#TENSORFLOW PYTHON HOW TO#
#TENSORFLOW PYTHON INSTALL#
#TENSORFLOW PYTHON GENERATOR#
#TENSORFLOW PYTHON CODE#

discriminator = make_discriminator_model()ĭecision = discriminator(generated_image) The model will be trained to output positive values for real images, and negative values for fake images. Use the (as yet untrained) discriminator to classify the generated images as real or fake.

The discriminator is a CNN-based image classifier. Generated_image = generator(noise, training=False)

#TENSORFLOW PYTHON GENERATOR#

Use the (as yet untrained) generator to create an image. Model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))Īssert model.output_shape = (None, 28, 28, 1) Model.add(layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))Īssert model.output_shape = (None, 7, 7, 256) # Note: None is the batch size Notice the tf. activation for each layer, except the output layer which uses tanh. Start with a Dense layer that takes this seed as input, then upsample several times until you reach the desired image size of 28x28x1. The generator uses tf.2DTranspose (upsampling) layers to produce an image from a seed (random noise). Train_dataset = tf._tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)īoth the generator and discriminator are defined using the Keras Sequential API. Train_images = (train_images - 127.5) / 127.5 # Normalize the images to Train_images = train_images.reshape(train_images.shape, 28, 28, 1).astype('float32') The generator will generate handwritten digits resembling the MNIST data. You will use the MNIST dataset to train the generator and the discriminator.

#TENSORFLOW PYTHON INSTALL#

# To generate GIFs pip install imageio pip install git+ import glob 04:03:54.775979: E external/local_xla/xla/stream_executor/cuda/cuda_:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered 04:03:54.774476: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 04:03:54.774427: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered To learn more about GANs, see MIT's Intro to Deep Learning course. The images begin as random noise, and increasingly resemble hand written digits over time. The following animation shows a series of images produced by the generator as it was trained for 50 epochs. This notebook demonstrates this process on the MNIST dataset.

The process reaches equilibrium when the discriminator can no longer distinguish real images from fakes. A generator ('the artist') learns to create images that look real, while a discriminator ('the art critic') learns to tell real images apart from fakes.ĭuring training, the generator progressively becomes better at creating images that look real, while the discriminator becomes better at telling them apart. Two models are trained simultaneously by an adversarial process. Generative Adversarial Networks (GANs) are one of the most interesting ideas in computer science today.

#TENSORFLOW PYTHON CODE#

The code is written using the Keras Sequential API with a tf.GradientTape training loop.

#TENSORFLOW PYTHON HOW TO#

This tutorial demonstrates how to generate images of handwritten digits using a Deep Convolutional Generative Adversarial Network (DCGAN).

Tensorflow python