Best TensorFlow Books to Buy in October 2025
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- MASTER ML PROJECTS END-TO-END WITH SCIKIT-LEARN EXPERTISE.
- EXPLORE POWERFUL MODELS: SVMS, TREES, FORESTS, AND ENSEMBLES.
- BUILD ADVANCED NEURAL NETS USING TENSORFLOW AND KERAS TOOLS.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition
TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow: From Neural Networks (CNN, DNN, GNN, RNN, ANN, LSTM, GAN) to Natural Language Processing (NLP)
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models
TensorFlow in Action
Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition
To print the shape of a tensor in TensorFlow, you can use the TensorFlow session to run the tensor and then use the shape attribute to access the shape of the tensor. Here is an example code snippet that demonstrates how to print the shape of a tensor in TensorFlow:
import tensorflow as tf
Create a sample tensor
tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
Start a TensorFlow session
with tf.Session() as sess: # Run the tensor and get the shape shape = sess.run(tf.shape(tensor))
Print the shape of the tensor
print(shape)
In this code snippet, we first create a sample tensor using the tf.constant function. Then we start a TensorFlow session using the tf.Session() context manager. Inside the session, we run the tensor using sess.run(tf.shape(tensor)) to get the shape of the tensor. Finally, we print the shape of the tensor using the print function.
How to view tensor shape in tensorflow?
In TensorFlow, you can view the shape of a tensor using the .shape attribute. Here is an example of how to view the shape of a tensor in TensorFlow:
import tensorflow as tf
Create a tensor
tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
View the shape of the tensor
print(tensor.shape)
This will output the shape of the tensor, which in this case is (2, 3) indicating that the tensor has 2 rows and 3 columns.
What is the technique to retrieve tensor shape in tensorflow?
In TensorFlow, you can retrieve the shape of a tensor using the tf.shape() function.
Here is an example code snippet that demonstrates how to retrieve the shape of a tensor in TensorFlow:
import tensorflow as tf
Create a tensor
tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
Get the shape of the tensor
tensor_shape = tf.shape(tensor)
Print the shape of the tensor
print(tensor_shape)
This will output the shape of the tensor as a TensorFlow tensor object. If you want to retrieve the shape as a numpy array, you can do so by evaluating the tensor shape using a TensorFlow session:
import tensorflow as tf
Create a tensor
tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
Get the shape of the tensor
tensor_shape = tf.shape(tensor)
Start a TensorFlow session
with tf.Session() as sess: # Evaluate the tensor shape shape = sess.run(tensor_shape)
Print the shape as a numpy array
print(shape)
This will output the shape of the tensor as a numpy array.
What is the function to print tensor shape in tensorflow?
In TensorFlow, you can print the shape of a tensor using the shape attribute. Here's an example:
import tensorflow as tf
Create a tensor
tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
Print the shape of the tensor
print(tensor.shape)
This will output the shape of the tensor, which in this case is (2, 3) indicating that it is a 2-dimensional tensor with 2 rows and 3 columns.
How to retrieve tensor shape in tensorflow?
In TensorFlow, you can retrieve the shape of a tensor using the tf.shape() function. Here is an example code snippet demonstrating how to do this:
import tensorflow as tf
Create a tensor
tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
Retrieve the shape of the tensor
tensor_shape = tf.shape(tensor)
Create a TensorFlow session
with tf.Session() as sess: # Run the session to get the shape shape = sess.run(tensor_shape) print(shape)
In this code snippet, we first create a tensor using tf.constant(), then use tf.shape() to retrieve the shape of the tensor. Finally, we run a TensorFlow session to compute and print out the shape of the tensor.
What is the technique to get tensor size in tensorflow?
To get the size of a tensor in TensorFlow, you can use the shape attribute of the tensor object. Here is an example:
import tensorflow as tf
Create a tensor
tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
Get the shape of the tensor
tensor_size = tf.size(tensor)
Create a TensorFlow session
with tf.Session() as sess: print(sess.run(tensor_size))
This will output the total number of elements in the tensor, which in this case is 6.
How to determine tensor shape in tensorflow?
In TensorFlow, you can determine the shape of a tensor by using the tf.shape() function. This function returns the shape of the input tensor as a 1-D integer tensor.
Here is an example code snippet that demonstrates how to determine the shape of a tensor in TensorFlow:
import tensorflow as tf
Define a tensor
tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
Get the shape of the tensor
shape = tf.shape(tensor)
Create a TensorFlow session
with tf.Session() as sess: print("Shape of the tensor: ", sess.run(shape))
When you run this code snippet, it will print out the shape of the tensor [2 3], indicating that the tensor has 2 rows and 3 columns.