You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [?,784] for MNIST dataset

Question

Here is the example I am testing on MNIST dataset for quantization. I am testing my model using below code:

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.python.framework import graph_util
from tensorflow.core.framework import graph_pb2
import numpy as np 


def test_model(model_file,x_in):
    with tf.Session() as sess:
        with open(model_file, "rb") as f:
            output_graph_def = graph_pb2.GraphDef()
            output_graph_def.ParseFromString(f.read())
            _ = tf.import_graph_def(output_graph_def, name="")
        x = sess.graph.get_tensor_by_name('Placeholder_1:0')
        y = sess.graph.get_tensor_by_name('softmax_cross_entropy_with_logits:0')
        new_scores = sess.run(y, feed_dict={x:x_in.test.images})
        print((orig_scores - new_scores) < 1e-6)
        find_top_pred(orig_scores)
        find_top_pred(new_scores)

#print(epoch_x.shape)
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)
test_model('mnist_cnn1.pb',mnist)

I am not getting where I am providing incorrect values. Here I have added the complete track of error code. Below is the error:

Extracting /tmp/data/train-images-idx3-ubyte.gz
Extracting /tmp/data/train-labels-idx1-ubyte.gz
Extracting /tmp/data/t10k-images-idx3-ubyte.gz
Extracting /tmp/data/t10k-labels-idx1-ubyte.gz
Traceback (most recent call last):
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1323, in _do_call
    return fn(*args)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1302, in _run_fn
    status, run_metadata)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/errors_impl.py", line 473, in __exit__
    c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [?,784]
     [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[?,784], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "tmp.py", line 26, in <module>
    test_model('/home/shringa/tensorflowdata/mnist_cnn1.pb',mnist)
  File "tmp.py", line 19, in test_model
    new_scores = sess.run(y, feed_dict={x:x_in.test.images})
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 889, in run
    run_metadata_ptr)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1120, in _run
    feed_dict_tensor, options, run_metadata)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1317, in _do_run
    options, run_metadata)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1336, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [?,784]
     [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[?,784], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

Caused by op 'Placeholder', defined at:
  File "tmp.py", line 26, in <module>
    test_model('/home/shringa/tensorflowdata/mnist_cnn1.pb',mnist)
  File "tmp.py", line 16, in test_model
    _ = tf.import_graph_def(output_graph_def, name="")
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 316, in new_func
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/importer.py", line 411, in import_graph_def
    op_def=op_def)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 3069, in create_op
    op_def=op_def)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1579, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [?,784]
     [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[?,784], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

As shown above I am using mnist_cnn1.pb file to extract my model and to test it on mnist test images but it is throwing error of shape of the placeholder.

Below shown is my cnn model:

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)
print(type(mnist));

n_classes = 10
batch_size = 128

x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32)

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding= 'SAME')

def maxpool2d(x):
    #                           size of window      movement of window
    return tf.nn.max_pool(x, ksize =[1,2,2,1], strides= [1,2,2,1], padding = 'SAME')

def convolutional_network_model(x):
    weights = {'W_conv1':tf.Variable(tf.random_normal([5,5,1,32])),
    'W_conv2':tf.Variable(tf.random_normal([5,5,32,64])),
    'W_fc':tf.Variable(tf.random_normal([7*7*64,1024])),
    'out':tf.Variable(tf.random_normal([1024, n_classes]))}

    biases = {'B_conv1':tf.Variable(tf.random_normal([32])),
    'B_conv2':tf.Variable(tf.random_normal([64])),
    'B_fc':tf.Variable(tf.random_normal([1024])),
    'out':tf.Variable(tf.random_normal([n_classes]))}

    x = tf.reshape(x, shape=[-1,28,28,1])
    conv1 =  conv2d(x, weights['W_conv1'])
    conv1 =  maxpool2d(conv1)

    conv2 =  conv2d(conv1, weights['W_conv2'])
    conv2 =  maxpool2d(conv2) 

    fc =tf.reshape(conv2,[-1,7*7*64])
    fc = tf.nn.relu(tf.matmul(fc, weights['W_fc'])+ biases['B_fc'])

    output =  tf.matmul(fc, weights['out']+biases['out'])

    return output

def train_neural_network(x):
    prediction = convolutional_network_model(x)
    # OLD VERSION:
    #cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) )
    # NEW:
    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits_v2(logits=prediction, labels=y) )
    optimizer = tf.train.AdamOptimizer().minimize(cost)

    hm_epochs = 25
    with tf.Session() as sess:
        # OLD:
        #sess.run(tf.initialize_all_variables())
        # NEW:
        sess.run(tf.global_variables_initializer())

        for epoch in range(hm_epochs):
            epoch_loss = 0
            for _ in range(int(mnist.train.num_examples/batch_size)):
                epoch_x, epoch_y = mnist.train.next_batch(batch_size)
                _, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y}) 
                epoch_loss += c

            print('Epoch', epoch, 'completed out of',hm_epochs,'loss:',epoch_loss)

        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))

        accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
        print('Accuracy:',accuracy.eval({x:mnist.test.images, y:mnist.test.labels}))

train_neural_network(x)

and by using bazel I have created mnist_cnn1.pb file:

python3 tensorflow/tools/quantization/quantize_graph.py   --input=/home/shringa/tensorflowdata/mnist_cnn.pb  --output=/home/shringa/tensorflowdata/mnist_cnn1.pb   --output_node_names=softmax_cross_entropy_with_logits  --mode=eightbit
bazel-bin/tensorflow/tools/graph_transforms/summarize_graph --in_graph=/home/shringa/tensorflowdata/mnist_cnn1.pb

Answer 1

Reason

The cause of your problem is that you didn't give names to your variables / nodes and, as a result, got confused.

When you define the placeholders:

x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32)

... x and y get assigned the following names by tensorflow:

Tensor("Placeholder:0", shape=(?, 784), dtype=float32)  <-- x
Tensor("Placeholder_1:0", dtype=float32)                <-- y

As a result, on test time, the following line pulls the wrong node:

x = sess.graph.get_tensor_by_name('Placeholder_1:0')  # this is y!

That's why tensorflow complains about not feeding the placeholder: it needs x, not y.

Solution

Make it explicit:

x = tf.placeholder(tf.float32, [None, 784], name='x')
y = tf.placeholder(tf.float32, name='y')
...
x = sess.graph.get_tensor_by_name('x')

I'd also provide the name to softmax_cross_entropy_with_logits op as well to make all inference nodes easily accessible.