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problem in my code :'Tensor' object does not support item assignment

1

def build_metric(self):
 with tf.variable_scope('fc', reuse=tf.AUTO_REUSE):
 response_m = self.response
 shape = response_m.get_shape().as_list()[1:3]
 output_list = [] 
 for i in range(shape[0]):
 for j in range(shape[1]):
 t1 = self.instance_embeds[:,i:i+6,j:j+6,:]
 t2 = self.templates
 t1, t2 = logit(t1, t2)
 f = gsml(t1, t2)
 for s in range(8):
 response_m[s, i, j] = f[s]
 output_list.append(f)
 self.response_m = response_m

response_m[s, i, j] = f[s]

TypeError: 'Tensor' object does not support item assignment

what can I do?

python-2.7 tensorflow

share|improve this question

edited Mar 8 at 13:59

Inder

2,15451226

asked Mar 8 at 12:52

ahuxyy_1ahuxyy_1

63

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  self.response is a variable tensor???
  
  – Inder
  Mar 8 at 13:04
  

  
  
  
  

add a comment | 

1

def build_metric(self):
 with tf.variable_scope('fc', reuse=tf.AUTO_REUSE):
 response_m = self.response
 shape = response_m.get_shape().as_list()[1:3]
 output_list = [] 
 for i in range(shape[0]):
 for j in range(shape[1]):
 t1 = self.instance_embeds[:,i:i+6,j:j+6,:]
 t2 = self.templates
 t1, t2 = logit(t1, t2)
 f = gsml(t1, t2)
 for s in range(8):
 response_m[s, i, j] = f[s]
 output_list.append(f)
 self.response_m = response_m

response_m[s, i, j] = f[s]

TypeError: 'Tensor' object does not support item assignment

what can I do?

python-2.7 tensorflow

share|improve this question

edited Mar 8 at 13:59

Inder

2,15451226

asked Mar 8 at 12:52

ahuxyy_1ahuxyy_1

63

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  self.response is a variable tensor???
  
  – Inder
  Mar 8 at 13:04
  

  
  
  
  

add a comment | 

def build_metric(self):
 with tf.variable_scope('fc', reuse=tf.AUTO_REUSE):
 response_m = self.response
 shape = response_m.get_shape().as_list()[1:3]
 output_list = [] 
 for i in range(shape[0]):
 for j in range(shape[1]):
 t1 = self.instance_embeds[:,i:i+6,j:j+6,:]
 t2 = self.templates
 t1, t2 = logit(t1, t2)
 f = gsml(t1, t2)
 for s in range(8):
 response_m[s, i, j] = f[s]
 output_list.append(f)
 self.response_m = response_m

response_m[s, i, j] = f[s]

TypeError: 'Tensor' object does not support item assignment

what can I do?

python-2.7 tensorflow

share|improve this question

edited Mar 8 at 13:59

Inder

2,15451226

asked Mar 8 at 12:52

ahuxyy_1ahuxyy_1

63

def build_metric(self):
 with tf.variable_scope('fc', reuse=tf.AUTO_REUSE):
 response_m = self.response
 shape = response_m.get_shape().as_list()[1:3]
 output_list = [] 
 for i in range(shape[0]):
 for j in range(shape[1]):
 t1 = self.instance_embeds[:,i:i+6,j:j+6,:]
 t2 = self.templates
 t1, t2 = logit(t1, t2)
 f = gsml(t1, t2)
 for s in range(8):
 response_m[s, i, j] = f[s]
 output_list.append(f)
 self.response_m = response_m

share|improve this question

edited Mar 8 at 13:59

Inder

2,15451226

asked Mar 8 at 12:52

ahuxyy_1ahuxyy_1

63

share|improve this question

edited Mar 8 at 13:59

Inder

2,15451226

asked Mar 8 at 12:52

ahuxyy_1ahuxyy_1

63

edited Mar 8 at 13:59

Inder

2,15451226

edited Mar 8 at 13:59

Inder

2,15451226

asked Mar 8 at 12:52

ahuxyy_1ahuxyy_1

63

asked Mar 8 at 12:52

ahuxyy_1ahuxyy_1

63

1 Answer
1

active

oldest

votes

0

Assuming that your response variable is a tensorflow variable:

You can use assing for this purpose:

def build_metric(self):
 with tf.variable_scope('fc', reuse=tf.AUTO_REUSE):
 response_m = self.response
 shape = response_m.get_shape().as_list()[1:3]
 output_list = [] 
 for i in range(shape[0]):
 for j in range(shape[1]):
 t1 = self.instance_embeds[:,i:i+6,j:j+6,:]
 t2 = self.templates
 t1, t2 = logit(t1, t2)
 f = gsml(t1, t2)
 for s in range(8):
 response_m=tf.assign(response[s,i,j],f[s]) #change I have made

 output_list.append(f)
 self.response_m = response_m

A simpler example for understanding can be:

one=tf.Variable(tf.zeros(shape=[1,10]))
with tf.Session() as sess:
 sess.run(tf.global_variables_initializer())
 print(sess.run(one),"n")

new_one=tf.assign(one[0,2],0.33) #using index to assign values
with tf.Session() as sess_2:
 sess_2.run(tf.global_variables_initializer()) #initialize variables with zero values
 print(sess_2.run(new_one))

The output of the code will be:

[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]] 

[[0. 0. 0.33 0. 0. 0. 0. 0. 0. 0. ]]

share|improve this answer

answered Mar 8 at 14:05

InderInder

2,15451226

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @ahuxyy_1 not sure I follow what you are trying to say kindly consider adding in question if this is a code and explain what is the issue with the suggestions I have made
  
  – Inder
  Mar 8 at 15:51
  

  
  
  
  

add a comment | 

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0

Assuming that your response variable is a tensorflow variable:

You can use assing for this purpose:

def build_metric(self):
 with tf.variable_scope('fc', reuse=tf.AUTO_REUSE):
 response_m = self.response
 shape = response_m.get_shape().as_list()[1:3]
 output_list = [] 
 for i in range(shape[0]):
 for j in range(shape[1]):
 t1 = self.instance_embeds[:,i:i+6,j:j+6,:]
 t2 = self.templates
 t1, t2 = logit(t1, t2)
 f = gsml(t1, t2)
 for s in range(8):
 response_m=tf.assign(response[s,i,j],f[s]) #change I have made

 output_list.append(f)
 self.response_m = response_m

A simpler example for understanding can be:

one=tf.Variable(tf.zeros(shape=[1,10]))
with tf.Session() as sess:
 sess.run(tf.global_variables_initializer())
 print(sess.run(one),"n")

new_one=tf.assign(one[0,2],0.33) #using index to assign values
with tf.Session() as sess_2:
 sess_2.run(tf.global_variables_initializer()) #initialize variables with zero values
 print(sess_2.run(new_one))

The output of the code will be:

[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]] 

[[0. 0. 0.33 0. 0. 0. 0. 0. 0. 0. ]]

share|improve this answer

answered Mar 8 at 14:05

InderInder

2,15451226

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @ahuxyy_1 not sure I follow what you are trying to say kindly consider adding in question if this is a code and explain what is the issue with the suggestions I have made
  
  – Inder
  Mar 8 at 15:51
  

  
  
  
  

add a comment | 

0

Assuming that your response variable is a tensorflow variable:

You can use assing for this purpose:

def build_metric(self):
 with tf.variable_scope('fc', reuse=tf.AUTO_REUSE):
 response_m = self.response
 shape = response_m.get_shape().as_list()[1:3]
 output_list = [] 
 for i in range(shape[0]):
 for j in range(shape[1]):
 t1 = self.instance_embeds[:,i:i+6,j:j+6,:]
 t2 = self.templates
 t1, t2 = logit(t1, t2)
 f = gsml(t1, t2)
 for s in range(8):
 response_m=tf.assign(response[s,i,j],f[s]) #change I have made

 output_list.append(f)
 self.response_m = response_m

A simpler example for understanding can be:

one=tf.Variable(tf.zeros(shape=[1,10]))
with tf.Session() as sess:
 sess.run(tf.global_variables_initializer())
 print(sess.run(one),"n")

new_one=tf.assign(one[0,2],0.33) #using index to assign values
with tf.Session() as sess_2:
 sess_2.run(tf.global_variables_initializer()) #initialize variables with zero values
 print(sess_2.run(new_one))

The output of the code will be:

[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]] 

[[0. 0. 0.33 0. 0. 0. 0. 0. 0. 0. ]]

share|improve this answer

answered Mar 8 at 14:05

InderInder

2,15451226

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @ahuxyy_1 not sure I follow what you are trying to say kindly consider adding in question if this is a code and explain what is the issue with the suggestions I have made
  
  – Inder
  Mar 8 at 15:51
  

  
  
  
  

add a comment | 

Assuming that your response variable is a tensorflow variable:

You can use assing for this purpose:

def build_metric(self):
 with tf.variable_scope('fc', reuse=tf.AUTO_REUSE):
 response_m = self.response
 shape = response_m.get_shape().as_list()[1:3]
 output_list = [] 
 for i in range(shape[0]):
 for j in range(shape[1]):
 t1 = self.instance_embeds[:,i:i+6,j:j+6,:]
 t2 = self.templates
 t1, t2 = logit(t1, t2)
 f = gsml(t1, t2)
 for s in range(8):
 response_m=tf.assign(response[s,i,j],f[s]) #change I have made

 output_list.append(f)
 self.response_m = response_m

A simpler example for understanding can be:

one=tf.Variable(tf.zeros(shape=[1,10]))
with tf.Session() as sess:
 sess.run(tf.global_variables_initializer())
 print(sess.run(one),"n")

new_one=tf.assign(one[0,2],0.33) #using index to assign values
with tf.Session() as sess_2:
 sess_2.run(tf.global_variables_initializer()) #initialize variables with zero values
 print(sess_2.run(new_one))

The output of the code will be:

[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]] 

[[0. 0. 0.33 0. 0. 0. 0. 0. 0. 0. ]]

share|improve this answer

answered Mar 8 at 14:05

InderInder

2,15451226

def build_metric(self):
 with tf.variable_scope('fc', reuse=tf.AUTO_REUSE):
 response_m = self.response
 shape = response_m.get_shape().as_list()[1:3]
 output_list = [] 
 for i in range(shape[0]):
 for j in range(shape[1]):
 t1 = self.instance_embeds[:,i:i+6,j:j+6,:]
 t2 = self.templates
 t1, t2 = logit(t1, t2)
 f = gsml(t1, t2)
 for s in range(8):
 response_m=tf.assign(response[s,i,j],f[s]) #change I have made

 output_list.append(f)
 self.response_m = response_m

one=tf.Variable(tf.zeros(shape=[1,10]))
with tf.Session() as sess:
 sess.run(tf.global_variables_initializer())
 print(sess.run(one),"n")

new_one=tf.assign(one[0,2],0.33) #using index to assign values
with tf.Session() as sess_2:
 sess_2.run(tf.global_variables_initializer()) #initialize variables with zero values
 print(sess_2.run(new_one))

[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]] 

[[0. 0. 0.33 0. 0. 0. 0. 0. 0. 0. ]]

share|improve this answer

answered Mar 8 at 14:05

InderInder

2,15451226

answered Mar 8 at 14:05

InderInder

2,15451226

answered Mar 8 at 14:05

InderInder

2,15451226