This Tensorflow Jupyter Notebook Example shows how to use Tensorflow to classify images of handwriten digits.
For more information check out our video:
Introduction to TensorFlow and Jupyter Notebook
This is a simple example of how to create a TensorFlow graph in Jupyter Notebook.
import tensorflow as tf
graph = tf.Graph()
# This is our “input” data, one dimensional for simplicity
x = tf.placeholder(tf.float32, shape=[None])
y = tf.placeholder(tf.float32, shape=[None])
# This is our model: y = x^2
y_pred = tf.multiply(x, x)
# This is our loss function: mean squared error
loss = tf.reduce_mean(tf.square(y – y_pred))
# This is our optimizer: gradient descent with a learning rate of 0.1
# This tells the optimizer to minimize the loss function by adjusting x
train_step = optimizer.minimize(loss)
With this simple graph, we can now run training iterations and see the results:
Setting up TensorFlow and Jupyter Notebook
This guide will show you how to set up TensorFlow and Jupyter Notebook on your machine. You will need to have Python 3 installed on your system.
First, you will need to install TensorFlow. We recommend using virtualenv to do this:
virtualenv – system-site-packages -p python3 ~/tensorflow
source ~/tensorflow/bin/activate # Activate the virtual environment
pip install – upgrade tensorflow==1.12.0 # Install TensorFlow 1.12.0
Now that TensorFlow is installed, we can verify that it is working by running the following code in a Jupyter Notebook:
import tensorflow as tf
tf.test.is_gpu_available() # Returns True if TensorFlow can access a GPU, False otherwise
A simple TensorFlow and Jupyter Notebook example
This is a simple example of how to use TensorFlow and Jupyter Notebook together.
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More complex TensorFlow and Jupyter Notebook examples
As you become more familiar with TensorFlow and Jupyter Notebook, you’ll want to explore more complex examples. In this section, we’ll show you some more advanced TensorFlow and Jupyter Notebook examples, including:
– training a model using a custom dataset
– using TensorBoard to visualize your results
– using GPUs to accelerate training
TensorFlow and Jupyter Notebook for machine learning
TensorFlow is an open-source software library for data analysis and machine learning. Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. In this tutorial, we will show you how to use TensorFlow and Jupyter Notebook to classify images of hand-written digits.
TensorFlow and Jupyter Notebook for deep learning
TensorFlow is a powerful tool for deep learning, and the Jupyter Notebook is a great platform for working with TensorFlow. In this example, we’ll show you how to get started with TensorFlow and Jupyter Notebook.
First, we’ll install TensorFlow. You can do this using pip:
pip install tensorflow
Once TensorFlow is installed, we can import it into our Jupyter Notebook:
import tensorflow as tf
Now, we’re ready to start working with TensorFlow. We can create a simple graph to add two numbers:
a = tf.constant(2) # a is a constant tensor (2-dimensional array) with value 2 b = tf.constant(3) # b is also a constant tensor with value 3 c = a + b # c stores the result of adding a and b print(c) # prints the value of c (5)
Now, let’s run our graph:
TensorFlow and Jupyter Notebook for data analysis
TensorFlow is a powerful tool for data analysis and machine learning. Jupyter Notebook is a great platform for interactive data analysis and machine learning. In this example, we will use TensorFlow and Jupyter Notebook to build a simple machine learning model to predict the age of a person based on their height and weight.
First, we need to import the required libraries. We will use the pandas library for data manipulation and analysis, the matplotlib library for data visualization, and the seaborn library for statistical data visualization.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
Next, we need to import the dataset that we will be using for this example. We will be using the ‘ height-weight’ dataset from Kaggle (https://www.kaggle.com/mustafaali96/height-weight). This dataset contains information on height and weight of over 32,000 people.
# Import dataset
df = pd.read_csv(“height-weight.csv”) # your path may vary depending on where you saved the dataset
# Let’s take a look at the first five rows of our dataset: df.head() # show first five rows of our dataset
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Keyword: Tensorflow Jupyter Notebook Example