CODEWITHSUNDEEP
pythontensorflowdeep-learning
piccolo
piccolo

Reputation: 2217

TensorFlow not recognising feed_dict input

I am running a simple neural network for linear regression. However TensorFlow is complaining that my feed_dict placeholder(s) are not an element of the graph. However my placeholders and my model are all defined within my graph as can be seen below:

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Dense

with tf.Graph().as_default():
    x = tf.placeholder(dtype=tf.float32, shape = (None,4))
    y = tf.placeholder(dtype=tf.float32, shape = (None,4))

    model = tf.keras.Sequential([
        Dense(units=4, activation=tf.nn.relu)
    ])
    y = model(x)

    loss = tf.reduce_mean(tf.square(y-x))
    train_op = tf.train.AdamOptimizer().minimize(loss)

with tf.Session() as sess:
    sess.run(train_op, feed_dict = {x:np.ones(dtype='float32', shape=(4)),
                                    y:5*np.ones(dtype='float32', shape=(4,))})

This gives an error:

TypeError: Cannot interpret feed_dict key as Tensor: Tensor 
Tensor("Placeholder:0", shape=(?, 4), dtype=float32) is not an element of this graph.

____________UPDATE________________

Following the advice from @Silgon and @Mcangus, I have modified the code:

g= tf.Graph()
with g.as_default():
    x = tf.placeholder(dtype=tf.float32, shape = (None,4))

    model = tf.keras.Sequential([
        Dense(units=4, activation=tf.nn.relu)
    ])
    y = model(x)

    loss = tf.reduce_mean(tf.square(y-x))
    train_op = tf.train.AdamOptimizer().minimize(loss)

    init_op = tf.group(tf.global_variables_initializer(),
                     tf.local_variables_initializer())
with tf.Session(graph=g) as sess:
    sess.run(init_op)
    for i in range(5):
        _ , answer = sess.run([train_op,loss], feed_dict = {x:np.ones(dtype='float32', shape=(1,4)),
                                                        y:5*np.ones(dtype='float32', shape=(1,4))})
        print(answer)

However the model doesn't appear to be learning:

16.0
16.0
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16.0

Upvotes: 0

Views: 191

Answers (2)

silgon
silgon

Reputation: 7211

The error tells you that the variable is not an element of the graph. It might be because it's not in the same scope. One way to solve it is to have a structure like the following.

# define a graph
graph = tf.Graph()
with graph.as_default():
    # placeholder
    x = tf.placeholder(...)
    y = tf.placeholder(...)
    # create model
    model = create_model(x, w, b)

with tf.Session(graph=graph) as sess:
    # initialize all the variables
    sess.run(init)

Also, as @Mcangus points out, be careful with the definition of your variables.

Upvotes: 2

McAngus
McAngus

Reputation: 1856

I believe your issue is this line:

y = model(x)

You overwrite y with the output of your model so it's no longer a placeholder.

Upvotes: 1

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