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Main Tools

  1. Create a directory called workspace_tf somewhere.
  2. Install Sourcetree (optional)
  3. Install Homebrew
  4. Clone this repo (cd ~/path_to/workspace_tf, git clone https://github.com/Knowm/HelloTensorFlow.git)
  5. brew cask install java
  6. brew install python3
  7. brew cask install eclipse-cpp or brew cask reinstall eclipse-cpp

PyDev in Eclipse

  1. Create an eclipse workspace that will be used to import workspace_tf.
  2. Install PyDev in Eclipse. Help ==> Install new Software... Click on Add… then PyDev in Name and http://pydev.org/updates/ in Location. Select PyDev and click through the wizard.
  3. Configure PyDev in Eclipse preferences to point to installed Python executable. Go to Eclipse ==> Preferences ==> PyDev ==> Interpreter - Python Select New, set python3 and /usr/local/bin/python3

Import Python Project into Eclipse (PyDev)

  1. Right click ==> New... ==> Project...
  2. PyDev ==> PyDev Project ==> Next
  3. Uncheck 'Use Default'
  4. Browse to project Directory
  5. Type 'HelloTensorFlow' for Project Name
  6. Click 'Finish'

Test Python in Eclipse

Right-click src/hellopy.py ==> Run As ==> Python Run

if __name__ == '__main__':
    print('Hello World')

Consol Output:

Hello World

Install Tensorflow

    pip3 install --upgrade tensorflow

note: If pip3 has not been installed after executing brew install python3, run:

brew postinstall python3

Test TensorFlow in Eclipse

  1. Right-click src/hellotf.py ==> Run As ==> Python Run
# https://mubaris.com/2017-10-21/tensorflow-101

# Import TensorFlow
import tensorflow as tf

# Define Constant
output = tf.constant("Hello, World")

# To print the value of constant you need to start a session.
sess = tf.Session()

# Print
print(sess.run(output))

# Close the session
sess.close()

2017-11-17 10:33:55.587159: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-11-17 10:33:55.587179: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-11-17 10:33:55.587188: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-11-17 10:33:55.587192: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
b'Hello, World'

Some Possible Issues

  1. AttributeError: module 'enum' has no attribute 'IntFlag' ==> run pip3 uninstall enum34
  2. RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6 ==> Just ignore the warning
  3. Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA ==> You need to compile TF yourself with the appropriate flags to leverage advanced CPU instructions. Just ignore.

MNIST from Tensorflow/models

cd ~/path_to/workspace_tf
git clone https://github.com/tensorflow/models.git
python3 models/tutorials/image/mnist/convolutional.py

Alternatively, import the models project into Eclipse as described above for HelloTensorFlow, right-click tutorials/image/mnist/convolutional.py ==> Run As ==> Python Run.

Cifar-10 from Tensorflow/models

cd ~/path_to/workspace_tf
python3 models/tutorials/image/cifar10/cifar10_train.py

In a different console window:

tensorboard --logdir=/tmp/cifar10_train

Open http://localhost:6006 in browser to view tensorboard.

After training and monitoring on tensorboard:

cd ~/path_to/workspace_tf
python3 models/tutorials/image/cifar10/cifar10_eval.py

should yield a consol output like:

2018-01-15 13:22:36.844078: precision @ 1 = 0.803
2018-01-15 13:27:47.173989: precision @ 1 = 0.803
2018-01-15 13:32:58.397531: precision @ 1 = 0.804
etc

Results on Mac (CPU only)

Device Info
CPU Intel i5 2.9 GHz
RAM Apple 8GB DDR3 1867 MHz 2 core

Running 5000 steps on the CPU took 57 minutes.

Possible Issues

If you receive an error like this:

ValueError: Failed to find file: /tmp/cifar10_data/cifar-10-batches-bin/data_batch_1.bin

it may be due to a corrupted data file after canceling (ctrl+c) a previous run attempt. Delete the /tmp/cifar10_data file and start again. If you can't see the /tmp files, enable viewing of hidden files by:

defaults write com.apple.finder AppleShowAllFiles YES