![]() There are number of caveats for getting this performance boost. Tf.functions takes a given native python function and autographs it onto the TensorFlow execution graph.This gives a performance boost over using a traditional python function which would have to use a context switch and not take advantage of graph optimizations. The tf.function decorator is one of the most interesting tools to come in TensorFlow 2.0. We won't be using the Keras model fit method here to show how custom training loops work with tf.functions and distributed training. With the generator and discriminator models created, the last step to get training is to build our training loop. Generator_optimizer = tf.(1e-3)ĭiscriminator_optimizer = tf.(1e-3) # create the models and optimizers for later functions Model = tf.keras.Model(inputs=inputs, outputs=out) Inputs = inputs # reorder the list to be smallest resolution first # every additional input gets its own conv layer then appended # for the first input we don't have features to append to # we have multiple inputs to make a real/fake decision from Model = tf.keras.Model(inputs=z_in, outputs=outputs) Outputs.append(layers.Conv2DTranspose(1, (5, 5), strides=(1, 1), Z_in = tf.keras.Input(shape=(NOISE_DIM,)) Here we will make a standard generator model with a noise vector input and three output images, ordered from smallest to largest. That means the generator and discriminator are made like any other Keras model. In 2.0, the Keras interface is the interface for all deep learning. We can move onto creating the generator and discriminator models, now that the dataset is made and verified. Random Sample of the Fashion MNIST Dataset Images = sample.numpy() #this converts the tensor to a numpy arrayĪx.imshow((images + 1.0)/2, cmap='gray')įor sample in dataset: # the dataset has to fit in memory with eager iteration Grid = gridspec.GridSpec(num_res, num_samples) ![]() Num_samples = min(NUM_EXAMPLES, len(sample)) # use matplotlib to plot a given tensor sample This extends to the dataset api in TensorFlow and grants us the ability to interact with the data pipeline interactively through iteration. With eager execution we can now use TensorFlow in a more pythonic way and debug as we go. The eager execution implemented in TensorFlow 2.0 removes the need for initializing variables and creating sessions. #Function for reshaping images into the multiple resolutions we will use Train_images = train_images.reshape().astype('float32')ĭataset = tf._tensor_slices(train_images)ĭataset = dataset.shuffle(len(train_images))ĭataset = dataset.batch(batch_size, drop_remainder=True) #fashion MNIST is a drop in replacement for MNIST that is harder to solve Here we will be using the fashion MNIST dataset and use the established dataset API to create a TensorFlow dataset. The first step for training a network is to get the data pipeline started. By having the generator produce multiple resolution images, we ensure that the latent features throughout the network are relevant to output images. Here the generator produces multiple different resolution images and the discriminator decides on multiple resolutions given to it. The GAN paper we will be implementing here is MSG-GAN: Multi-Scale Gradient GAN for Stable Image Synthesis. To demonstrate what we can do with TensorFlow 2.0, we will be implementing a GAN model. This tutorial assumes a familiarity with TensorFlow, the Keras API and generative models. These features are eager execution, tf.function decorator, and the new distribution interface. In this tutorial, we will go over a few of the new major features in TensorFlow 2.0 and how to utilize them in deep learning projects. A beta version is available to experiment on the official site and you can also use the preconfigured template on Paperspace Gradient. Luckily, we don't have to wait for the official release. TensorFlow is one of the most popular frameworks used for deep learning projects and is approaching a major new release- TensorFlow 2.0.
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