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

conda install tensorflow-gpu
pip install tensorflow-addons

• If you prefer, you can install only cuda and cudnn from conda:
  • See https://www.tensorflow.org/install/source#linux for a list of compatible Cuda and Cudnn versions.
  • conda search cudatoolkit to which versions of cuda available
  • Download cudnn and copy the binaries to the environment's Library/bin/ directory.

Install TF1

conda install tensorflow-gpu=1.15

Notes

• Conda will automatically install a compatible cuda and cudnn into the cuda environment. Your host OS only needs to have a sufficiently new version of nvidia drivers installed.
• Sometimes, I get CUDNN_STATUS_INTERNAL_ERROR. This is fixed by setting the environment variable TF_FORCE_GPU_ALLOW_GROWTH=true in my conda env. See Add env variables to conda env

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:

import tensorflow as tf
from tensorflow import keras

my_model = keras.Sequential([
    keras.Input(shape=(400,)),
    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

Expand

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')

Expand

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)

In TF1, you first build a computational graph by chaining commands with placeholders and constant variables.
Then, you execute the graph in a tf.Session().

Expand

TF1 MNIST Example

import tensorflow as tf
from tensorflow import keras
import numpy as np

NUM_EPOCHS = 10
BATCH_SIZE = 64

(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
rng = np.random.default_rng()

classification_model = keras.Sequential([
    keras.Input(shape=(28, 28, 1)),
    keras.layers.Conv2D(16, 3, padding="SAME"),
    keras.layers.ReLU(),
    keras.layers.Conv2D(16, 3, padding="SAME"),
    keras.layers.ReLU(),
    keras.layers.Flatten(),
    keras.layers.Dense(10, activation='relu'),
])
x_in = tf.compat.v1.placeholder(dtype=tf.float32, shape=(None, 28, 28, 1))
logits = classification_model(x_in)
gt_classes = tf.compat.v1.placeholder(dtype=tf.int32, shape=(None,))
loss = tf.losses.softmax_cross_entropy(tf.one_hot(gt_classes, 10), logits)
optimizer = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(loss)

with tf.compat.v1.Session() as sess:
    sess.run(tf.compat.v1.global_variables_initializer())
    global_step = 0
    for epoch in range(NUM_EPOCHS):
        x_count = x_train.shape[0]
        image_ordering = rng.choice(range(x_count), x_count, replace=False)
        current_idx = 0
        while current_idx < x_count:
            my_indices = image_ordering[current_idx:min(current_idx + BATCH_SIZE, x_count)]
            x = x_train[my_indices]
            x = x[:, :, :, None] / 255
            logits_val, loss_val, _ = sess.run((logits, loss, optimizer), {
                x_in: x,
                gt_classes: y_train[my_indices]
            })
            if global_step % 100 == 0:
                print("Loss", loss_val)

            current_idx += BATCH_SIZE
            global_step += 1

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.