If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
In Course 2 of the deeplearning. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models.
This new deeplearning. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization. A very comprehensive and easy to learn course on Tensor Flow. I am really impressed by the Instructor ability to teach difficult concept with ease. I will look forward another course of this series. Very clear explanation on the concepts at the higher level and practical application of it is discussed, demonstrated and also the exercises are of the same way.
You will just love learning this way. In the first course in this specialization, you had an introduction to TensorFlow, and how, with its high level APIs you could do basic image classification, an you learned a little bit about Convolutional Neural Networks ConvNets. In this course you'll go deeper into using ConvNets will real-world data, and learn about techniques that you can use to improve your ConvNet performance, particularly when doing image classification!
In Week 1, this week, you'll get started by looking at a much larger dataset than you've been using thus far: The Cats and Dogs dataset which had been a Kaggle Challenge in image classification!
Loupe Copy. Training with the cats vs. Convolutional Neural Networks in TensorFlow. Course 2 of 4 in the TensorFlow in Practice Specialization.
Enroll for Free. From the lesson. A conversation with Andrew Ng Working through the notebook Fixing through cropping In this tutorial, we're going to be running through taking raw images that have been labeled for us already, and then feeding them through a convolutional neural network for classification.
We've got the data, but we can't exactly just stuff raw images right through our convolutional neural network. First, we need all of the images to be the same size, and then we also will probably want to just grayscale them. Also, the labels of "cat" and "dog" are not useful, we want them to be one-hot arrays.
Interestingly, we may be approaching a time when our data might not need to be all the same size. Introduction to deep learning with neural networks. Introduction to TensorFlow. Intro to Convolutional Neural Networks. Convolutional Neural Network in TensorFlow tutorial. Finally, I will be making use of TFLearn. Once you have TensorFlow installed, do pip install tflearn. Now, our first order of business is to convert the images and labels to array information that we can pass through our network.
To do this, we'll need a helper function to convert the image name to an array. Our images are labeled like "cat. Now, we can build another function to fully process the training images and their labels into arrays:. The tqdm module was introduced to me by one of my viewers, it's a really nice, pretty, way to measure where you are in a process, rather than printing things out at intervals Super neat.
When we've gone through all of the images, we shuffle them, then save. Shuffle modifies a variable in place, so there's no need to re-define it here.
With this function, we will both save, and return the array data. This way, if we just change the neural network's structure, and not something with the images, like image size.
Cats vs Dogs App
While we're here, we might as well also make a function to process the testing data. This is the actual competition test data, NOT the data that we'll use to check the accuracy of our algorithm as we test. This data has no label.Last Updated on October 3, The Dogs vs. Cats dataset is a standard computer vision dataset that involves classifying photos as either containing a dog or cat. Although the problem sounds simple, it was only effectively addressed in the last few years using deep learning convolutional neural networks.
While the dataset is effectively solved, it can be used as the basis for learning and practicing how to develop, evaluate, and use convolutional deep learning neural networks for image classification from scratch. This includes how to develop a robust test harness for estimating the performance of the model, how to explore improvements to the model, and how to save the model and later load it to make predictions on new data.
In this tutorial, you will discover how to develop a convolutional neural network to classify photos of dogs and cats. Discover how to build models for photo classification, object detection, face recognition, and more in my new computer vision bookwith 30 step-by-step tutorials and full source code.
The dogs vs cats dataset refers to a dataset used for a Kaggle machine learning competition held in The dataset is comprised of photos of dogs and cats provided as a subset of photos from a much larger dataset of 3 million manually annotated photos. The dataset was developed as a partnership between Petfinder. Asirra is easy for users; user studies indicate it can be solved by humans This classifier is a combination of support-vector machine classifiers trained on color and texture features extracted from images.
The Kaggle competition provided 25, labeled photos: 12, dogs and the same number of cats. Predictions were then required on a test dataset of 12, unlabeled photographs. The competition was won by Pierre Sermanet currently a research scientist at Google Brain who achieved a classification accuracy of about The dataset is straightforward to understand and small enough to fit into memory.
The dataset can be downloaded for free from the Kaggle website, although I believe you must have a Kaggle account. Download the dataset by visiting the Dogs vs. Unzip the file and you will see train. Unzip the train. The file naming convention is as follows:.
Looking at a few random photos in the directory, you can see that the photos are color and have different shapes and sizes. We can update the example and change it to plot cat photos instead; the complete example is listed below. We can also see a photo where the cat is barely visible bottom left corner and another that has two cats lower right corner. This suggests that any classifier fit on this problem will have to be robust. The photos will have to be reshaped prior to modeling so that all images have the same shape.
Dogs vs. Cats: Image Classification with Deep Learning using TensorFlow in Python
This is often a small square image. There are many ways to achieve this, although the most common is a simple resize operation that will stretch and deform the aspect ratio of each image and force it into the new shape. We could load all photos and look at the distribution of the photo widths and heights, then design a new photo size that best reflects what we are most likely to see in practice. Smaller inputs mean a model that is faster to train, and typically this concern dominates the choice of image size.
If we want to load all of the images into memory, we can estimate that it would require about 12 gigabytes of RAM. We could load all of the images, reshape them, and store them as a single NumPy array.
This could fit into RAM on many modern machines, but not all, especially if you only have 8 gigabytes to work with. We can write custom code to load the images into memory and resize them as part of the loading process, then save them ready for modeling. The label is also determined for each photo based on the filenames.This blog introduced a transfer-learning strategy to use ImageNet data for a pre-trained network and then using this semi-trained model to identify cats from dogs.
The original dataset with pictures of 12, cats and 12, dogs were obtained from Kaggle Dogs vs. Cats Redux. This framework for this project, building up with Keras with Tensorflow backend, were revised from a similar solution using ResNet50 and the official guide of Keras.
We concluded that the number of images for cats and dogs were generally equal and the number of graphs in each group is above 10, This helps prevent overfitting and helps the model generalize better.
A generator were used to generate images from disk filesystem with a given batch-size. This was suggested by the Official Guide of Keraswhere:.
Building up the model, freezing the convolutionary layers with downloaded parameters trained using ImageNet and just train the bottom layers. Define the loss function as crossentropy and optimizer as adadelta. Training the model for 50 epoches and save the best one with the lowest loss cost for validation sets.
Toggle navigation Boqiang Hu Studio. My Cat Vs Dog solution using deep learning with transfer learning strategy. My Cat Vs Dog solution using deep learning with transfer learning strategy This blog introduced a transfer-learning strategy to use ImageNet data for a pre-trained network and then using this semi-trained model to identify cats from dogs. This framework for this project, building up with Keras with Tensorflow backend, were revised from a similar solution using ResNet50 and the official guide of Keras 1.
A Volatile Uncorr. Back to top.Convnet works by abstracting image features from the detail to higher level elements. An analogy can be described with the way how humans think. Each of us knows how airplane looks, but most likely when thinking about airplane we are not thinking about every little bit of airplane structure.
In a similar way, convnet learns to recognize higher level elements in the image and this helps to classify new images when they look similar to the ones used for the training. Image classification model should be trained using this notebook you will find a description there from where to download image dataset with cats and dogs images.
Model is being used and classification prediction is invoked in this notebook. I was running notebook in Jupyter Docker image, a path to image dataset should be updated refer to code example in my GitHub repoyou should use Docker configured path as the root to fetch dataset images from the disk:.
Model is built using a subset of data:. First model training attempt is done directly using available images from the dataset. Convnet trains to identify cats vs dogs using Keras and TensorFlow backend.
But overfitting happens during early iterations. To fight with overfitting, more training data is supplied by applying a data augmentation technique. Augmentation process allows generating more training data from existing data, by altering existing data.
Random transformations are applied to adjust the existing image and create multiple images out of one refer to the source from Deep Learning with Python book.
After data augmentation convnet trains better by far — validation quality stays very close to the training quality:. Image classification based on convnet model is done in endpoint notebook. A good practice is save trained model and later re-open it for classification task:.
I will be testing model with our dog images. I will be using 11 pictures, all are uploaded to the GitHub repo along with Python notebooks. First picture:. Using the code from Deep Learning with Python book to transform the image into the format to be sent to model. It might be useful to display a transformed image of x pixels:. We repeat the same steps calling model. Summary: convnet was trained on a small dataset and still it can offer fantastic classification results verified with my dog pictures :.
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Write the first response. More From Medium. More from Towards Data Science.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.
If nothing happens, download the GitHub extension for Visual Studio and try again. After downloading the datasets from Kaggle website, you need to extract these two zips. Actually, I just extract train. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Sign up. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. Introduction This repository is for kaggle Dogs vs. Cats match, but you can utilize this code to learn how to use keras.
For optimizer, only Adam and SGD are illustrated in my repository. Environment Python 3. VGGNet: python demo. InceptionNet: python demo. The follow directories will be created automatically.This tutorial shows how to classify cats or dogs from images. It builds an image classifier using a tf. Sequential model and load data using tf.
You will get some practical experience and develop intuition for the following concepts:. Let's start by importing the required packages. The os package is used to read files and directory structure, NumPy is used to convert python list to numpy array and to perform required matrix operations and matplotlib. Begin by downloading the dataset. This tutorial uses a filtered version of Dogs vs Cats dataset from Kaggle. After extracting its contents, assign variables with the proper file path for the training and validation set.
For convenience, set up variables to use while pre-processing the dataset and training the network. Format the images into appropriately pre-processed floating point tensors before feeding to the network:. Fortunately, all these tasks can be done with the ImageDataGenerator class provided by tf. It can read images from disk and preprocess them into proper tensors. It will also set up generators that convert these images into batches of tensors—helpful when training the network.
Visualize the training images by extracting a batch of images from the training generator—which is 32 images in this example—then plot five of them with matplotlib.
The next function returns a batch from the dataset. Discard the labels to only visualize the training images. The model consists of three convolution blocks with a max pool layer in each of them. There's a fully connected layer with units on top of it that is activated by a relu activation function.
For this tutorial, choose the ADAM optimizer and binary cross entropy loss function. To view training and validation accuracy for each training epoch, pass the metrics argument.
View all the layers of the network using the model's summary method:. Also, the difference in accuracy between training and validation accuracy is noticeable—a sign of overfitting. When there are a small number of training examples, the model sometimes learns from noises or unwanted details from training examples—to an extent that it negatively impacts the performance of the model on new examples. This phenomenon is known as overfitting. It means that the model will have a difficult time generalizing on a new dataset.
There are multiple ways to fight overfitting in the training process.
In this tutorial, you'll use data augmentation and add dropout to our model. Overfitting generally occurs when there are a small number of training examples. One way to fix this problem is to augment the dataset so that it has a sufficient number of training examples. Data augmentation takes the approach of generating more training data from existing training samples by augmenting the samples using random transformations that yield believable-looking images.
The goal is the model will never see the exact same picture twice during training. This helps expose the model to more aspects of the data and generalize better. Implement this in tf. Pass different transformations to the dataset and it will take care of applying it during the training process.
Begin by applying random horizontal flip augmentation to the dataset and see how individual images look like after the transformation.
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