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MNIST NCS Implementation: [Error 5] Toolkit Error: Stage Details Not Supported: fc1/add

Hi Intel,

I am using for generating a model and I got model_inference.meta as described in the blog.
But when I am trying to compile the same, I am getting error as : [Error 5] Toolkit Error: Stage Details Not Supported: fc1/add.

Can you please help me to solve the error

Tensorflow code for generating tensorflow model.:

Copyright 2015 The TensorFlow Authors. All Rights Reserved.


Licensed under the Apache License, Version 2.0 (the "License");

you may not use this file except in compliance with the License.

You may obtain a copy of the License at



Unless required by applicable law or agreed to in writing, software

distributed under the License is distributed on an "AS IS" BASIS,


See the License for the specific language governing permissions and

limitations under the License.


"""A deep MNIST classifier using convolutional layers.
See extensive documentation at

Disable linter warnings to maintain consistency with tutorial.

pylint: disable=invalid-name

pylint: disable=g-bad-import-order

from future import absolute_import
from future import division
from future import print_function

import argparse
import sys
import tempfile

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

FLAGS = None

def deepnn(x):
"""deepnn builds the graph for a deep net for classifying digits.
x: an input tensor with the dimensions (N_examples, 784), where 784 is the
number of pixels in a standard MNIST image.
A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values
equal to the logits of classifying the digit into one of 10 classes (the
digits 0-9). keep_prob is a scalar placeholder for the probability of
# Reshape to use within a convolutional neural net.
# Last dimension is for "features" - there is only one here, since images are
# grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
with tf.name_scope('reshape'):
x_image = tf.reshape(x, [-1, 28, 28, 1])

# First convolutional layer - maps one grayscale image to 32 feature maps.
with tf.name_scope('conv1'):
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

# Pooling layer - downsamples by 2X.
with tf.name_scope('pool1'):
h_pool1 = max_pool_2x2(h_conv1)

# Second convolutional layer -- maps 32 feature maps to 64.
with tf.name_scope('conv2'):
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

# Second pooling layer.
with tf.name_scope('pool2'):
h_pool2 = max_pool_2x2(h_conv2)

# Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
# is down to 7x7x64 feature maps -- maps this to 1024 features.
with tf.name_scope('fc1'):
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

# Dropout - controls the complexity of the model, prevents co-adaptation of
# features.
with tf.name_scope('dropout'):
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# Map the 1024 features to 10 classes, one for each digit
with tf.name_scope('fc2'):
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv = tf.matmul(h_fc1, W_fc2) + b_fc2

return y_conv

def conv2d(x, W):
"""conv2d returns a 2d convolution layer with full stride."""
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
"""max_pool_2x2 downsamples a feature map by 2X."""
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')

def weight_variable(shape):
"""weight_variable generates a weight variable of a given shape."""
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)

def bias_variable(shape):
"""bias_variable generates a bias variable of a given shape."""
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)

def main(_):
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)

# Create the model
#x = tf.placeholder(tf.float32, [None, 784])
x = tf.placeholder(tf.float32, [None, 784], name="input")

# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])

# Build the graph for the deep net
y_conv= deepnn(x)
output = tf.nn.softmax(y_conv, name='output')

with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,
cross_entropy = tf.reduce_mean(cross_entropy)

with tf.name_scope('adam_optimizer'):
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)

graph_location = tempfile.mkdtemp()
print('Saving graph to: %s' % graph_location)
train_writer = tf.summary.FileWriter(graph_location)
saver = tf.train.Saver()

with tf.Session() as sess:
for i in range(5000):
batch = mnist.train.next_batch(50)
if i % 100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x: batch[0], y_: batch[1]})
print('step %d, training accuracy %g' % (i, train_accuracy)){x: batch[0], y_: batch[1]})

print('test accuracy %g' % accuracy.eval(feed_dict={
    x: mnist.test.images, y_: mnist.test.labels}))
graph_location = "."
save_path =, graph_location + "/mnist_model")

if name == 'main':
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str,
help='Directory for storing input data')
FLAGS, unparsed = parser.parse_known_args(), argv=[sys.argv[0]] + unparsed)

And you can download model_inference files from!AioA6iXbzJf_gQxaLQOWszmvMeN1

Please help me to solve the error ASAP. Will be waiting for your reply.



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