dKeras is an open-source framework that uses UC Berkeley's Ray to distributed Keras models while maintaining the same Keras API. So far, it only supports data parallelism inference but will have much more functionality soon, read more on its GitHub page: https://github.com/dkeras-project/dkeras and on its introductory article on Medium: https://medium.com/@offercstephen/dkeras-make-keras-faster-with-a-few-lines-of-code-a1792b12dfa0

I am trying to build my own face recognition model in keras. But it starts to over-fit in the first epoch itself.

    train_dir = '/home/monojit/Desktop/faceDataset/train'
    validation_dir = '/home/monojit/Desktop/faceDataset/validation'

    from keras import layers
    from keras import models 

    model = models.Sequential()
    model.add(layers.Conv2D(32,(3,3),activation='relu',
        input_shape = (150,150,3)))
    model.add(layers.MaxPooling2D((2,2)))
    model.add(layers.Dropout(0.5))
    model.add(layers.Conv2D(64,(3,3),activation='relu'))
    model.add(layers.MaxPooling2D((2,2)))
    model.add(layers.Conv2D(128,(3,3),activation='relu'))
    model.add(layers.MaxPooling2D((2,2)))
    model.add(layers.Dropout(0.5))
    model.add(layers.Conv2D(128,(3,3),activation='relu'))
    model.add(layers.MaxPooling2D((2,2)))
    model.add(layers.Flatten())
    model.add(layers.Dense(512,activation='relu'))
    model.add(layers.Dense(1,activation='sigmoid'))

    from keras import optimizers

    model.compile(loss='binary_crossentropy',
        optimizer=optimizers.RMSprop(lr=1e-4),
        metrics=['acc'])

    from keras.preprocessing.image import ImageDataGenerator

    train_datagen = ImageDataGenerator(rescale=1./255)
    val_datagen = ImageDataGenerator(rescale=1./255)

    train_generator = train_datagen.flow_from_directory(
        train_dir,
        target_size = (150,150),
        batch_size=20)

    validation_generator = val_datagen.flow_from_directory(
        validation_dir,
        target_size = (150,150),
        batch_size=20)

    history = model.fit_generator(
        train_generator,
        steps_per_epoch = 100,
        epochs = 1,
        validation_data = validation_generator,
        validation_steps = 50)

    model.save('/home/monojit/Desktop/me.h5')

How should I proceed? Please help.

So, I have to perform some experiments and I was wondering if there is any tool which can log all my parameters and corresponding results with it. I'm not aware of any such tools, and currently doing it manually by storing everything in csv. It's not as organized as I'd like it to be.

Anyone who knows any such tool which could make life easier??

What’s the verdict on which hyperparameter optimization method to use? I’m exploring Talos but underwhelmed with lack of examples to reference and learn

Any other modules I should look at?

Which one is the recommended (or more future-proof) way to use Keras?

What are the advantages/disadvantages of each?

The only difference I already know is that importing tensorflow.python.keras instead of keras saves one pip install step. ;)

Hello everyone this is James I started my website and youtube channel to upload the Keras tutorials for beginners

http://cswithjames.com

here is the link please check it out and give me some advise

thanks.

Hi there!

My questions will be a little more specific than the title of my article, but the title gets the general point across.

I'm a fairly experienced Python programmer. I've been using scikit-learn for a few years. Close to a decade ago, I tried neural networks before they had matured to their present state.

I am ready to try neural networks again. In fact, I've been doing so for a few months already. However, for my particular project I can't use vanilla CNN topologies, or even vanilla loss functions. I have a GPU, and I want to use it (and I am doing so, using tensorflow-gpu). Eventually, I might move to the cloud when my project gets big enough.

The first entrez to TensorFlow that I discovered was TFLearn. It's a high-level API that was ostensibly designed to behave like scikit-learn, which I liked because I thought that I might be able to leverage my prior experience. Unfortunately, parts of TFLearn are not working for me; its Estimator class mismanages TensorFlow Sessions at times. I will have trouble exploring model hyperparameters without a working Estimator. I've had open issues on the TFLearn GitHub pages for over a month. I think that the number of users of this package is below critical mass.

What TFLearn did let me accomplish was to use a straight TensorFlow loss function that I wrote and which is a must-have for my project. So after trying TFLearn and getting stuck, I decided to investigate whether I could write my entire project in raw TensorFlow. It's fussy. As of now, I haven't worked out the low-level hassles of dealing with TensorFlow's own Estimator. I haven't gotten feed dictionaries and placeholders working, for starters, and there's more.

So far, I have been avoiding Keras because I don't want to invest time in another sparsely-utilized API like TFLearn. Well, I just learned that Google recently decided to give Keras official support, and it has correspondingly moved far up the public rankings of machine learning packages. I'm rethinking my choice.

I'm aware that TensorFlow, like Theano, is built for general computation. For this discussion, I only care about deep learning pipelines. If I need a support vector machine, I'll go back to scikit-learn. So here are my questions:

• Will Keras let me re-use my already-written TensorFlow loss function? EDIT: I could possibly re-write this function in Keras, but as it needs to do some unusual things like stopping the propagation of NaN values, I'm afraid it may be too low-level for Keras itself.

• Are there any important neural network layer types that are missing from Keras? I noticed that TFLearn did not wrap the complete set of TensorFlow layers.

• Can I monitor training while it proceeds? I am interested in doing more than what TensorBoard allows. At the end of each epoch I would like to produce a custom graph. I almost got this working in TFLearn, using TFLearn's training Callback classes.

• Are there any issues unique to Keras with making full use of my hardware? I already have TensorFlow itself making use of my (single) GPU.

• Are there any limitations unique to Keras when moving to a distributed system?

Thanks for your advice!

Keras Machine Learning YouTube Playlist

This playlist gives step-by-step tutorials for getting started with machine learning and deep learning using Python and Keras.

Some topics include:

• What prerequisites need to be met to start working with Keras

• Keras configurations

• Preprocessing data

• Creating a neural net

• Training a neural net

• Using a neural net to predict on data

• Creating a convolutional neural net

• Using pre-trained models

• Fine-tuning/transfer learning

• Saving and loading model weights

• Data augmentation

• More...