This Tensorflow Jupyter Notebook Example shows how to use Tensorflow to classify images of handwriten digits.

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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()

with graph.as_default():

# 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

optimizer =tf.train.GradientDescentOptimizer(learning_rate=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.

“`python

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.

“`python

# Import dataset

df = pd.read_csv(“height-weight.csv”) # your path may vary depending on where you saved the dataset

“`

“`python

# 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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## TensorFlow and Jupyter Notebook for image processing

TensorFlow is a powerful tool for image processing. In this article, we’ll show you how to use TensorFlow and Jupyter Notebook to process images.

Jupyter Notebook is a popular application that enables you to create and share documents that contain live code, equations, visualizations, and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code.

The following example shows how to use TensorFlow and Jupyter notebook for image processing. We’ll take a look at how to load image data using TensorFlow and keras package into our notebook environment. We’ll also explore some basic image processing methods using the OpenCV package.

## TensorFlow and Jupyter Notebook for natural language processing

TensorFlow and Jupyter Notebook are popular tools for natural language processing (NLP). In this example, we’ll use them to train a simple NLP model to classify movie reviews as positive or negative.

To get started, you’ll need to install TensorFlow and Jupyter Notebook. We recommend using Anaconda, a popular Python distribution that includes both of these tools.

Once you have Anaconda installed, you can create a new Jupyter Notebook by opening the Anaconda Navigator and selecting “New > Python 3”.

In your new notebook, you’ll need to import TensorFlow:

import tensorflow as tf

If everything went well, you should now be able to run TensorFlow operations in your notebook. To test this, try running the following code:

tf.constant(‘Hello, TensorFlow!’)

## TensorFlow and Jupyter Notebook for time series analysis

TensorFlow is a powerful tool for deep learning, and the Jupyter Notebook is a great platform for working with TensorFlow. In this tutorial, we’ll show you how to use TensorFlow and the Jupyter Notebook to perform time series analysis.

First, we’ll need to import some libraries:

import matplotlib.pyplot as plt

import numpy as np

import tensorflow as tf

from tensorflow.contrib import learn

from tensorflow.contrib.learn.python import SKCompat

tf.logging.set_verbosity(tf.logging.ERROR) %matplotlib inline

Now, we’ll load some data:

Index | Height | Weight | |
---|---|---|---|

(0) | (0) 0 | (1) 72… |

Keyword: Tensorflow Jupyter Notebook Example