A C++/Python neural network toolkit by Google. I am using it for solving general machine-learning problems, and frequently enough that I need notes.

The construction of graphs is more explicit than in Theano, so I find it easier to understand, although this means that you lose the near-python syntax of Theano.

Tensorflow also claims to compile to smartphones etc, although that looks buggy ATM.

- Keras supports tensorflow
*and*Theano as a backend, for comfort and convenience. See below for some notes. - tensorflowslim eases some boring bits.
- tflearn wraps the tensorflow machine in scikit-learn (Although the implementation is not very enlightening, nor the syntax especially clear.)
- sonnet is Deepmind’s tensorflow library and shares with keras layer-like abstractions and some helpers to make recurrent neural nets bearable.

## Debugging

Basic ways:

- Not in mid training? Explicitly fetch, and print (or do whatever you want) using
Session.run()- Tensorboard: Histogram and Image Summary
tf.Print(input, data, message=None, first_n=None, summarize=None, name=None)(link)tf.Assert(condition, data, summarize=None, name=None)(link)Advanced ways:

- Interpose any python codelet in the computation graph
- A step-by-step debugger
tfdbg_: The TensorFlow debugger

### Tensorboard

Tensorboard is a sorta *de facto* debugging standard.
It’s not immediately intuitive; I recommend reading
Li Yin’s explanation.

Minimally, .. code-block:

tensorboard --logdir=path/to/log-directory

or, more usually, .. code-block:

tensorboard --logdir=name1:/path/to/logs/1,name2:/path/to/logs/2 --host=localhost

Projector visualises embeddings:

TensorBoard has a built-in visualizer, called the Embedding Projector, for interactive visualization and analysis of high-dimensional data like embeddings. It is meant to be useful for developers and researchers alike. It reads from the checkpoint files where you save your tensorflow variables. Although it’s most useful for embeddings, it will load any 2D tensor, potentially including your training weights.

## Getting data in

This is a depressingly complex topic; Likely it’s more lines of code than building your actual learning algorithm.

For example, things break differently if

- you are inputting data of variable dimensions via python
(which requires a “feed”, which requires keeping references to a placeholder
`Op`around, and ALWAYS resubmitting the data every time you run an op, even if the data is not required for the current Op), or - Or inputting a
`Variable`(which may also be feeds, just to mess with you, and claim to also be variable dimensions but that never works for me) via C++.

These interact in various different ways that seem irritating, but are probably to do with enabling very large scale data reading workflows, so that you might accidentally solve a problem for Google and they can get your solution for cheap.

Here’s a walk through of some of the details. And here are the manual pages for feeding and queueing

My experience that that stuff is so horribly messy that you should just build different graphs for the estimation and deployment phases of your mode and implement them each according to convenience.

I’m not yet sure how to easily transmit the estimated parameters between graphs in these two separate phases… I’ll make notes about THAT when i come to it.

## (Non-recurrent) convolutions

- CNNs for text classification
- CNN axis ordering is easy to mess up
- The Theano guide to convolutions is superior if you want to work out the actual dimensions your tensors should have. It also gives an intelligible account of how you invert convolutions for decoding.
- The Tensorflow convolution guide is more lackadaisical, but it does get us there:

For the

SAMEpadding, the output height and width are computed as:out_height = ceil(float(in_height) / float(strides[1])) out_width = ceil(float(in_width) / float(strides[2]))For the

VALIDpadding, the output height and width are computed as:out_height = ceil(float(in_height - filter_height + 1) / float(strides[1])) out_width = ceil(float(in_width - filter_width + 1) / float(strides[2]))

Tensorflow’s 4d tensor packing for images?

TensorFlow supportsNHWC(default) andNCHW(cuDNN default). The best practice is to build models that work with bothNCHWandNHWCas it is common to train using NCHW on GPU, and then do inference with NHWC on CPU.

`NCHW` is, to be clear, `(batch, channels, height, width)`.

Theano by contrast, is AFAICT always `NCHW`.

## Recurrent networks

The documentation for these is abysmal.

To write: How to create standard linear filters in Tensorflow.

### Official documentation

The Tensorflow RNN documentation, as bad as it is, is not even easy to find, being scattered across several non-obvious locations without consistent crosslinks.

To make it actually make sense without unwarranted time wasting and guessing, you will then need to read other stuff:

### Community guides

`seq2seq`models with GRUs : Fun with Recurrent Neural Nets.- Variable sequence length HOWTO.
- Where do the RNN weights come from? Magic.
- Denny Britz’s blog posts * RNNs in Tensorflow, a practical guide and undocumented features. * He also gives a good explanation of vanishing gradients.
- Danijar Hafner * Introduction to Recurrent Networks in TensorFlow * Variable sequence lengths HOWTO
- Philippe Remy, Stateful LSTM in Keras
- Ben Bolte, Deep Language Modeling for Question Answering using Keras

## Keras

You probably want to start using a higher level `keras` unless your needs are extraordinarily esoteric or you like reinventing wheels.
Keras is a good choice, since it removes a lot of boilerplate, and makes even writing new boilerplate easier.

- Easing pain via Keras.
- Jason Brownlee’s HOWTO guide.
- Recurrent neural networks are truncated to the sequence length, whcih might not be obvious.

### Useful libraries

recurrentshop makes it easier to manage recurrent topologies using keras.

## Go faster for free

stackoverflow recommends a tweak on the classic source install:

.. code:: shell

./configure bazel build —config=opt //tensorflow/tools/pip_package:build_pip_package bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg pip install /tmp/tensorflow_pkg/tensorflow-1.0.0-py2-none-any.whl

- bazel build -c opt —copt=-mavx —copt=-mavx2 —copt=-mfma —copt=-mfpmath=both —copt=-msse4.2 —config=cuda -k //tensorflow/tools/pip_package:build_pip_package
- bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg pip3 install /tmp/tensorflow_pkg/tensorflow-1.0.0-cp35-cp35m-macosx_10_6_intel.whl

## Getting models out

- For a local app: Hamed MP, Exporting trained TensorFlow models to C++ the RIGHT way!
- For serving it online, Tensorflow
`serving`is the preferred means. See the Serving documentation.

## Doing it in the cloud because you don’t have NVIDIA sponsorship

See practical cloud computing, which has a couple of sections on that.

## Misc HOWTOs

### Gradient tricks

Hessian matrix. There is a discussion on Jacobians in TF, including some fancy examples by jjough:

here’s mine — works for high-dimensional Jacobians (numerator and denominator have >1 dimension), undefined batch sizes, and tensors that are not statically known.

Remember to use an interactive session, otherwise tf.get_default_session() will not be able to find the session.

def tf_jacobian(tensor2, tensor1, feed_dict, sess = tf.get_default_session()): """ Computes the tensor d(tensor2)/d(tensor1) recursively. :param tensor2: numerator of Jacobian :param tensor1: denominator of Jacobian :param feed_dict: input data (need this if tensors are not statically known) :return: a tensor of dimension (dim_tensor2 x dim_tensor1) """ # can't do tensor.get_shape() because it doesn't work for undefined batch size shape = list(sess.run(tf.shape(tensor2), feed_dict)) if shape: # split tensor2 along first dimension and recur # int trick from https://github.com/tensorflow/tensorflow/issues/7754 tensor2_split = tf.split(axis = 0, num_or_size_splits = int(shape[0]), value = tensor2) grad_split = [tf_jacobian(tf.squeeze(M, squeeze_dims = 0), tensor1, feed_dict) for M in tensor2_split] return tf.stack(grad_split) else: # calculate gradient of scalar grad = tf.gradients(tensor2, tensor1) if grad[0] != None: return tf.squeeze(grad, squeeze_dims = [0]) else: # replace any undefined gradients with zeros return tf.zeros_like(tensor1)And here’s one for batched tensors:

def batch_tf_jacobian(tensor2, tensor1, feed_dict, sess = tf.get_default_session()): """ Computes the matrix d(tensor2)/d(tensor1) recursively. Tensorflow doesn't really have its own Jacobian operator (tf.gradients sums over all dims of tensor2). :param tensor2: numerator of Jacobian, first dimension is batch :param tensor1: denominator of Jacobian, first dimension is batch :param feed_dict: input data (need this if tensors are not statically known) :return: batch Jacobian tensor """ shape2 = list(sess.run(tf.shape(tensor2), feed_dict)) shape1 = list(sess.run(tf.shape(tensor1), feed_dict)) jacobian = tf_jacobian(tensor2, tensor1, feed_dict) batch_size = shape2[0] batch_jacobian = [tf.slice(jacobian, [i] + [0]*(len(shape2)-1) + [i] + [0]*(len(shape1)-1), [1] + [-1]*(len(shape2)-1) + [1] + [-1]*(len(shape1)-1)) for i in range(batch_size)] batch_jacobian = [tf.squeeze(tensor, squeeze_dims = (0, len(shape2))) for tensor in batch_jacobian] batch_jacobian = tf.stack(batch_jacobian) return batch_jacobian

### Optimisation tricks

Using traditional/experimental optimisers rather than SGD-type ones.