Introduction

Welcome back to our ongoing series, "Learn TensorFlow for Machine Learning," where we delve deeper into the fascinating world of machine learning using the TensorFlow framework. In our previous article, we introduced the fundamentals of TensorFlow and its importance in the field of artificial intelligence and machine learning. Today, we will continue our journey by exploring some more topics that will further enhance your understanding and skills in TensorFlow.

In the world of machine learning, artificial intelligence, and data science, TensorFlow has emerged as a powerful framework for building and training models. While TensorFlow can be used interactively like any Python library, it also offers additional tools for performance optimization and model export. One such tool is tf.function, which allows developers to separate pure-TensorFlow code from Python code, enabling enhanced performance and exportability.

Understanding Graphs and tf.function

In TensorFlow, a graph represents a computational flow that defines the operations and dependencies between them. By constructing a graph, TensorFlow can optimize the execution of operations, making it more efficient than traditional Python execution. This optimization is particularly beneficial for complex and computationally intensive tasks commonly encountered in machine learning.

The tf.function decorator plays a crucial role in leveraging the benefits of graphs. When applied to a Python function, tf.function performs a process called tracing. During tracing, the function is executed in Python, but TensorFlow captures the computations and constructs an optimized graph representation of the operations performed within the function.

Performance Optimization with tf.function

To illustrate the concept of graph construction and performance optimization, consider the following code snippet:

@tf.function
def my_func(x):
  print('Tracing.\n')
  return tf.reduce_sum(x)

When you run this code for the first time, it doesn't produce any output. However, behind the scenes, TensorFlow traces the function and creates an optimized graph for future executions. Let's see the effect of this optimization by executing the function with a specific input:

x = tf.constant([31, 1, 4])
my_func(x)

Code Output

Tracing.
<tf.Tensor: shape=(), dtype=int32, numpy=36>

Here, the function is traced, and the optimized graph is utilized to compute the sum of the elements in the tensor x. It is important to note that the first execution involves the tracing process, which may take a bit longer due to graph construction. However, subsequent executions will benefit from the optimized graph, resulting in faster computation.

Reusability and Signature Sensitivity

While the optimization provided by the tf.function is advantageous, it is crucial to understand that the generated graph may not be reusable for inputs with a different signature, such as varying shapes or data types. In such cases, TensorFlow generates a new graph specifically tailored to the input signature.

Let's modify our previous example and observe the behavior of tf.function when presented with a different input:

x = tf.constant([8.0, 11.8, 14.3], dtype=tf.float32)
my_func(x)

Code Output

Tracing.
<tf.Tensor: shape=(), dtype=float32, numpy=34.1>

As you can see, even though the function is the same, TensorFlow generates a new graph to accommodate the input's different data types and shapes. This signature sensitivity ensures accurate computation and prevents potential errors that could arise from incompatible inputs.

Managing Variables and Functions in TensorFlow

Modules and tf.Module

In TensorFlow, tf.Module is a class that allows you to manage tf.Variable objects and the corresponding tf.function objects. It facilitates two crucial functionalities:

1. Variable Saving and Restoration: You can save and restore the values of variables using tf.train.Checkpoint. This is especially useful during training to save and restore a model's state efficiently.

2. Importing and Exporting: With tf.saved_model, you can import and export the values of tf.Variable objects and their associated tf.function graphs. This enables running a model independently of the Python program that created it, enhancing its portability.

An example showcasing the usage of tf.Module is as follows:

class MyModule(tf.Module):
  def __init__(self, value):
    self.weight = tf.Variable(value)

  @tf.function
  def multiply(self, x):
    return x * self.weight

Here, we define a subclass of tf.Module named MyModule, which includes a variable weight and a tf.function called multiply. The multiply function performs element-wise multiplication between the input x and the weight variable.

Let's see the code in action:

mod = MyModule(3)
mod.multiply(tf.constant([5, 6, 3]))

Code Output

<tf.Tensor: shape=(3,), dtype=int32, numpy=array([15, 18,  9], dtype=int32)>

In this example, we instantiate MyModule with an initial value of 3 and invoke the multiply function on a tensor. The output represents the element-wise multiplication between the tensor and the weight variable.

Saving and Loading Modules

Once you have defined and utilized a tf.Module object, you can save it for future use or deployment. To save the module, you can employ tf.saved_model.save() and specify the desired save path:

save_path = './saved'
tf.saved_model.save(mod, save_path)

The resulting SavedModel is independent of the code that created it. It can be loaded from Python, other language bindings, TensorFlow Serving, or even converted to run with TensorFlow Lite or TensorFlow.js.

To load and utilize the saved module, you can use tf.saved_model.load():

reloaded = tf.saved_model.load(save_path)
reloaded.multiply(tf.constant([5, 6, 3]))

Code Output

<tf.Tensor: shape=(3,), dtype=int32, numpy=array([15, 18,  9], dtype=int32)>

Layers and Models: Building and Training Complex Models

Built on top of tf.Module, TensorFlow provides two higher-level classes: tf.keras.layers.Layer and tf.keras.Model. These classes offer additional functionality and convenience methods for constructing, training, and saving complex models.

tf.keras.layers.Layer serves as a base class for implementing custom layers. It provides a structure to define layers with trainable weights and can be used to build custom architectures or extend existing ones.

Extending tf.keras.layers.Layer, tf.keras.Model adds methods for training, evaluation, and model saving. It is commonly used as a container for multiple layers, enabling the definition and management of complex neural network architectures.

By leveraging the capabilities of tf.keras.layers.Layer and tf.keras.Model, you can create advanced models, train them on extensive datasets, and save their configurations and weights for future use or deployment.

• In conclusion: The concepts of modules, layers, and models in TensorFlow are crucial for effectively managing variables and functions. They provide mechanisms for saving and restoring variable values, exporting models for deployment, and constructing complex architectures. Utilizing these features empowers you to develop powerful and portable machine learning models that can be easily shared, reused, and deployed across different environments.

Let's Build a Model: Training a Basic Model from Scratch

Now, we will put together the concepts of Tensors, Variables, Automatic Differentiation, Graphs, tf.function, Modules, Layers, and Models to build a basic machine learning model from scratch using TensorFlow. This part is dedicated to anyone who wants to learn and understand the process of building and training a model.

Note: Remember as said earlier this and the previous articles are just an overview, let us dig deeper into each topics in coming articles.

Generating Example Data

To begin, let's create some example data. We will generate a cloud of points that roughly follows a quadratic curve. We'll use the Matplotlib library to visualize the data.

import matplotlib
from matplotlib import pyplot as plt

matplotlib.rcParams['figure.figsize'] = [10, 6]

x = tf.linspace(-3, 3, 201)
x = tf.cast(x, tf.float32)

def f(x):
  y = x**2 + 2*x - 8
  return y

y = f(x) + tf.random.normal(shape=[201])

plt.plot(x.numpy(), y.numpy(), '.', label='Data')
plt.plot(x, f(x), label='Ground truth')
plt.legend();

Code Output

Creating a Quadratic Model

Next, we'll define a quadratic model with randomly initialized weights and a bias. The model will take an input x and predict the corresponding output using the equation y = w_q * x^2 + w_l * x + b, where w_q represents the weight for the quadratic term, w_l represents the weight for the linear term, and b represents the bias term.

class Model(tf.Module):
  def __init__(self):
    rand_init = tf.random.uniform(shape=[3], minval=0., maxval=5., seed=22)
    self.w_q = tf.Variable(rand_init[0])
    self.w_l = tf.Variable(rand_init[1])
    self.b = tf.Variable(rand_init[2])

  @tf.function
  def __call__(self, x):
    return self.w_q * (x**2) + self.w_l * x + self.b

quad_model = Model()

We also define a function plot_preds that helps visualize the model predictions.

def plot_preds(x, y, f, model, title):
  plt.figure()
  plt.plot(x, y, '.', label='Data')
  plt.plot(x, f(x), label='Ground truth')
  plt.plot(x, model(x), label='Predictions')
  plt.title(title)
  plt.legend()

Let's plot the initial predictions of the model:

plot_preds(x, y, f, quad_model, 'Initial Predictions')

Code Output

Defining the Loss Function

Since the model aims to predict continuous values, we will use the mean squared error (MSE) as the loss function. The MSE calculates the mean of the squared differences between the predicted values and the ground truth.

def mse_loss(y_pred, y):
  return tf.reduce_mean(tf.square(y_pred - y))

Training the Model

We will now write a basic training loop to train the model from scratch. The loop will use the MSE loss function and its gradients with respect to the input to update the model's parameters. We will use mini-batches for training, which provides memory efficiency and faster convergence. The tf.data.Dataset API is used for batching and shuffling the data.

batch_size = 32
dataset = tf.data.Dataset.from_tensor_slices((x, y))
dataset = dataset.shuffle(buffer_size=x.shape[0]).batch(batch_size)


epochs = 150
learning_rate = 0.01
losses = []

for epoch in range(epochs):
  for x_batch, y_batch in dataset:
    with tf.GradientTape() as tape:
      batch_loss = mse_loss(quad_model(x_batch), y_batch)
    grads = tape.gradient(batch_loss, quad_model.variables)
    for g,v in zip(grads, quad_model.variables):
        v.assign_sub(learning_rate*g)

  loss = mse_loss(quad_model(x), y)
  losses.append(loss)
  if epoch % 10 == 0:
    print(f'Mean squared error for step {epoch}: {loss.numpy():0.3f}')

plt.plot(range(epochs), losses)
plt.xlabel("Epoch")
plt.ylabel("Mean Squared Error (MSE)")
plt.title('MSE loss vs training iterations')

Code Output

Mean squared error for step 0: 35.056
Mean squared error for step 10: 11.213
Mean squared error for step 20: 4.120
Mean squared error for step 30: 1.982
Mean squared error for step 40: 1.340
Mean squared error for step 50: 1.165
Mean squared error for step 60: 1.118
Mean squared error for step 70: 1.127
Mean squared error for step 80: 1.087
Mean squared error for step 90: 1.087
Mean squared error for step 100: 1.098
Mean squared error for step 110: 1.094
Mean squared error for step 120: 1.086
Mean squared error for step 130: 1.089
Mean squared error for step 140: 1.088

Evaluating the Trained Model

Let's observe the performance of the trained model:

plot_preds(x, y, f, quad_model, 'Predictions after Training')

Code Output

Utilizing tf.keras for Training

While implementing a training loop from scratch is educational, TensorFlow's tf.keras module provides convenient utilities for training models. We can use the Model.compile and Model.fit methods to simplify the training process.

To demonstrate this, we'll create a Sequential model using tf.keras.Sequential. We'll use the dense layer (tf.keras.layers.Dense) to learn linear relationships and a lambda layer (tf.keras.layers.Lambda) to transform the input for capturing the quadratic relationship.

new_model = tf.keras.Sequential([
    tf.keras.layers.Lambda(lambda x: tf.stack([x, x**2], axis=1)),
    tf.keras.layers.Dense(units=1, kernel_initializer=tf.random.normal)
])

new_model.compile(
    loss=tf.keras.losses.MSE,
    optimizer=tf.keras.optimizers.SGD(learning_rate=0.01)
)

history = new_model.fit(x, y,
                        epochs=150,
                        batch_size=32,
                        verbose=0)

new_model.save('./my_new_model')

Let's visualize the training progress:

plt.plot(history.history['loss'])
plt.xlabel('Epoch')
plt.ylim([0, max(plt.ylim())])
plt.ylabel('Loss [Mean Squared Error]')
plt.title('Keras training progress')

Code Output

Finally, we can evaluate the performance of the Keras model:

plot_preds(x, y, f, new_model, 'Predictions after Training')

Code Output

Conclusion

In this article, we built a basic machine learning model from scratch using TensorFlow. We started by generating example data, created a quadratic model, defined a loss function, and trained the model using a training loop. We also explored utilizing the tf.keras module for a more convenient training process. By understanding these concepts, you can now begin building and training your own machine-learning models using TensorFlow.

This is not the end of this series, this was just an overview of how we can make use of the TensorFlow framework to ease our model-building in Machine Learning. In upcoming articles, we will dive deeper into every topic break them into parts, and clearly understand.