Tensorflow 2：维度 1 的切片索引 64 超出范围。 [操作：StridedSlice] 名称：caption_generator_5/strided_slice/
Tensorflow 2: slice index 64 of dimension 1 out of bounds. [Op:StridedSlice] name: caption_generator_5/strided_slice/
下面是我的代码，用于为视频序列创建字幕
'''将tensorflow导入为tf
将 tensorflow.keras 导入为 keras
将 numpy 导入为 np
打印（tf.版本）
class 词嵌入（tf.keras.layers.Layer）：
def init(self,n_words,dim_hidden):
super(WordEmbeding, self).init()
self.wordEmbed = self.add_variable(name='wordEmbed', shape=(n_words, dim_hidden), trainable=True)
def build(self, input_shape):
self.wordEmbed.assign(tf.random.uniform(minval=-0.1,maxval=0.1,seed=10,shape=self.wordEmbed.shape,dtype=tf.float32))
def call(self, inputs, **kwargs):
out=tf.nn.embedding_lookup(self.wordEmbed, inputs)
return out
class 字幕生成器（tf.keras.Model）：
def init(self,n_words,batch_size,dim_feature=512,dim_hidden=512,n_video_lstm=80 ,
n_caption_lstm=20,bias_init_vector=None):
super(CaptionGenerator, self).init()
self.n_words=n_words
self.dim_feature=dim_feature
self.dim_hidden=dim_hidden
self.n_video_lstm=n_video_lstm
self.n_caption_lstm=n_caption_lstm
self.batch_size=batch_size
self.wordEmbed = 词嵌入(n_words,dim_hidden)
self.wordEmbed.build(input_shape=(None,))
self.dense_feature=keras.layers.Dense(units=dim_hidden,name='dense_feature')
self.dense_feature.build(input_shape=(None,dim_feature))
self.lstm1=keras.layers.LSTMCell(units=dim_hidden,name='lstm_video')
self.lstm1.build(input_shape=(batch_size,dim_hidden))
self.lstm2=keras.layers.LSTMCell(units=dim_hidden,name='lstm_caption')
self.lstm2.build(input_shape=(batch_size, dim_hidden*2))
self.dense_output=keras.layers.Dense(units=n_words,
name='dense_output')
self.dense_output.build(input_shape=(None,dim_hidden))
if bias_init_vector is not None:
self.dense_output.bias.assign_add(bias_init_vector)
def call(self,X,Y=None,Y_mask=None):
if Y is not None:
return self.train(X,Y,Y_mask) # loss
else:
return self.predict(X) # result
def train(self,X,Y,Y_mask):
self.state1 = self.lstm1.get_initial_state(batch_size=self.batch_size, dtype=tf.float32)
self.state2 = self.lstm2.get_initial_state(batch_size=self.batch_size, dtype=tf.float32)
self.padding = tf.zeros([self.batch_size, self.dim_hidden])
X = tf.reshape(X, shape=(-1, self.dim_feature)) # (batch_size*T,dim_feature)
X = self.dense_feature(X) # (batch_size*T,dim_hidden)
X = tf.reshape(X, shape=(self.batch_size, -1, self.dim_hidden))
# encoding video
losses=0.0
for i in range(self.n_video_lstm):
output1, self.state1 = self.lstm1(X[:, i, :], self.state1)
output2, self.state2 = self.lstm2(tf.concat([output1, self.padding], 1), self.state2)
# decoding
for i in range(self.n_caption_lstm + 1):
with tf.device('cpu:0'):
current_embed = self.wordEmbed(Y[:, i]) # tf.gather
output1, self.state1 = self.lstm1(self.padding, self.state1)
output2, self.state2 = self.lstm2(tf.concat([output1, current_embed], 1), self.state2)
labels=Y[:,i+1]
onehot_labels=tf.one_hot(labels,depth=self.n_words)
logit_words=self.dense_output(output2)
cross_entropy=tf.nn.softmax_cross_entropy_with_logits(labels=onehot_labels,logits=logit_words)
cross_entropy=cross_entropy*Y_mask[:,i]
current_loss=tf.reduce_mean(cross_entropy)
losses+=current_loss
return losses
def predict(self,X):
batch_size=X.shape[0]
self.state1 = self.lstm1.get_initial_state(batch_size=batch_size, dtype=tf.float32)
self.state2 = self.lstm2.get_initial_state(batch_size=batch_size, dtype=tf.float32)
self.padding = tf.zeros([X.shape[0], self.dim_hidden])
X = tf.reshape(X, shape=(-1, self.dim_feature)) # (batch_size*T,dim_feature)
X = self.dense_feature(X) # (batch_size*T,dim_hidden)
X = tf.reshape(X, shape=(batch_size, -1, self.dim_hidden))
# encoding video
for i in range(self.n_video_lstm):
output1, self.state1 = self.lstm1(X[:, i, :], self.state1)
output2, self.state2 = self.lstm2(tf.concat([output1, self.padding], 1), self.state2)
# decoding
generated_words=[]
for i in range(self.n_caption_lstm + 1):
if i==0:
with tf.device('cpu:0'):
current_embed = self.wordEmbed(tf.ones([batch_size],dtype=tf.int64))
output1, self.state1 = self.lstm1(self.padding, self.state1)
output2, self.state2 = self.lstm2(tf.concat([output1, current_embed], 1), self.state2)
logit_words = self.dense_output(output2)
max_prob_index=tf.argmax(logit_words,axis=-1)
with tf.device('cpu:0'):
current_embed=self.wordEmbed(max_prob_index)
generated_words.append(max_prob_index.numpy())
return np.array(generated_words).T'''
我收到以下错误
维度 1 的切片索引 64 超出范围。 [操作：StridedSlice] 名称：caption_generator_5/strided_slice/
我的输入是一个视频特征“.npy”文件，具有 (64,512) 个特征
此行错误：
output1, self.state1 = self.lstm1(X[:, i, :], self.state1)
这里'i'是帧数（从0到79）。但是 self.lstm 的 1 dim 是一个特征（从 0 到 63）。
下面是我的代码，用于为视频序列创建字幕
'''将tensorflow导入为tf 将 tensorflow.keras 导入为 keras 将 numpy 导入为 np 打印（tf.版本）
class 词嵌入（tf.keras.layers.Layer）： def init(self,n_words,dim_hidden): super(WordEmbeding, self).init()
self.wordEmbed = self.add_variable(name='wordEmbed', shape=(n_words, dim_hidden), trainable=True)
def build(self, input_shape):
self.wordEmbed.assign(tf.random.uniform(minval=-0.1,maxval=0.1,seed=10,shape=self.wordEmbed.shape,dtype=tf.float32))
def call(self, inputs, **kwargs):
out=tf.nn.embedding_lookup(self.wordEmbed, inputs)
return out
class 字幕生成器（tf.keras.Model）： def init(self,n_words,batch_size,dim_feature=512,dim_hidden=512,n_video_lstm=80 , n_caption_lstm=20,bias_init_vector=None): super(CaptionGenerator, self).init() self.n_words=n_words self.dim_feature=dim_feature self.dim_hidden=dim_hidden self.n_video_lstm=n_video_lstm self.n_caption_lstm=n_caption_lstm self.batch_size=batch_size self.wordEmbed = 词嵌入(n_words,dim_hidden) self.wordEmbed.build(input_shape=(None,))
self.dense_feature=keras.layers.Dense(units=dim_hidden,name='dense_feature')
self.dense_feature.build(input_shape=(None,dim_feature))
self.lstm1=keras.layers.LSTMCell(units=dim_hidden,name='lstm_video')
self.lstm1.build(input_shape=(batch_size,dim_hidden))
self.lstm2=keras.layers.LSTMCell(units=dim_hidden,name='lstm_caption')
self.lstm2.build(input_shape=(batch_size, dim_hidden*2))
self.dense_output=keras.layers.Dense(units=n_words,
name='dense_output')
self.dense_output.build(input_shape=(None,dim_hidden))
if bias_init_vector is not None:
self.dense_output.bias.assign_add(bias_init_vector)
def call(self,X,Y=None,Y_mask=None):
if Y is not None:
return self.train(X,Y,Y_mask) # loss
else:
return self.predict(X) # result
def train(self,X,Y,Y_mask):
self.state1 = self.lstm1.get_initial_state(batch_size=self.batch_size, dtype=tf.float32)
self.state2 = self.lstm2.get_initial_state(batch_size=self.batch_size, dtype=tf.float32)
self.padding = tf.zeros([self.batch_size, self.dim_hidden])
X = tf.reshape(X, shape=(-1, self.dim_feature)) # (batch_size*T,dim_feature)
X = self.dense_feature(X) # (batch_size*T,dim_hidden)
X = tf.reshape(X, shape=(self.batch_size, -1, self.dim_hidden))
# encoding video
losses=0.0
for i in range(self.n_video_lstm):
output1, self.state1 = self.lstm1(X[:, i, :], self.state1)
output2, self.state2 = self.lstm2(tf.concat([output1, self.padding], 1), self.state2)
# decoding
for i in range(self.n_caption_lstm + 1):
with tf.device('cpu:0'):
current_embed = self.wordEmbed(Y[:, i]) # tf.gather
output1, self.state1 = self.lstm1(self.padding, self.state1)
output2, self.state2 = self.lstm2(tf.concat([output1, current_embed], 1), self.state2)
labels=Y[:,i+1]
onehot_labels=tf.one_hot(labels,depth=self.n_words)
logit_words=self.dense_output(output2)
cross_entropy=tf.nn.softmax_cross_entropy_with_logits(labels=onehot_labels,logits=logit_words)
cross_entropy=cross_entropy*Y_mask[:,i]
current_loss=tf.reduce_mean(cross_entropy)
losses+=current_loss
return losses
def predict(self,X):
batch_size=X.shape[0]
self.state1 = self.lstm1.get_initial_state(batch_size=batch_size, dtype=tf.float32)
self.state2 = self.lstm2.get_initial_state(batch_size=batch_size, dtype=tf.float32)
self.padding = tf.zeros([X.shape[0], self.dim_hidden])
X = tf.reshape(X, shape=(-1, self.dim_feature)) # (batch_size*T,dim_feature)
X = self.dense_feature(X) # (batch_size*T,dim_hidden)
X = tf.reshape(X, shape=(batch_size, -1, self.dim_hidden))
# encoding video
for i in range(self.n_video_lstm):
output1, self.state1 = self.lstm1(X[:, i, :], self.state1)
output2, self.state2 = self.lstm2(tf.concat([output1, self.padding], 1), self.state2)
# decoding
generated_words=[]
for i in range(self.n_caption_lstm + 1):
if i==0:
with tf.device('cpu:0'):
current_embed = self.wordEmbed(tf.ones([batch_size],dtype=tf.int64))
output1, self.state1 = self.lstm1(self.padding, self.state1)
output2, self.state2 = self.lstm2(tf.concat([output1, current_embed], 1), self.state2)
logit_words = self.dense_output(output2)
max_prob_index=tf.argmax(logit_words,axis=-1)
with tf.device('cpu:0'):
current_embed=self.wordEmbed(max_prob_index)
generated_words.append(max_prob_index.numpy())
return np.array(generated_words).T'''
我收到以下错误
维度 1 的切片索引 64 超出范围。 [操作：StridedSlice] 名称：caption_generator_5/strided_slice/
我的输入是一个视频特征“.npy”文件，具有 (64,512) 个特征
此行错误：
output1, self.state1 = self.lstm1(X[:, i, :], self.state1)
这里'i'是帧数（从0到79）。但是 self.lstm 的 1 dim 是一个特征（从 0 到 63）。