CODEWITHSUNDEEP
pythontensorflow
Amit Kumar
Amit Kumar

Reputation: 2745

Tensorflow : Shape mismatch issue with one dimensional data

I am trying to pass x_data as feed_dict but getting below error, I am not sure that what is wrong in the code.

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'x_12' with dtype int32 and shape [1000]
     [[Node: x_12 = Placeholder[dtype=DT_INT32, shape=[1000], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

My Code:

import tensorflow as tf
import numpy as np
model = tf.global_variables_initializer()
#define x and y
x = tf.placeholder(shape=[1000],dtype=tf.int32,name="x")
y = tf.Variable(5*x**2-3*x+15,name = "y")
x_data = tf.pack(np.random.randint(0,100,size=1000))
print(x_data)
print(x)
with tf.Session() as sess:
    sess.run(model)
    print(sess.run(y,feed_dict={x:x_data}))

I checked the shape of the x and x_data and it is same

Tensor("pack_8:0", shape=(1000,), dtype=int32)
Tensor("x_14:0", shape=(1000,), dtype=int32)

I am working with one dimensional data. Any help is appreciated, Thanks!

Upvotes: 2

Views: 672

Answers (1)

agold
agold

Reputation: 6276

To make it work I have changed two things, first I changed y to be a Tensor. And secondly I have not changed the x_data to Tensor, as commented here:

The optional feed_dict argument allows the caller to override the value of tensors in the graph. Each key in feed_dict can be one of the following types:

If the key is a Tensor, the value may be a Python scalar, string, list, or numpy ndarray that can be converted to the same dtype as that tensor. Additionally, if the key is a placeholder, the shape of the value will be checked for compatibility with the placeholder.

The changed code which works for me:

import tensorflow as tf
import numpy as np
model = tf.global_variables_initializer()
#define x and y
x = tf.placeholder(shape=[1000],dtype=tf.int32,name="x")
y = 5*x**2-3*x+15 # without tf.Variable, making it a tf.Tensor
x_data = np.random.randint(0,100,size=1000) # without tf.pack
print(x_data)
print(x)
with tf.Session() as sess:
    sess.run(model)
    print(sess.run(y,feed_dict={x:x_data}))

Upvotes: 1

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