Intermediate layers for style and content Now load a VGG19 without the classification head, and list the layer names vgg = tf.19(include_top=False, weights='imagenet')Ĩ0134624/80134624 - 0s 0us/stepĬhoose intermediate layers from the network to represent the style and content of the image: content_layers = Predicted_top_5 = tf._predictions(prediction_probabilities.numpy())ģ5363/35363 - 0s 0us/step Load a VGG19 and test run it on our image to ensure it's used correctly: x = tf._input(content_image*255) For an input image, try to match the corresponding style and content target representations at these intermediate layers. These intermediate layers are necessary to define the representation of content and style from the images. In this case, you are using the VGG19 network architecture, a pretrained image classification network. As you step through the network, the final few layers represent higher-level features-object parts like wheels or eyes. Starting from the network's input layer, the first few layer activations represent low-level features like edges and textures. Use the intermediate layers of the model to get the content and style representations of the image. Stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))

Before getting into the details, let's see how the TensorFlow Hub model does this: import tensorflow_hub as hub This tutorial demonstrates the original style-transfer algorithm, which optimizes the image content to a particular style. Follow the guide at for how to download and setup the required libraries for your platform. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. 05:54:16.043262: W tensorflow/core/common_runtime/gpu/gpu_:2211] Cannot dlopen some GPU libraries. New_shape = tf.cast(shape * scale, tf.int32)Ĭreate a simple function to display an image: def imshow(image, title=None): Shape = tf.cast(tf.shape(img), tf.float32) Img = tf.nvert_image_dtype(img, tf.float32) Style_path = tf._file('kandinsky5.jpg','')Ĩ3281/83281 - 0s 0us/stepġ95196/195196 - 0s 0us/stepĭefine a function to load an image and limit its maximum dimension to 512 pixels. Tensor = np.array(tensor, dtype=np.uint8)ĭownload images and choose a style image and a content image: content_path = tf._file('YellowLabradorLooking_new.jpg', '') 05:54:12.979315: E tensorflow/compiler/xla/stream_executor/cuda/cuda_:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered 05:54:12.979276: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered

05:54:12.979229: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered

# Load compressed models from tensorflow_hub Setup Import and configure modules import os Now, what would it look like if Kandinsky decided to paint the picture of this Dog exclusively with this style? Something like this? Yellow Labrador Looking, from Wikimedia Commons by Elf. These statistics are extracted from the images using a convolutional network.įor example, let’s take an image of this dog and Wassily Kandinsky's Composition 7: This is implemented by optimizing the output image to match the content statistics of the content image and the style statistics of the style reference image. Neural style transfer is an optimization technique used to take two images-a content image and a style reference image (such as an artwork by a famous painter)-and blend them together so the output image looks like the content image, but “painted” in the style of the style reference image. For an example of style transfer with TensorFlow Lite, refer to Artistic style transfer with TensorFlow Lite. This approach is much faster (up to 1000x).įor a simple application of style transfer with a pretrained model from TensorFlow Hub, check out the Fast style transfer for arbitrary styles tutorial that uses an arbitrary image stylization model. Modern approaches train a model to generate the stylized image directly (similar to CycleGAN). It optimizes the image content to a particular style. Note: This tutorial demonstrates the original style-transfer algorithm. This is known as neural style transfer and the technique is outlined in A Neural Algorithm of Artistic Style (Gatys et al.). This tutorial uses deep learning to compose one image in the style of another image (ever wish you could paint like Picasso or Van Gogh?).

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