input, Embedding, Dense, GlobalMaxPooling1D, Conv2D, MaxPool2D, LSTM, Bidirectional, Lambda, Conv1D, MaxPooling1D, GlobalMaxPooling1D
model_lstm = Sequential()
model_lstm.add(
Embedding(vocab_size
,embed_size
,weights = [embedding_matrix] #Supplied embedding matrix created from glove
,input_length = maxlen
,trainable=False)
)
model_lstm.add(SpatialDropout1D(rate = 0.4))
model_lstm.add(Conv1D(256, 7, activation="relu"))
model_lstm.add(MaxPooling1D())
#model_lstm.add(LSTM(128, dropout=0.3, recurrent_dropout=0.3, return_sequences=True))
model_lstm.add(Conv1D(128, 5, activation="relu"))
model_lstm.add(MaxPooling1D())
model_lstm.add(GlobalMaxPooling1D())
model_lstm.add(LSTM(128, dropout=0.3,return_sequences=True))
model_lstm.add(Dropout(0.3))
model_lstm.add(Dense(128, activation="relu"))
model_lstm.add(Dense(4, activation='softmax'))
print(model_lstm.summary())
完整代碼
print("Train shape : ",train_X2.shape)
print("Test shape : ",test_X2.shape)
## Tokenize the sentences
tokenizer = Tokenizer(num_words=num_unique_words)
tokenizer.fit_on_texts(list(train_X2))
train_X2 = tokenizer.texts_to_sequences(train_X2)
test_X2 = tokenizer.texts_to_sequences(test_X2)
## Pad the sentences
train_X = pad_sequences(train_X2, maxlen=maxlen)
test_X = pad_sequences(test_X2, maxlen=maxlen)
word_index = tokenizer.word_index
vocab_size = len(tokenizer.word_index) 1
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.utils import to_categorical
#label encoding
le = LabelEncoder()
train_y = le.fit_transform(train_y2.tolist())
test_y = le.transform(test_y2.tolist())
#one hot encoding
train_y = to_categorical(train_y)
test_y = to_categorical(test_y)
# Word2Vec as pretrained embedding
import gensim
from gensim.models import Word2Vec
from gensim.utils import simple_preprocess
from gensim.models.keyedvectors import KeyedVectors
NUM_WORDS=20000
word_vectors = KeyedVectors.load_word2vec_format(r'./input/GoogleNews-vectors-negative300.bin', binary=True)
EMBEDDING_DIM=300
vocabulary_size=min(len(word_index) 1,NUM_WORDS)
embedding_matrix = np.zeros((vocabulary_size, EMBEDDING_DIM))
for word, i in word_index.items():
if i>=NUM_WORDS:
continue
try:
embedding_vector = word_vectors[word]
embedding_matrix[i] = embedding_vector
except KeyError:
embedding_matrix[i]=np.random.normal(0,np.sqrt(0.25),EMBEDDING_DIM)
del(word_vectors)
from keras.layers import Embedding
embedding_layer = Embedding(vocabulary_size,
EMBEDDING_DIM,
weights=[embedding_matrix],
trainable=True)
from keras.layers import Embedding
EMBEDDING_DIM=300
vocabulary_size=min(len(word_index) 1,NUM_WORDS)
embedding_layer = Embedding(vocabulary_size,
EMBEDDING_DIM)
# CNN
uj5u.com熱心網友回復：
也許嘗試洗掉 GlobalMaxPooling1D 層，將您的張量減少到 2D。例如嘗試復制并運行：
from keras.models import Sequential
from keras.layers import Input, Embedding, Dense, GlobalMaxPooling1D, Conv2D, MaxPool2D, LSTM, Bidirectional, Lambda, Conv1D, MaxPooling1D, GlobalMaxPooling1D
model_lstm = Sequential()
model_lstm.add(
Embedding(vocab_size
,embed_size
,weights = [embedding_matrix] #Supplied embedding matrix created from glove
,input_length = maxlen
,trainable=False)
)
model_lstm.add(SpatialDropout1D(rate = 0.4))
model_lstm.add(Conv1D(256, 7, activation="relu"))
model_lstm.add(MaxPooling1D())
#model_lstm.add(LSTM(128, dropout=0.3, recurrent_dropout=0.3, return_sequences=True))
model_lstm.add(Conv1D(128, 5, activation="relu"))
model_lstm.add(MaxPooling1D())
model_lstm.add(LSTM(128, dropout=0.3, return_sequences=False))
model_lstm.add(Dropout(0.3))
model_lstm.add(Dense(128, activation="relu"))
model_lstm.add(Dense(4, activation='softmax'))
print(model_lstm.summary())
uj5u.com熱心網友回復：
保留您的所有作品并在沒有本地字典或從單行字串快速搜索的情況下創建標簽的意義。???? 所有剩余的你都不需要強行審查繼續處理。
很簡單，當平面映射和標簽從分離的源和單行字串創建歌詞時，我嘗試在不創建標記器的情況下制作它。
示例：????這種方式很容易讓你沒有可以手動插入的快速單詞映射，從源代碼中看到你需要填寫快速單詞搜索的數量。
import os
from os.path import exists
import tensorflow as tf
import tensorflow_text as tft
import matplotlib.pyplot as plt
import gensim
from gensim.models import Word2Vec
from gensim.utils import simple_preprocess
from gensim.models.keyedvectors import KeyedVectors
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
None
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
config = tf.config.experimental.set_memory_growth(physical_devices[0], True)
print(physical_devices)
print(config)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
input_word = tf.constant(' \'Cause it\'s easy as an ice cream sundae Slipping outta your hand into the dirt Easy as an ice cream sundae Every dancer gets a little hurt Easy as an ice cream sundae Slipping outta your hand into the dirt Easy as an ice cream sundae Every dancer gets a little hurt Easy as an ice cream sundae Oh, easy as an ice cream sundae ')
dataset = tf.data.Dataset.from_tensors( tf.strings.bytes_split(input_word) )
window_size = 6
dataset = dataset.map( lambda x: tft.sliding_window(x, width=window_size, axis=0) ).flat_map(tf.data.Dataset.from_tensor_slices)
dataset = dataset.batch(1)
list_word = []
label = []
vocab = [ "a", "b", "c", "d", "e", "f", "g", "h", "I", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "_" ]
vocab_hot = [ "ice" ]
layer = tf.keras.layers.StringLookup(vocabulary=vocab)
layer_hot = tf.keras.layers.StringLookup(vocabulary=vocab_hot)
for example in dataset.take(200):
sequences_mapping_string = layer(example[0])
sequences_mapping_string = tf.constant( sequences_mapping_string, shape=(1, 6) )
list_word.append(sequences_mapping_string.numpy())
sequences_mapping_string = tf.reduce_sum(layer_hot( example[0][0] example[0][1] example[0][2] ))
sequences_mapping_string = tf.constant( sequences_mapping_string, shape=(1, 1) )
label.append(sequences_mapping_string.numpy())
list_word = tf.constant(list_word, shape=(200, 1, 6, 1), dtype=tf.int64)
label = tf.constant(label, shape=(200, 1, 1, 1), dtype=tf.int64)
dataset = tf.data.Dataset.from_tensor_slices((list_word, label))
checkpoint_path = "F:\\models\\checkpoint\\" os.path.basename(__file__).split('.')[0] "\\TF_DataSets_01.h5"
checkpoint_dir = os.path.dirname(checkpoint_path)
if not exists(checkpoint_dir) :
os.mkdir(checkpoint_dir)
print("Create directory: " checkpoint_dir)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Class / Definition
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
class MyLSTMLayer( tf.keras.layers.LSTM ):
def __init__(self, units, return_sequences, return_state):
super(MyLSTMLayer, self).__init__( units, return_sequences=True, return_state=False )
self.num_units = units
def build(self, input_shape):
self.kernel = self.add_weight("kernel",
shape=[int(input_shape[-1]),
self.num_units])
def call(self, inputs):
return tf.matmul(inputs, self.kernel)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Callback
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
class custom_callback(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs={}):
if( logs['accuracy'] >= 0.97 ):
self.model.stop_training = True
custom_callback = custom_callback()
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
mycustomlayer = MyLSTMLayer( 64, True, False )
mycustomlayer_2 = MyLSTMLayer( 16, True, False )
model = tf.keras.models.Sequential([
tf.keras.layers.InputLayer(input_shape=(6, 1)),
tf.keras.layers.Embedding(1000, 128, input_length=1),
tf.keras.layers.Reshape(( 6, 128 )),
tf.keras.layers.SpatialDropout1D( rate = 0.4 ),
tf.keras.layers.Conv1D(32, 6, activation="relu"),
tf.keras.layers.MaxPooling1D(strides=1, pool_size=1),
### LSTM
mycustomlayer,
tf.keras.layers.Reshape(( 1, 1, 64 )),
tf.keras.layers.UpSampling2D( size=(4, 4), data_format=None, interpolation='nearest' ),
tf.keras.layers.Conv1D(16, 3, activation="relu"),
tf.keras.layers.Reshape(( 8, 16 )),
tf.keras.layers.MaxPooling1D(),
tf.keras.layers.GlobalMaxPooling1D(),
### LSTM
tf.keras.layers.Reshape(( 1, 16 )),
mycustomlayer_2,
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(128, activation="relu"),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(4),
], name="MyModelClassification")
model.build()
model.summary()
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Optimizer
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
optimizer = tf.keras.optimizers.SGD(
learning_rate=0.000001,
momentum=0.5,
nesterov=True,
name='SGD',
)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Loss Fn
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
lossfn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=False,
reduction=tf.keras.losses.Reduction.AUTO,
name='sparse_categorical_crossentropy'
)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Summary
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model.compile(optimizer=optimizer, loss=lossfn, metrics=['accuracy'])
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: FileWriter
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
if exists(checkpoint_path) :
model.load_weights(checkpoint_path)
print("model load: " checkpoint_path)
input("Press Any Key!")
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit(dataset, batch_size=100, epochs=3, callbacks=[custom_callback] )
model.save_weights(checkpoint_path)
輸出：
Model: "MyModelClassification"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 6, 1, 128) 128000
reshape (Reshape) (None, 6, 128) 0
spatial_dropout1d (SpatialD (None, 6, 128) 0
ropout1D)
conv1d (Conv1D) (None, 1, 32) 24608
max_pooling1d (MaxPooling1D (None, 1, 32) 0
)
my_lstm_layer (MyLSTMLayer) (None, 1, 64) 2048
reshape_1 (Reshape) (None, 1, 1, 64) 0
up_sampling2d (UpSampling2D (None, 4, 4, 64) 0
)
conv1d_1 (Conv1D) (None, 4, 2, 16) 3088
reshape_2 (Reshape) (None, 8, 16) 0
max_pooling1d_1 (MaxPooling (None, 4, 16) 0
1D)
global_max_pooling1d (Globa (None, 16) 0
lMaxPooling1D)
reshape_3 (Reshape) (None, 1, 16) 0
my_lstm_layer_1 (MyLSTMLaye (None, 1, 16) 256
r)
dropout (Dropout) (None, 1, 16) 0
dense (Dense) (None, 1, 128) 2176
flatten (Flatten) (None, 128) 0
dense_1 (Dense) (None, 4) 516
=================================================================
Total params: 160,692
Trainable params: 160,692
Non-trainable params: 0
_________________________________________________________________
Epoch 1/3
2022-10-14 16:33:44.261736: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8100
200/200 [==============================] - 3s 5ms/step - loss: 0.3487 - accuracy: 0.9000
Epoch 2/3
200/200 [==============================] - 1s 5ms/step - loss: 0.2064 - accuracy: 0.9850
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