Tensorflow Cheat Sheet [2025 Updated] - Download PDF

TensorFlow is an open-source powerful library by Google to build machine learning and deep learning models. The huge ecosystem of TensorFlow will make it easier for everyone in developing, training and deployment of scalable AI solutions. TensorFlow cheat sheet helps you on immediate reference to commands, tools, and techniques. Whether you are a beginner or an experienced developer, this guide will streamline your workflow and boost your productivity with TensorFlow.

In this article , Tensor Cheat-Sheet provides a concise overview of key commands and Techniques;

What is TensorFlow?

TensorFlow is a free and open-source machine learning framework developed by Google mainly used to build, train and deploy machine learning and deep learning models. It supports numerous tasks such as image recognition and natural language processing. It is available on both CPU, GPU and TPU without hiccups and the easy-to-use in Keras API.

TensorFlow Cheat-Sheet

A "TensorFlow cheat sheet" is a convenient reference guide giving easy and ready access to key commands, functions and techniques. It forms a useful pocket guide for programmers, data scientists and Machine Learning enthusiasts to make life easier by compressing the main features of core TensorFlow into their workflow.

Download the Cheat-Sheet Here- Tensorflow Cheat-Sheet

Import Tensor-Flow

To import TensorFlow in your python code, use the command

import tensorflow as tf

Basic Operations

TensorFlow offers many basic operations which can be applied to tensors. Below is a summary of common operations that include addition, subtraction, multiplication, division, and reshaping.

Tensors

Tensors are the data structures used in TensorFlow to basically refer to multi-dimensional arrays. These are similar to NumPy arrays but offer some capabilities that are special to machine and deep learning.

Optimizers

Loss Functions

Training and Evaluation

Training and Evaluation are two core processes in TensorFlow that allow building and evaluating machine learning models.

TensorFlow Datasets

The TensorFlow Datasets library is one that provides datasets ready to be used in a machine learning program with TensorFlow or other frameworks. It simplifies the process of downloading, preparing, and loading datasets through `tfds.load()`, returning a `tf.data.Dataset` object.

Saving and Loading Models

You can save and load models in TensorFlow using the model.save() and tf.keras.models.load_model() methods. To save the whole model including architecture, weights, and optimizer state, you would call model.save(my_model).

GPU Utilization

GPU utilization in TensorFlow is very important to optimize the performance of model training. Tools like NVIDIA's nvidia-smi can be used to monitor the GPU metrics. TensorBoard has profiling tools to visualize the GPU performance where you can easily find inefficiencies.

TensorFlow Utilities

TensorFlow offers strong utilities to streamline development in various facts of machine learning and deep learning. The features involved include efficient data handling through `tf.data`, which can easily scale input pipelines, Keras for easy building and training models and some built-in functions involving evaluation metrics, such as accuracy.

TensorFlow Lite

TensorFlow Lite is a lightweight, cross-platform, open-source software framework designed primarily for deploying machine learning models on mobile and edge devices. It powers efficient on-device inference, reducing latency and enhancing user privacy by handling data locally-no server communication is involved.

Hands-on Practice on TensorFlow

Import TensorFlow

import tensorflow as tf
print(tf.__version__)  

Define Tensors

a = tf.constant(5)
b = tf.constant(3)
result = a + b
print("Addition Result:", result.numpy())

Output:

Addition Result: 8

Creating Tensor

tensor = tf.constant([[1, 2], [3, 4]])
print("Tensor:\n", tensor.numpy())

Output:

Tensor:

[[1 2]

[3 4]]

Perform Matrix Operations

matrix1 = tf.constant([[1, 2], [3, 4]])
matrix2 = tf.constant([[2, 0], [1, 2]])
result = tf.matmul(matrix1, matrix2)
print("Matrix Multiplication Result:\n", result.numpy())

Output:

Matrix Multiplication Result:

[[4 4]

[10 8]]

Build Neural Network

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Create a model
model = Sequential([
    Dense(10, activation='relu', input_shape=(2,)),
    Dense(1, activation='sigmoid')
])

# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Display the model summary
model.summary()

Output:

Model: "sequential"

Layer (type) Output Shape Param #

dense (Dense) (None, 10) 30

dense_1 (Dense) (None, 1) 11

Total params: 41

Trainable params: 41

Non-trainable params: 0

Training Model

import numpy as np

# Generate some dummy data
X = np.random.rand(100, 2)
y = np.random.randint(0, 2, size=(100,))

# Train the model
model.fit(X, y, epochs=5, batch_size=10)

Output:

Epoch 1/5

10/10 - 0s 1ms/step - loss: 0.6951 - accuracy: 0.5100

Epoch 2/5

10/10 - 0s 1ms/step - loss: 0.6940 - accuracy: 0.5200

Making Model Prediction

# Predict on new data
test_data = np.random.rand(5, 2)
predictions = model.predict(test_data)
print("Predictions:\n", predictions)

Output:

Predictions:

[[0.54983985]

[0.50234926]

[0.49029356]

[0.52347356]

[0.56718266]]

Save and Load Model

# Save the model
model.save('my_model.h5')

# Load the model
loaded_model = tf.keras.models.load_model('my_model.h5')
print("Model loaded successfully!")

Output:

Model loaded successfully!

Use TensorFlow for Customer Training Loops

# Define a custom training loop
optimizer = tf.keras.optimizers.Adam()
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy()

@tf.function
def train_step(x, y):
    with tf.GradientTape() as tape:
        predictions = model(x, training=True)
        loss = loss_fn(y, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    return loss

# Example loop
for epoch in range(3):
    for i in range(100):  # Assuming batch size 100
        loss = train_step(x_train[i:i+100], y_train[i:i+100])
    print(f"Epoch {epoch + 1}, Loss: {loss.numpy()}")

Output:

Epoch 1, Loss: 2.3095782

Epoch 2, Loss: 2.308771

Epoch 3, Loss: 2.3081365

Conclusion

In summary, the "TensorFlow Cheat Sheet" is really indispensable for any kind of developer working with TensorFlow since it offers streamlined ways to engage with the said library in ways that reduce manual work when setting up tasks including defining tensors as well as constructing their neural networks from scratch just to mention common operations. Whether you're a new machine learning user coming in or an experienced practitioner seeking ready reference this cheat sheet makes the development, debugging and deployment of AI models precise and efficient.