TensorFlow

TensorFlow is the famous machine learning library by Google

Install

Install CUDA and CuDNN
Create a conda environment with python 3.5+
- conda create -n my_env python=3.8
Install with pip

Install TF2

Install tensorflow and tensorflow-addons

# Install cuda and cudnn if necessary
conda install cudatoolkit=10.1.243 cudnn=7.6.5

pip install tensorflow tensorflow-addons

Install TF1

Note: You will only need TF1 if working with a TF1 repo.
If migrating your old code, you can install TF2 and use:

import tensorflow.compat.v1 as tf
See TF Guide Migrate

See https://www.tensorflow.org/install/source#linux for a list of compatible Cuda and Cudnn versions.

# Install compatible cuda and cudnn versions.
conda install cudatoolkit=10.0.130 cudnn=7.6.5

# Install tensorflow
pip install tensorflow-gpu==1.15

# Test GPU support
python -c "import tensorflow as tf;print(tf.test.is_gpu_available())"

Usage (TF2)

Here we'll cover usage using TensorFlow 2 which has eager execution.
This is using the Keras API in tensorflow.keras.

Basics

The general pipeline using Keras is:

Define a model, typically using tf.keras.Sequential
Call model.compile
- Here you pass in your optimizer, loss function, and metrics.
Train your model by calling model.fit
- Here you pass in your training data, batch size, number of epochs, and training callbacks
- For more information about callbacks, see Keras custom callbacks.

After training, you can use your model by calling model.evaluate

Custom Models

An alternative way to define a model is by extending the Model class:

Write a python class which extends tf.keras.Model
Implement a forward pass in the call method

See Tensorflow: Custom Layers And Models #Building Models

Custom Training Loop

Reference
While you can train using model.compile and model.fit, using your own custom training loop is much more flexable and easier to understand. You can write your own training loop by doing the following:

my_model= keras.Sequential([
    keras.layers.Dense(400, input_shape=400, activation='relu'),
    keras.layers.Dense(400, activation='relu'),
    keras.layers.Dense(400, activation='relu'),
    keras.layers.Dense(400, activation='relu'),
    keras.layers.Dense(400, activation='relu'),
    keras.layers.Dense(2)
])

optimizer = keras.optimizers.SGD(learning_rate=1e-3)

training_loss = []
validation_loss = []
for epoch in range(100):
    print('Start of epoch %d' % (epoch,))
    for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):
        with tf.GradientTape() as tape:
            guess = my_model(x_batch_train)
            loss_value = my_custom_loss(y_batch_train, guess)

        # Use the gradient tape to automatically retrieve
        # the gradients of the trainable variables with respect to the loss.
        grads = tape.gradient(loss_value, my_model.trainable_weights)

        # Run one step of gradient descent by updating
        # the value of the variables to minimize the loss.
        optimizer.apply_gradients(zip(grads, my_model.trainable_weights))

        # Log every 200 batches.
        if step % 200 == 0:
            print('Training loss at step %s: %s' % (step, float(loss_value)))
        training_loss.append(loss_value)
        guess_validation = model(x_validation)
        validation_loss.append(my_custom_loss(y_validation, guess_validation))

Save and Load Models

Reference

Custom Layers

Extend tf.keras.layer.Layer

ReflectionPadding2D

SO Source

class ReflectionPadding2D(Layer):
    def __init__(self, padding=(1, 1), **kwargs):
        self.padding = tuple(padding)
        self.input_spec = [InputSpec(ndim=4)]
        super(ReflectionPadding2D, self).__init__(**kwargs)

    def compute_output_shape(self, s):
        """ If you are using "channels_last" configuration"""
        return (s[0], s[1] + 2 * self.padding[0], s[2] + 2 * self.padding[1], s[3])

    def call(self, x, mask=None):
        w_pad,h_pad = self.padding
        return tf.pad(x, [[0,0], [h_pad,h_pad], [w_pad,w_pad], [0,0] ], 'REFLECT')

BilinearUpsample

class BilinearUpsample(layers.Layer):

  def __init__(self):
    super().__init__()
    self.input_spec = [keras.layers.InputSpec(ndim=4)]

  def compute_output_shape(self, shape):
      return shape[0], 2 * shape[1], 2 * shape[2], shape[3]

  def call(self, inputs, training=None, mask=None):
    new_height = int(2 * inputs.shape[1])
    new_width = int(2 * inputs.shape[2])
    return tf.image.resize_images(inputs, [new_height, new_width])

Operators

Matrix Multiplication

The two matrix multiplication operators are:

tf.linalg.matmul (also aliased as tf.matmul)
tf.linalg.matvec

New: With both operators, the first \(k-2\) dimensions can now be the batch size.
E.g. If \(A\) is \(b_1 \times b_2 \times 3 \times 3\) and \(B\) is \(b_1 \times b_2 \times 3\), you can multiply them with \(C = \operatorname{tf.linalg.matvec}(A,B)\) and \(C\) will be \(b_1 \times b_2 \times 3\).
Also the batch size in A can be 1 and it will properly broadcast to the same size as \(B\).

Usage (TF1)

Estimators

First Contact w/ TF Estimator (TDS)

Training Statistics

Reference
You can extract the training loss from the events file in tensorflow.

Tensorflow Addons

pip install tensorflow-addons

`tfa.image.interpolate_bilinear`

Reference

This is a bilinear interpolation. It is equivalent to PyTorch's grid_sample.
However, you need to reshape the grid to a nx2 array and make sure indexing='xy' when calling the function.
You can reshape the output back to the dimensions of your original image.

Tensorflow Graphics

pip install tensorflow-graphics --upgrade

You may need to install a static openexr from https://www.lfd.uci.edu/~gohlke/pythonlibs/#openexr.