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NCAppZoo: https://developer.movidius.com/examples

Neural Compute blog: https://developer.movidius.com/blog

Getting Started: https://developer.movidius.com/start

NCSDK documentation: https://developer.movidius.com/docs

Neural Compute SDK: https://developer.movidius.com/repo

NCAppZoo: https://developer.movidius.com/examples

Neural Compute blog: https://developer.movidius.com/blog

Hi Intel,

I am using https://movidius.github.io/ncsdk/tf_compile_guidance.html 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.:

#

#

#

"""A deep MNIST classifier using convolutional layers.

See extensive documentation at

https://www.tensorflow.org/get_started/mnist/pros

"""

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.

Args:

x: an input tensor with the dimensions (N_examples, 784), where 784 is the

number of pixels in a standard MNIST image.

Returns:

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

dropout.

"""

# 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_,

logits=y_conv)

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)

train_writer.add_graph(tf.get_default_graph())

saver = tf.train.Saver()

with tf.Session() as sess:

sess.run(tf.global_variables_initializer())

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))

train_step.run(feed_dict={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 = saver.save(sess, graph_location + "/mnist_model")
```

if **name** == '**main**':

parser = argparse.ArgumentParser()

parser.add_argument('--data_dir', type=str,

default='/tmp/tensorflow/mnist/input_data',

help='Directory for storing input data')

FLAGS, unparsed = parser.parse_known_args()

tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

And you can download model_inference files from https://1drv.ms/f/s!AioA6iXbzJf_gQxaLQOWszmvMeN1

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

Thanks-in-advance.

## Comments

7 Commentssorted by Votes Date Added0Vote UpVote DownHi @saikrishnaTheGreat

Which Neural Compute SDK (NCSDK) are you using? I was able to compile your model into a graph with the NCSDK v2.08.

`mvNCCompile mnist_inference.meta -s 12 -in input -on output -o mnist_inference.graph`

Also, there is an example in the Neural Compute App Zoo (NCAPPZOO) for hand written digits 0 - 9.

https://github.com/movidius/ncappzoo/tree/master/tensorflow/mnist

Regards,

Jesus

0Vote UpVote DownHi Jesus,

Still same problem. NCSDK version is : "mvNCCompile v02.00, Copyright @ Intel Corporation 2017"

And when I use mnist from ncappzoo, same problem I got.

Regards,

Sai Krishna.

0Vote UpVote Down@Jesus_at_Intel ,

FYI, I am using NCSDK v2.10.0.1

0Vote UpVote DownHi @saikrishnaTheGreat

You are correct, I ran into the same issue with the NCSDK v2.10. We have opened a ticket with the development team to take a look.

In the meantime, I recommend using the NCSDK v2.08 as it doesn't seem to have the same issue.

Regards,

Jesus

0Vote UpVote DownHi,

Is there any updating for this issue?

Yung-Sheng Lu

0Vote UpVote DownHi @yungshenglu

There are no updates to this issue, the workaround was to use the Intel® Movidius™ Neural Compute SDK version 2.08. Due to the Intel® Movidius™ Neural Compute Stick discontinuation there are no further software updates planned for the Intel® Movidius™ Neural Compute SDK.

Take a look at the discontinuation notice for additional information:

https://www.intel.com/content/www/us/en/support/articles/000033257/boards-and-kits/neural-compute-sticks.html

Regards,

Jesus

0Vote UpVote DownHi @Jesus_at_Intel ,

I have already used Neural Compute SDK version 2.08 but still meet the same problem. Is there any help?

Thanks.

Yung-Sheng Lu