{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n#import os\n#for dirname, _, filenames in os.walk('/kaggle/input'):\n#    for filename in filenames:\n#        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-03-29T23:47:15.106870Z","iopub.execute_input":"2023-03-29T23:47:15.108051Z","iopub.status.idle":"2023-03-29T23:47:15.114461Z","shell.execute_reply.started":"2023-03-29T23:47:15.107971Z","shell.execute_reply":"2023-03-29T23:47:15.113271Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Table of content\n\n1. Introduction to GAN\n2. Competiton context\n3. Data preparation\n4. Model preparation\n    - 4.1. Different type of Neural Networks\n    - 4.2. Evaluation of time generation\n    - 4.3. Build the GAN\n5. Train the GAN\n6. Test the GAN\n7. Create submission file","metadata":{}},{"cell_type":"markdown","source":"# 1. Introduction to GAN","metadata":{}},{"cell_type":"markdown","source":"Nowadays, a new tendency of AI is trending the net: AI that generate images like Midjourney and DALL-E.  \nHere are some exemples of cats generated from the AI:  \n![image.png](attachment:f5fdfda3-9852-4980-a88b-c83d0dad7dd4.png)  \n\nThe main method to do these kind of images is called GAN: Generative Adversial Network.","metadata":{},"attachments":{"f5fdfda3-9852-4980-a88b-c83d0dad7dd4.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"\n\nGenerative Adversarial Networks (GANs) are a type of machine learning algorithm that consists of two neural networks: a generator and a discriminator. The generator creates new data instances while the discriminator evaluates the authenticity of these instances. The two networks are trained simultaneously in a game-like process where the generator learns to create increasingly realistic data instances that can fool the discriminator, and the discriminator learns to become more accurate in distinguishing between real and fake data. Through this iterative process, GANs can produce high-quality, realistic outputs such as images, videos, and even music.","metadata":{}},{"cell_type":"markdown","source":"# 2. Competition context","metadata":{}},{"cell_type":"markdown","source":"The main purpose of this competition is to make a GAN that will be able to generate between 7000 and 10000 images with the style of the famous painter Monet.\n\nThe generated images have to be jpg format with a size of 256x256x3 (RGB) and be zipped in a single file named images.zip.  \nThe evaluation metric used by kaggle to know if the image is considered good is MiFD: **Memorization-informed Fréchet Inception Distance**.  \nThe smaller MiFID is, the better your generated images are.  \nMore details can be found at: [Kaggle Competition](https://www.kaggle.com/competitions/gan-getting-started/overview/description).  ","metadata":{}},{"cell_type":"markdown","source":"# 3. Data Preparation\n\nBased on \"Monet CycleGAN Tutorial\" notebook from Amy Jang, wich is a tutorial starter for this competition, we will prepare the setup and data we need to implement our GAN. It can be found there:  \n[https://www.kaggle.com/amyjang/monet-cyclegan-tutorial](https://www.kaggle.com/amyjang/monet-cyclegan-tutorial)\n  \nThis notebook gives basis of how data will be loaded inside a kaggle notebook. The method used in this notebbok to generate images is called cycleGAN, that we won't use for the time being.","metadata":{}},{"cell_type":"markdown","source":"First of all, we have decided to try executing\"Monet CycleGAN Tutorial\" notebook to know how it works and how long it takes to train and generate images for the competiton.  \nThe notebook take around 1h to execute. It trains a CycleGAN on 25 epochs with 300 images with each epoch taking around 40s (except for the first one).  \nThat notebook generate a bit more than 7000 images that saved on a images.zip file.  \nThat notebook used a TPU to be run.  \nAn explanation about TPU and ow it's used on kaggle can be found here: [Kaggle TPU documentation](https://www.kaggle.com/docs/tpu).  \nIn order to evaluate how TPU is usefull for the executaion, we tried to run the notebook without using TPU.  \nAfter running withou TPU, the same noteboo took around 11h to be executed. We could notice that each training epoch took around 1600s compared to 40s with TPU.  \nBased on that, we choose to use TPU as the same way it's used in Amy Jang notebook.  \nFirst, we need to activate the use of TPU (v3-8) on our kaggle notebook like this:  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"}}},{"cell_type":"code","source":"# Library for Data science\n\nimport tensorflow as tf #NN processing\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nimport tensorflow_addons as tfa\n\nfrom kaggle_datasets import KaggleDatasets\nimport matplotlib.pyplot as plt\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:15.119613Z","iopub.execute_input":"2023-03-29T23:47:15.119987Z","iopub.status.idle":"2023-03-29T23:47:22.597196Z","shell.execute_reply.started":"2023-03-29T23:47:15.119956Z","shell.execute_reply":"2023-03-29T23:47:22.596272Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import glob\nimport imageio\nimport matplotlib.pyplot as plt\nimport os\nimport PIL\nimport time\n\nfrom IPython import display","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:22.600121Z","iopub.execute_input":"2023-03-29T23:47:22.601371Z","iopub.status.idle":"2023-03-29T23:47:22.700338Z","shell.execute_reply.started":"2023-03-29T23:47:22.601323Z","shell.execute_reply":"2023-03-29T23:47:22.699449Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Library for TPU acceleration\ntry:\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n    print('Device:', tpu.master())\n    tf.config.experimental_connect_to_cluster(tpu)\n    tf.tpu.experimental.initialize_tpu_system(tpu)\n    strategy = tf.distribute.experimental.TPUStrategy(tpu)\nexcept:\n    strategy = tf.distribute.get_strategy()\nprint('Number of replicas:', strategy.num_replicas_in_sync)\n\nAUTOTUNE = tf.data.experimental.AUTOTUNE\n    \nprint(tf.__version__)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:22.702220Z","iopub.execute_input":"2023-03-29T23:47:22.703113Z","iopub.status.idle":"2023-03-29T23:47:22.721373Z","shell.execute_reply.started":"2023-03-29T23:47:22.703066Z","shell.execute_reply":"2023-03-29T23:47:22.720222Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Loading Data\n\nThe dataset we are using contains four directories: monet_tfrec, photo_tfrec, monet_jpg, and photo_jpg.  \nThe monet_tfrec and monet_jpg directories contain the same painting images sized 256x256 in JPEG format or TFRecord format, and the photo_tfrec and photo_jpg directories contain the same photos also sized 256x256 in JPEG format or TFRecord format.  \n\nThe monet directories contain Monet paintings that we will use for training our models.  \nThe photo directories contain photos that we will add Monet-style and submit the new generated images.  \n\nWe want to keep our photo dataset and our Monet dataset separate. First, load in the filenames of the TFRecords.","metadata":{}},{"cell_type":"code","source":"GCS_PATH = KaggleDatasets().get_gcs_path() #Get path of data from competition dataset","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:22.724169Z","iopub.execute_input":"2023-03-29T23:47:22.725042Z","iopub.status.idle":"2023-03-29T23:47:23.241993Z","shell.execute_reply.started":"2023-03-29T23:47:22.724982Z","shell.execute_reply":"2023-03-29T23:47:23.241092Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"MONET_FILENAMES = tf.io.gfile.glob(str(GCS_PATH + '/monet_tfrec/*.tfrec'))\nprint('Monet TFRecord Files:', len(MONET_FILENAMES))\n\nPHOTO_FILENAMES = tf.io.gfile.glob(str(GCS_PATH + '/photo_tfrec/*.tfrec'))\nprint('Photo TFRecord Files:', len(PHOTO_FILENAMES))","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:23.243564Z","iopub.execute_input":"2023-03-29T23:47:23.244215Z","iopub.status.idle":"2023-03-29T23:47:23.757534Z","shell.execute_reply.started":"2023-03-29T23:47:23.244177Z","shell.execute_reply":"2023-03-29T23:47:23.755943Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"All the images for the competition are already sized to 256x256. As these images are RGB images, set the channel to 3. Additionally, we need to scale the images to a [-1, 1] scale. Because we are building a generative model, we don't need the labels or the image id so we'll only return the image from the TFRecord.","metadata":{}},{"cell_type":"code","source":"img_sz = 256\nIMAGE_SIZE = [img_sz, img_sz]\n\ndef decode_image(image):\n    image = tf.image.decode_jpeg(image, channels=3)\n    image = (tf.cast(image, tf.float32) / 127.5) - 1\n    image = tf.reshape(image, [*IMAGE_SIZE, 3])\n    return image\n\ndef read_tfrecord(example):\n    tfrecord_format = {\n        \"image_name\": tf.io.FixedLenFeature([], tf.string),\n        \"image\": tf.io.FixedLenFeature([], tf.string),\n        \"target\": tf.io.FixedLenFeature([], tf.string)\n    }\n    example = tf.io.parse_single_example(example, tfrecord_format)\n    image = decode_image(example['image'])\n    return image","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:23.761579Z","iopub.execute_input":"2023-03-29T23:47:23.761975Z","iopub.status.idle":"2023-03-29T23:47:23.771161Z","shell.execute_reply.started":"2023-03-29T23:47:23.761941Z","shell.execute_reply":"2023-03-29T23:47:23.769641Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"##### Define the function to extract the image from the files.\n\n","metadata":{}},{"cell_type":"code","source":"def load_dataset(filenames, labeled=True, ordered=False):\n    dataset = tf.data.TFRecordDataset(filenames)\n    dataset = dataset.map(read_tfrecord, num_parallel_calls=AUTOTUNE)\n    return dataset","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:23.772925Z","iopub.execute_input":"2023-03-29T23:47:23.774185Z","iopub.status.idle":"2023-03-29T23:47:23.783561Z","shell.execute_reply.started":"2023-03-29T23:47:23.774137Z","shell.execute_reply":"2023-03-29T23:47:23.782368Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's load in our datasets.","metadata":{}},{"cell_type":"code","source":"batch_size = 1 #128*8","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:23.784912Z","iopub.execute_input":"2023-03-29T23:47:23.786335Z","iopub.status.idle":"2023-03-29T23:47:23.795196Z","shell.execute_reply.started":"2023-03-29T23:47:23.786287Z","shell.execute_reply":"2023-03-29T23:47:23.794174Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# batch_size: number of consecutive elements of this dataset to combine in a single batch --> batch_size=1 will give 1 element in each batch where we have a number of batch=number of elements in the dataset\n\n# 288 allows to be a multiple of 32\n\nif batch_size ==1 or batch_size ==12 :\n    monet_ds = load_dataset(MONET_FILENAMES, labeled=True).batch(batch_size)\n    photo_ds = load_dataset(PHOTO_FILENAMES, labeled=True).batch(batch_size)\nelse:\n    monet_ds = load_dataset(MONET_FILENAMES, labeled=True).take(288).batch(batch_size)\n    photo_ds = load_dataset(PHOTO_FILENAMES, labeled=True).take(288).batch(batch_size)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:23.797170Z","iopub.execute_input":"2023-03-29T23:47:23.798134Z","iopub.status.idle":"2023-03-29T23:47:24.225264Z","shell.execute_reply.started":"2023-03-29T23:47:23.798077Z","shell.execute_reply":"2023-03-29T23:47:24.224057Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We took one exemple of each of theses lists.","metadata":{}},{"cell_type":"code","source":"#next : Get following element of the iter(list) each time it is called. \nexample_monet = next(iter(monet_ds))\nexample_photo = next(iter(photo_ds))","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:24.229483Z","iopub.execute_input":"2023-03-29T23:47:24.229914Z","iopub.status.idle":"2023-03-29T23:47:29.433105Z","shell.execute_reply.started":"2023-03-29T23:47:24.229875Z","shell.execute_reply":"2023-03-29T23:47:29.431809Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's visualize a photo example and a Monet example.","metadata":{}},{"cell_type":"code","source":"plt.subplot(121)\nplt.title('Photo')\nplt.imshow(example_photo[0] * 0.5 + 0.5)\n\nplt.subplot(122)\nplt.title('Monet')\nplt.imshow(example_monet[0] * 0.5 + 0.5)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:29.435166Z","iopub.execute_input":"2023-03-29T23:47:29.435560Z","iopub.status.idle":"2023-03-29T23:47:29.866430Z","shell.execute_reply.started":"2023-03-29T23:47:29.435523Z","shell.execute_reply":"2023-03-29T23:47:29.865289Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"_, ax = plt.subplots(5, 2, figsize=(12, 12))\nfor i, img in enumerate(photo_ds.take(5)):\n    #print(str(i))\n    img = (img[0] * 127.5 + 127.5).numpy().astype(np.uint8)\n\n    ax[i, 0].imshow(img)\n    #ax[i, 1].imshow(monet)\n    ax[i, 0].set_title(\"Input Photo\")\n    #ax[i, 1].set_title(\"Monet-esque\")\n    ax[i, 0].axis(\"off\")\n    ax[i, 1].axis(\"off\")\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:29.867438Z","iopub.execute_input":"2023-03-29T23:47:29.867782Z","iopub.status.idle":"2023-03-29T23:47:32.951576Z","shell.execute_reply.started":"2023-03-29T23:47:29.867753Z","shell.execute_reply":"2023-03-29T23:47:32.950404Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Calcul of number of elements in datasets.","metadata":{}},{"cell_type":"code","source":"len_monet_ds = 0\nfor i in monet_ds:\n    #print(i)\n    #print(len(i))\n    len_monet_ds = len_monet_ds + len(i)\n    #len_monet_ds+=1","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:32.952970Z","iopub.execute_input":"2023-03-29T23:47:32.954094Z","iopub.status.idle":"2023-03-29T23:47:43.206866Z","shell.execute_reply.started":"2023-03-29T23:47:32.953979Z","shell.execute_reply":"2023-03-29T23:47:43.205485Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len_monet_ds #len_data = ","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:43.208601Z","iopub.execute_input":"2023-03-29T23:47:43.209092Z","iopub.status.idle":"2023-03-29T23:47:43.216727Z","shell.execute_reply.started":"2023-03-29T23:47:43.209052Z","shell.execute_reply":"2023-03-29T23:47:43.215312Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len_photo_ds = 0\nfor i in photo_ds:\n    #print(i)\n    #len_photo_ds+=1\n    len_photo_ds = len_photo_ds + len(i)\nlen_photo_ds","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:43.218646Z","iopub.execute_input":"2023-03-29T23:47:43.219155Z","iopub.status.idle":"2023-03-29T23:47:45.805994Z","shell.execute_reply.started":"2023-03-29T23:47:43.219109Z","shell.execute_reply":"2023-03-29T23:47:45.805025Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# monet_ds.cardinality().numpy()\n#INFINITE_CARDINALITY\t-1\n#UNKNOWN_CARDINALITY\t-2","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:45.807712Z","iopub.execute_input":"2023-03-29T23:47:45.808429Z","iopub.status.idle":"2023-03-29T23:47:45.813393Z","shell.execute_reply.started":"2023-03-29T23:47:45.808383Z","shell.execute_reply":"2023-03-29T23:47:45.812037Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 4. Model preparation\n\nIn order to build our GAN, we use the DCGAN tutorial from TensorFlow helping us to prepare all the functions we need: https://www.tensorflow.org/tutorials/generative/dcgan?hl=en  \nAs this tutorial used a CNN architecture which is not adapted to our problem, we decide to try different architectures that we found on the net.\n\n\n## 4.1. Different type of Neural Networks\n\nGANs, and specially  computer vision in machine learning use a type of neural networks called Convolutional Neural Networks. CNN are very good to understand differents type of features in image processing. The two NN we will focus use theses types of NN: \n\n- CNN-based model that proved good results with a lot of epochs.\n- CNN based on U-Net model.","metadata":{}},{"cell_type":"markdown","source":"The CNN-based scratch model is based on https://www.geeksforgeeks.org/building-a-generative-adversarial-network-using-keras/ made in 2019/06.  \nIt uses functions and methods which are not really fitted anymore for GAN use (Upsampling2D, relu, ...) but seems to good results for generating images.  \n\nThe second one will use methods from pix2pix CNN based on https://www.tensorflow.org/tutorials/generative/pix2pix?hl=en#build_the_generator Tensofflow tutorial in 2022/08. It usually uses a simplified U-Net architecture. It's the same used in the CycleGAN tutorial, without the skips layers because we will generate images from a random vector, not from an input image.  \n\nLet's start by designing the generator and discriminator. Then we will build the GAN architecture.","metadata":{}},{"cell_type":"code","source":"input_shape = tuple(IMAGE_SIZE) + (3,) #[256,256,3]\ninput_shape","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:45.814917Z","iopub.execute_input":"2023-03-29T23:47:45.815395Z","iopub.status.idle":"2023-03-29T23:47:45.829707Z","shell.execute_reply.started":"2023-03-29T23:47:45.815350Z","shell.execute_reply":"2023-03-29T23:47:45.828607Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 4.1.1. Generator design  \n\nThe first generator design based from the link of GeekForGeek looks like that:","metadata":{}},{"cell_type":"code","source":"def basic_generator():  \n    \n        #inputs = keras.Input(shape=input_shape)\n        model = tf.keras.Sequential()\n  \n        #Building the input layer\n        model.add(keras.Input(shape=(100,)))\n    \n        model.add(layers.Dense(128 * 32 * 32, activation=\"relu\"))\n        model.add(layers.Reshape((32, 32, 128)))\n        \n        #print(model.output_shape)  \n        model.add(layers.UpSampling2D())\n        #print(model.output_shape)\n        \n        model.add(layers.Conv2D(256, kernel_size=3, padding=\"same\"))\n        model.add(layers.BatchNormalization(momentum=0.78))\n        model.add(layers.LeakyReLU())\n          \n        model.add(layers.UpSampling2D())\n          \n        model.add(layers.Conv2D(128, kernel_size=3, padding=\"same\"))\n        model.add(layers.BatchNormalization(momentum=0.78))\n        model.add(layers.LeakyReLU())\n        \n        #print(model.output_shape)\n        model.add(layers.UpSampling2D())\n        \n        #print(model.output_shape)\n        \n        model.add(layers.Conv2D(64, kernel_size=3, padding=\"same\"))\n        model.add(layers.BatchNormalization(momentum=0.78))\n        model.add(layers.LeakyReLU())\n        \n        #print(model.output_shape)\n          \n        model.add(layers.Conv2D(3, kernel_size=3, padding=\"same\"))\n        model.add(layers.Activation(\"relu\"))\n        \n        #assert model.output_shape == (None, 28, 28, 1) # we need to be sure our NN will generate an image with the same input_shape as we'll use it as the discriminator input also\n        #model.add(layers.Dense(3, activation=\"relu\"))\n        \"\"\"\n        last_input = model.output_shape\n        print(\"last_input: \" + str(last_input))\n        \n        k_size = last_input[1] - img_sz*np.sign(last_input[1] - img_sz+1) + 1 # 1: strides of kernel filters\n        \n        print(\"k_size: \"+str(k_size))\n        print(str(np.sign(last_input[1] - img_sz)))\n        model.add(layers.Conv2D(3, kernel_size=(k_size,k_size), padding=\"valid\"))\n        model.add(layers.Activation(\"tanh\"))\n        \"\"\"\n        #print(model.output_shape)\n        assert model.output_shape == (None, img_sz, img_sz, 3) # we need to be sure our NN will generate an image with the same input_shape as we'll use it as the discriminator input also\n        \n\n        #Generating the output image\n        #noise = Input(shape=(latent_dimensions,))\n        #image = model(noise)\n  \n        #return Model(noise, image)\n        return model","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:45.831452Z","iopub.execute_input":"2023-03-29T23:47:45.831835Z","iopub.status.idle":"2023-03-29T23:47:45.846271Z","shell.execute_reply.started":"2023-03-29T23:47:45.831802Z","shell.execute_reply":"2023-03-29T23:47:45.845061Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"It's a relatively simple design with few layers.","metadata":{}},{"cell_type":"markdown","source":"The second one is based on a U-NET architecture, an efficient architecture used for these types of problems. It's based on the generator based on https://www.tensorflow.org/tutorials/generative/pix2pix?hl=en#build_the_generator and it's quite the same as the one from the CycleGAN tutorial except the skips layers that are used if we try to change the nature of an existing image. But we are generating a new image from a random vector, so these layers should not be needed!","metadata":{}},{"cell_type":"markdown","source":"U-Net is a convolutional neural network architecture designed for image segmentation tasks, which involves dividing an image into different regions or objects of interest. The U-Net architecture consists of an encoder and a decoder, which are connected by a bridge that passes information between the two parts of the network. The encoder is made up of several convolutional layers that extract features from the input image, while the decoder uses up-sampling layers to increase the resolution of the feature maps and generate a segmentation map. The architecture also includes skip connections that allow the decoder to use information from earlier layers in the encoder to generate more accurate segmentation maps. The U-Net architecture has been shown to perform well on various medical image segmentation tasks, including segmentation of organs and tumors. An illustration of the U-Net architecture can be found at this URL: (https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/u-net-architecture.png)","metadata":{}},{"cell_type":"code","source":"OUTPUT_CHANNELS = 3","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:45.847835Z","iopub.execute_input":"2023-03-29T23:47:45.848260Z","iopub.status.idle":"2023-03-29T23:47:45.864844Z","shell.execute_reply.started":"2023-03-29T23:47:45.848225Z","shell.execute_reply":"2023-03-29T23:47:45.863416Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def downsample(filters, size, apply_batchnorm=True):\n    initializer = tf.random_normal_initializer(0., 0.02)\n\n    result = tf.keras.Sequential()\n    result.add(\n        tf.keras.layers.Conv2D(filters, size, strides=2, padding='same',\n                         kernel_initializer=initializer, use_bias=False))\n\n    if apply_batchnorm:\n        result.add(tf.keras.layers.BatchNormalization())\n\n    result.add(tf.keras.layers.LeakyReLU())\n\n    return result","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:45.866628Z","iopub.execute_input":"2023-03-29T23:47:45.867327Z","iopub.status.idle":"2023-03-29T23:47:45.905930Z","shell.execute_reply.started":"2023-03-29T23:47:45.867290Z","shell.execute_reply":"2023-03-29T23:47:45.903770Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"inp = example_photo[0] * 0.5 + 0.5\n\ndown_model = downsample(3, 4)\ndown_result = down_model(tf.expand_dims(inp, 0))\nprint (down_result.shape)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:45.908338Z","iopub.execute_input":"2023-03-29T23:47:45.909465Z","iopub.status.idle":"2023-03-29T23:47:46.091909Z","shell.execute_reply.started":"2023-03-29T23:47:45.909411Z","shell.execute_reply":"2023-03-29T23:47:46.090472Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def upsample(filters, size, apply_dropout=False):\n    initializer = tf.random_normal_initializer(0., 0.02)\n\n    result = tf.keras.Sequential()\n    result.add(\n        tf.keras.layers.Conv2DTranspose(filters, size, strides=2,\n                                        padding='same',\n                                        kernel_initializer=initializer,\n                                        use_bias=False))\n\n    result.add(tf.keras.layers.BatchNormalization())\n\n    if apply_dropout:\n        result.add(tf.keras.layers.Dropout(0.5))\n\n    result.add(tf.keras.layers.ReLU())\n\n    return result","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:46.093574Z","iopub.execute_input":"2023-03-29T23:47:46.094040Z","iopub.status.idle":"2023-03-29T23:47:46.103640Z","shell.execute_reply.started":"2023-03-29T23:47:46.093968Z","shell.execute_reply":"2023-03-29T23:47:46.102172Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"up_model = upsample(3, 4)\nup_result = up_model(down_result)\nprint (up_result.shape)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:46.106000Z","iopub.execute_input":"2023-03-29T23:47:46.106512Z","iopub.status.idle":"2023-03-29T23:47:46.234332Z","shell.execute_reply.started":"2023-03-29T23:47:46.106465Z","shell.execute_reply":"2023-03-29T23:47:46.233121Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def pix_generator():\n    \n    #inputs = keras.Input(shape=input_shape)\n\n    #Building the input layer\n     \n    #inputs = tf.keras.layers.Input(shape=[256, 256, 3])\n    \n    inputs = tf.keras.layers.Input(shape=(100,))\n    \n    x = inputs\n    x = layers.Dense(256 * 256 * 3, activation=\"relu\") (x)\n    x = layers.Reshape((256, 256, 3))(x)\n\n    down_stack = [\n        downsample(64, 4, apply_batchnorm=False),  # (batch_size, 128, 128, 64)\n        downsample(128, 4),  # (batch_size, 64, 64, 128)\n        downsample(256, 4),  # (batch_size, 32, 32, 256)\n        downsample(512, 4),  # (batch_size, 16, 16, 512)\n        downsample(512, 4),  # (batch_size, 8, 8, 512)\n        downsample(512, 4),  # (batch_size, 4, 4, 512)\n        downsample(512, 4),  # (batch_size, 2, 2, 512)\n        downsample(512, 4),  # (batch_size, 1, 1, 512)\n    ]\n\n    up_stack = [\n        upsample(512, 4, apply_dropout=True),  # (batch_size, 2, 2, 1024)\n        upsample(512, 4, apply_dropout=True),  # (batch_size, 4, 4, 1024)\n        upsample(512, 4, apply_dropout=True),  # (batch_size, 8, 8, 1024)\n        upsample(512, 4),  # (batch_size, 16, 16, 1024)\n        upsample(256, 4),  # (batch_size, 32, 32, 512)\n        upsample(128, 4),  # (batch_size, 64, 64, 256)\n        upsample(64, 4),  # (batch_size, 128, 128, 128)\n    ]\n\n    initializer = tf.random_normal_initializer(0., 0.02)\n    last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, 4,\n                                     strides=2,\n                                     padding='same',\n                                     kernel_initializer=initializer,\n                                     activation='tanh')  # (batch_size, 256, 256, 3)\n\n    #x = inputs\n    \"\"\"\n    # Downsampling through the model\n    skips = []\n    for down in down_stack:\n        x = down(x)\n        skips.append(x)\n\n    skips = reversed(skips[:-1])\n\n    # Upsampling and establishing the skip connections\n    for up, skip in zip(up_stack, skips):\n        x = up(x)\n        x = tf.keras.layers.Concatenate()([x, skip])\n\n    \"\"\"\n    \n    # Downsampling through the model\n    for down in down_stack:\n        x = down(x)\n\n    # Upsampling and establishing the skip connections\n    for up in up_stack:\n        x = up(x)\n\n    x = last(x)\n\n    return tf.keras.Model(inputs=inputs, outputs=x, name=\"pix_generator\")","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:46.236456Z","iopub.execute_input":"2023-03-29T23:47:46.236923Z","iopub.status.idle":"2023-03-29T23:47:46.251556Z","shell.execute_reply.started":"2023-03-29T23:47:46.236881Z","shell.execute_reply":"2023-03-29T23:47:46.250251Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def dcgan_generator():\n    \n    #inputs = keras.Input(shape=input_shape)\n\n    #Building the input layer\n     \n    #inputs = tf.keras.layers.Input(shape=[256, 256, 3])\n    \n    inputs = tf.keras.layers.Input(shape=(100,))\n    \n    x = inputs\n    x = layers.Dense(4 * 4 * 4096, activation=\"relu\") (x)\n    x = layers.Reshape((4, 4, 4096))(x)\n\n    up_stack = [\n        upsample(2048, 5, apply_dropout=True),  # (batch_size, 8, 8, 2048)\n        upsample(1024, 5, apply_dropout=True),  # (batch_size, 16, 16, 1024)\n        upsample(512, 5, apply_dropout=True),  # (batch_size, 64, 64, 512)\n        upsample(256, 5),  # (batch_size, 128, 128, 256)\n        upsample(128, 5),  # (batch_size, 256, 256, 128)\n    ]\n\n    initializer = tf.random_normal_initializer(0., 0.02)\n    last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, 5,\n                                     strides=2,\n                                     padding='same',\n                                     kernel_initializer=initializer,\n                                     activation='tanh')  # (batch_size, 256, 256, 3)\n\n    #x = inputs\n\n\n    # Upsampling and establishing the skip connections\n    for up in up_stack:\n        x = up(x)\n\n    x = last(x)\n\n    return tf.keras.Model(inputs=inputs, outputs=x, name=\"dcgan_generator\")","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:46.253769Z","iopub.execute_input":"2023-03-29T23:47:46.254288Z","iopub.status.idle":"2023-03-29T23:47:46.270090Z","shell.execute_reply.started":"2023-03-29T23:47:46.254240Z","shell.execute_reply":"2023-03-29T23:47:46.269097Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\"\"\"\ndef dcgan_generator_2():\n    \n    #inputs = keras.Input(shape=input_shape)\n\n    #Building the input layer\n     \n    #inputs = tf.keras.layers.Input(shape=[256, 256, 3])\n    \n    inputs = tf.keras.layers.Input(shape=(100,))\n    \n    x = inputs\n    x = layers.Dense(8 * 8 * 2048, activation=\"relu\") (x)\n    x = layers.Reshape((8, 8, 2048))(x)\n\n    up_stack = [\n        #upsample(2048, 5, apply_dropout=True),  # (batch_size, 8, 8, 2048)\n        upsample(1024, 5, apply_dropout=True),  # (batch_size, 16, 16, 1024)\n        upsample(512, 5, apply_dropout=True),  # (batch_size, 64, 64, 512)\n        upsample(256, 5),  # (batch_size, 128, 128, 256)\n        upsample(128, 5),  # (batch_size, 256, 256, 128)\n    ]\n\n    initializer = tf.random_normal_initializer(0., 0.02)\n    last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, 5,\n                                     strides=2,\n                                     padding='same',\n                                     kernel_initializer=initializer,\n                                     activation='tanh')  # (batch_size, 256, 256, 3)\n\n    #x = inputs\n\n\n    # Upsampling and establishing the skip connections\n    for up in up_stack:\n        x = up(x)\n\n    x = last(x)\n\n    return tf.keras.Model(inputs=inputs, outputs=x, name=\"dcgan_generator_2\")\"\"\"","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:46.271038Z","iopub.execute_input":"2023-03-29T23:47:46.271343Z","iopub.status.idle":"2023-03-29T23:47:46.289833Z","shell.execute_reply.started":"2023-03-29T23:47:46.271314Z","shell.execute_reply":"2023-03-29T23:47:46.289025Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def dcgan_generator_2():\n    \n    #inputs = keras.Input(shape=input_shape)\n\n    #Building the input layer\n     \n    #inputs = tf.keras.layers.Input(shape=[256, 256, 3])\n    \n    inputs = tf.keras.layers.Input(shape=(100,))\n    \n    x = inputs\n    x = layers.Dense(8 * 8 * 1024, activation=\"relu\") (x)\n    x = layers.Reshape((8, 8, 1024))(x)\n\n    up_stack = [\n        #upsample(2048, 5, apply_dropout=True),  # (batch_size, 8, 8, 2048)\n        upsample(512, 5, apply_dropout=True),  # (batch_size, 16, 16, 512)\n        upsample(256, 5, apply_dropout=True),  # (batch_size, 64, 64, 256)\n        upsample(128, 5),  # (batch_size, 128, 128, 128)\n        upsample(256, 5),  # (batch_size, 256, 256, 64)\n    ]\n\n    initializer = tf.random_normal_initializer(0., 0.02)\n    last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, 5,\n                                     strides=2,\n                                     padding='same',\n                                     kernel_initializer=initializer,\n                                     activation='tanh')  # (batch_size, 256, 256, 3)\n\n    #x = inputs\n\n\n    # Upsampling and establishing the skip connections\n    for up in up_stack:\n        x = up(x)\n\n    x = last(x)\n\n    return tf.keras.Model(inputs=inputs, outputs=x, name=\"dcgan_generator_2\")","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:46.291085Z","iopub.execute_input":"2023-03-29T23:47:46.291426Z","iopub.status.idle":"2023-03-29T23:47:46.310102Z","shell.execute_reply.started":"2023-03-29T23:47:46.291396Z","shell.execute_reply":"2023-03-29T23:47:46.308733Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def dcgan_generator_3():\n    \n    #inputs = keras.Input(shape=input_shape)\n\n    #Building the input layer\n     \n    #inputs = tf.keras.layers.Input(shape=[256, 256, 3])\n    \n    inputs = tf.keras.layers.Input(shape=(100,))\n    \n    x = inputs\n    x = layers.Dense(8 * 8 * 512, activation=\"relu\") (x)\n    x = layers.Reshape((8, 8, 512))(x)\n\n    up_stack = [\n        #upsample(2048, 5, apply_dropout=True),  # (batch_size, 8, 8, 2048)\n        upsample(256, 5, apply_dropout=True),  # (batch_size, 16, 16, 512)\n        upsample(128, 5, apply_dropout=True),  # (batch_size, 64, 64, 256)\n        upsample(64, 5),  # (batch_size, 128, 128, 128)\n        upsample(32, 5),  # (batch_size, 256, 256, 64)\n    ]\n\n    initializer = tf.random_normal_initializer(0., 0.02)\n    last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, 5,\n                                     strides=2,\n                                     padding='same',\n                                     kernel_initializer=initializer,\n                                     activation='tanh')  # (batch_size, 256, 256, 3)\n\n    #x = inputs\n\n\n    # Upsampling and establishing the skip connections\n    for up in up_stack:\n        x = up(x)\n\n    x = last(x)\n\n    return tf.keras.Model(inputs=inputs, outputs=x, name=\"dcgan_generator_3\")","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:46.318180Z","iopub.execute_input":"2023-03-29T23:47:46.318583Z","iopub.status.idle":"2023-03-29T23:47:46.334218Z","shell.execute_reply.started":"2023-03-29T23:47:46.318551Z","shell.execute_reply":"2023-03-29T23:47:46.332775Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"gan_gen = dcgan_generator_3()\ngan_gen.summary()","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:46.335284Z","iopub.execute_input":"2023-03-29T23:47:46.336485Z","iopub.status.idle":"2023-03-29T23:47:46.626921Z","shell.execute_reply.started":"2023-03-29T23:47:46.336446Z","shell.execute_reply":"2023-03-29T23:47:46.625636Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Exemple of generated images from a photo input passed through ou basic generator\nnoise_input = tf.random.normal([1, 100])\n\nfor test_generator in [basic_generator(),pix_generator(), dcgan_generator(), dcgan_generator_2(), dcgan_generator_3()]:\n    test_generator.summary()\n\n    start = time.time()\n    to_monet = test_generator(noise_input)\n    print ('Time for one generated_image from photo is {} sec'.format(time.time()-start))\n\n    plt.subplot(1, 2, 1)\n    plt.title(\"Original Photo\")\n    plt.imshow(example_photo[0] * 0.5 + 0.5)\n\n    plt.subplot(1, 2, 2)\n    plt.title(\"Monet-esque Photo\")\n    plt.imshow(to_monet[0] * 0.5 + 0.5)\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:46.628401Z","iopub.execute_input":"2023-03-29T23:47:46.628769Z","iopub.status.idle":"2023-03-29T23:47:55.746951Z","shell.execute_reply.started":"2023-03-29T23:47:46.628735Z","shell.execute_reply":"2023-03-29T23:47:55.745896Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We need to generate at least 7000 images in less than 5h (and 3h if we use TPU).  \nLet see the average calculation for 100 images generated for each methods.","metadata":{}},{"cell_type":"code","source":"# Exemple of generated images from a photo input passed through our basic generator\n\ngen_time =[[],[],[],[],[]]\nfor num_ex in  range(100):\n    \n    noise_input = tf.random.normal([1, 100])\n    i=0\n    for test_generator in [basic_generator(),pix_generator(), dcgan_generator(), dcgan_generator_2(), dcgan_generator_3()]:\n        #test_generator.summary()\n\n        start = time.time()\n        to_monet = test_generator(noise_input)\n        #print ('Time for one generated_image from photo is {} sec'.format(time.time()-start))\n        \n        time_gen = time.time()-start\n        gen_time[i].append(time_gen)\n        i+=1\n","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:47:55.748199Z","iopub.execute_input":"2023-03-29T23:47:55.749304Z","iopub.status.idle":"2023-03-29T23:57:40.797058Z","shell.execute_reply.started":"2023-03-29T23:47:55.749264Z","shell.execute_reply":"2023-03-29T23:57:40.796079Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(gen_time)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:57:40.798347Z","iopub.execute_input":"2023-03-29T23:57:40.798677Z","iopub.status.idle":"2023-03-29T23:57:40.809108Z","shell.execute_reply.started":"2023-03-29T23:57:40.798647Z","shell.execute_reply":"2023-03-29T23:57:40.808157Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(gen_time[0])","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:57:40.810422Z","iopub.execute_input":"2023-03-29T23:57:40.811441Z","iopub.status.idle":"2023-03-29T23:57:40.818897Z","shell.execute_reply.started":"2023-03-29T23:57:40.811397Z","shell.execute_reply":"2023-03-29T23:57:40.817785Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = plt.figure(figsize =(10, 7))\n \n# Creating axes instance\nax = fig.add_axes([0, 0, 1, 1])\n \n# Creating plot\nbp = ax.boxplot(gen_time, showmeans=True)\nplt.xlabel(\"Type of Generator\")\nplt.ylabel(\"Duration of image generation [s]\")\n#plt.legend(['Duration','Mean'])\n#ax.legend([bp['boxes'][0],bp['boxes'][1],bp['boxes'][2]],['A','B','C'])\n# show plot\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:57:40.820768Z","iopub.execute_input":"2023-03-29T23:57:40.821233Z","iopub.status.idle":"2023-03-29T23:57:41.052161Z","shell.execute_reply.started":"2023-03-29T23:57:40.821192Z","shell.execute_reply":"2023-03-29T23:57:41.051004Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = plt.figure(figsize =(10, 7))\n\nfor i in range(len(gen_time)):\n\n    plt.plot(gen_time[i])\n\n    #plt.plot(np.mean(gen_time[i]),marker='o')\n\n    plt.plot([0,100],[np.mean(gen_time[i]),np.mean(gen_time[i])])\n\nplt.legend(['basic_generator()','mean_basic','pix_generator()','mean_pix','dcgan_generator()','mean_dcgan','dcgan_generator_2()','mean_dcgan_2','dcgan_generator_3()','mean_dcgan_3'])\n\nplt.xlabel(\"Image number\")\nplt.ylabel(\"Time to generate one image [s]\")\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:57:41.054166Z","iopub.execute_input":"2023-03-29T23:57:41.054642Z","iopub.status.idle":"2023-03-29T23:57:41.385187Z","shell.execute_reply.started":"2023-03-29T23:57:41.054597Z","shell.execute_reply":"2023-03-29T23:57:41.384209Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\"\"\"\nfig = plt.figure(figsize =(10, 7))\n\nplt.plot(gen_time[0])\nplt.plot(gen_time[1])\n\nplt.plot(np.mean(gen_time[0]),marker='o')\nplt.plot(np.mean(gen_time[1]),marker='o')\n\nplt.plot([0,100],[np.mean(gen_time[0]),np.mean(gen_time[0])])\nplt.plot([0,100],[np.mean(gen_time[1]),np.mean(gen_time[1])])\n\nplt.legend(['basic_generator()','pix_generator()',\n           'mean_','mean_pix'])\n\nplt.xlabel(\"Image number\")\nplt.ylabel(\"Time to generate one image [s]\")\n\nplt.show()\"\"\"","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:57:41.386676Z","iopub.execute_input":"2023-03-29T23:57:41.387278Z","iopub.status.idle":"2023-03-29T23:57:41.394281Z","shell.execute_reply.started":"2023-03-29T23:57:41.387243Z","shell.execute_reply":"2023-03-29T23:57:41.392821Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":" \n1h = 3600s . The average time for geenrate images without TPU is 0.25s for pix_generator.  \n--> 3600s/0.25s = 14400 images hoiceuld be generated in one hour.  \nWe need to generate at least 7000 images in less than 5h (3h if we use TPU).  \nThis method seems computationnaly valuable. Let's test it.  \n\nIn average, the U-net based pix_generator seems quite faster than the basic generator to generate images from an input vector. We choose to continue with the fastest.  \n\nThe second method using a U-Net-like architecture seems faster to generate images.  \n","metadata":{}},{"cell_type":"code","source":"input_shape","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:57:41.395524Z","iopub.execute_input":"2023-03-29T23:57:41.396479Z","iopub.status.idle":"2023-03-29T23:57:41.408050Z","shell.execute_reply.started":"2023-03-29T23:57:41.396428Z","shell.execute_reply":"2023-03-29T23:57:41.406768Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"example_photo.shape","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:57:41.409722Z","iopub.execute_input":"2023-03-29T23:57:41.410053Z","iopub.status.idle":"2023-03-29T23:57:41.423342Z","shell.execute_reply.started":"2023-03-29T23:57:41.410024Z","shell.execute_reply":"2023-03-29T23:57:41.422457Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"example_photo[0].shape","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:57:41.425087Z","iopub.execute_input":"2023-03-29T23:57:41.425411Z","iopub.status.idle":"2023-03-29T23:57:41.435105Z","shell.execute_reply.started":"2023-03-29T23:57:41.425383Z","shell.execute_reply":"2023-03-29T23:57:41.433985Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"noise_input.shape","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:57:41.436788Z","iopub.execute_input":"2023-03-29T23:57:41.437332Z","iopub.status.idle":"2023-03-29T23:57:41.451900Z","shell.execute_reply.started":"2023-03-29T23:57:41.437291Z","shell.execute_reply":"2023-03-29T23:57:41.450788Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 4.1.2. Discriminator design  \nWe build two different types of discriminators. But based on the study made previously with geenrators, we decide to keep the discriminator used with U-net architecture geenrator: pix_discriminator.","metadata":{}},{"cell_type":"code","source":"def basic_discriminator():\n    #Building the convolutional layers\n    #to classify whether an image is real or fake\n    \n    #inputs = tf.keras.Input(shape=input_shape)\n    model = tf.keras.Sequential()\n  \n    #Building the input layer\n    model.add(keras.Input(shape=input_shape))\n    \n    #model = Sequential()\n\n    model.add(layers.Conv2D(32, kernel_size=3, strides=2, padding=\"same\"))\n    model.add(layers.LeakyReLU(alpha=0.2))\n    model.add(layers.Dropout(0.25))\n\n    model.add(layers.Conv2D(64, kernel_size=3, strides=2, padding=\"same\"))\n    model.add(layers.ZeroPadding2D(padding=((0,1),(0,1))))\n    model.add(layers.BatchNormalization(momentum=0.82))\n    model.add(layers.LeakyReLU(alpha=0.25))\n    model.add(layers.Dropout(0.25))\n\n    model.add(layers.Conv2D(128, kernel_size=3, strides=2, padding=\"same\"))\n    model.add(layers.BatchNormalization(momentum=0.82))\n    model.add(layers.LeakyReLU(alpha=0.2))\n    model.add(layers.Dropout(0.25))\n\n    model.add(layers.Conv2D(256, kernel_size=3, strides=1, padding=\"same\"))\n    model.add(layers.BatchNormalization(momentum=0.8))\n    model.add(layers.LeakyReLU(alpha=0.25))\n    model.add(layers.Dropout(0.25))\n    \n    #print(model.output_shape)\n\n    #Building the output layer\n    model.add(layers.Flatten())\n    print(model.output_shape)\n    #assert model.output_shape == (None, 278784)\n    model.add(layers.Dense(1, activation='sigmoid'))\n\n    #image = Input(shape=image_shape)\n    #validity = model(image)\n\n    #return Model(image, validity)\n    return model","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:57:41.453703Z","iopub.execute_input":"2023-03-29T23:57:41.454165Z","iopub.status.idle":"2023-03-29T23:57:41.467614Z","shell.execute_reply.started":"2023-03-29T23:57:41.454123Z","shell.execute_reply":"2023-03-29T23:57:41.466526Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def pix_discriminator():\n    initializer = tf.random_normal_initializer(0., 0.02)\n\n    #inp = tf.keras.layers.Input(shape=[256, 256, 3], name='input_image')\n    #tar = tf.keras.layers.Input(shape=[256, 256, 3], name='target_image')\n\n    #x = tf.keras.layers.concatenate([inp, tar])  # (batch_size, 256, 256, channels*2)\n    \n    inputs = tf.keras.layers.Input(shape=input_shape)\n    \n    x = inputs\n\n    down1 = downsample(64, 4, False)(x)  # (batch_size, 128, 128, 64)\n    down2 = downsample(128, 4)(down1)  # (batch_size, 64, 64, 128)\n    down3 = downsample(256, 4)(down2)  # (batch_size, 32, 32, 256)\n\n    zero_pad1 = tf.keras.layers.ZeroPadding2D()(down3)  # (batch_size, 34, 34, 256)\n    conv = tf.keras.layers.Conv2D(512, 4, strides=1,\n                                kernel_initializer=initializer,\n                                use_bias=False)(zero_pad1)  # (batch_size, 31, 31, 512)\n\n    batchnorm1 = tf.keras.layers.BatchNormalization()(conv)\n\n    leaky_relu = tf.keras.layers.LeakyReLU()(batchnorm1)\n\n    zero_pad2 = tf.keras.layers.ZeroPadding2D()(leaky_relu)  # (batch_size, 33, 33, 512)\n\n    last = tf.keras.layers.Conv2D(1, 4, strides=1,\n                                kernel_initializer=initializer)(zero_pad2)  # (batch_size, 30, 30, 1)\n    \n    last2 = tf.keras.layers.Flatten()(last)\n    last3 = tf.keras.layers.Dense(1)(last2)\n\n    return tf.keras.Model(inputs=inputs, outputs=last3, name=\"pix_discriminator\")","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:57:41.469127Z","iopub.execute_input":"2023-03-29T23:57:41.469475Z","iopub.status.idle":"2023-03-29T23:57:41.480788Z","shell.execute_reply.started":"2023-03-29T23:57:41.469445Z","shell.execute_reply":"2023-03-29T23:57:41.479716Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def dcgan_discriminator():\n    initializer = tf.random_normal_initializer(0., 0.02)\n\n    #inp = tf.keras.layers.Input(shape=[256, 256, 3], name='input_image')\n    #tar = tf.keras.layers.Input(shape=[256, 256, 3], name='target_image')\n\n    #x = tf.keras.layers.concatenate([inp, tar])  # (batch_size, 256, 256, channels*2)\n    \n    inputs = tf.keras.layers.Input(shape=input_shape)\n    \n    x = inputs\n\n    down_stack = [\n        downsample(256, 5, apply_batchnorm=False),  # (batch_size, 128, 128, 256)\n        downsample(512, 5),  # (batch_size, 64, 64, 512)\n        downsample(1024, 5),  # (batch_size, 32, 32, 1024)\n        downsample(2048, 5),  # (batch_size, 16, 16, 2048)\n        downsample(4096, 5),  # (batch_size, 8, 8, 4096)\n        #downsample(8192, 5),  # (batch_size, 4, 4, 8192) #too much computation\n    ]\n    \n    # Downsampling through the model\n    \n    for down in down_stack:\n        x = down(x)\n    \n    last = tf.keras.layers.Conv2D(1, 5, strides=2,\n                                  kernel_initializer=initializer)(x)  # (batch_size, , , 1)\n    \n    last2 = tf.keras.layers.Flatten()(last)\n    last3 = tf.keras.layers.Dense(1, activation='sigmoid')(last2)\n\n    return tf.keras.Model(inputs=inputs, outputs=last3, name=\"dcgan_discriminator\")","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:57:41.482269Z","iopub.execute_input":"2023-03-29T23:57:41.482568Z","iopub.status.idle":"2023-03-29T23:57:41.499092Z","shell.execute_reply.started":"2023-03-29T23:57:41.482541Z","shell.execute_reply":"2023-03-29T23:57:41.497862Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def dcgan_discriminator_2():\n    initializer = tf.random_normal_initializer(0., 0.02)\n\n    #inp = tf.keras.layers.Input(shape=[256, 256, 3], name='input_image')\n    #tar = tf.keras.layers.Input(shape=[256, 256, 3], name='target_image')\n\n    #x = tf.keras.layers.concatenate([inp, tar])  # (batch_size, 256, 256, channels*2)\n    \n    inputs = tf.keras.layers.Input(shape=input_shape)\n    \n    x = inputs\n\n    down_stack = [\n        downsample(256, 5, apply_batchnorm=False),  # (batch_size, 128, 128, 256)\n        downsample(512, 5),  # (batch_size, 64, 64, 512)\n        downsample(1024, 5),  # (batch_size, 32, 32, 1024)\n        #downsample(2048, 5),  # (batch_size, 16, 16, 2048)\n        #downsample(4096, 5),  # (batch_size, 8, 8, 4096)\n        #downsample(8192, 5),  # (batch_size, 4, 4, 8192) #too much computation\n    ]\n    \n    # Downsampling through the model\n    \n    for down in down_stack:\n        x = down(x)\n    \n    last = tf.keras.layers.Conv2D(1, 5, strides=2,\n                                  kernel_initializer=initializer)(x)  # (batch_size, , , 1)\n    \n    last2 = tf.keras.layers.Flatten()(last)\n    last3 = tf.keras.layers.Dense(1, activation='sigmoid')(last2)\n\n    return tf.keras.Model(inputs=inputs, outputs=last3, name=\"dcgan_discriminator_2\")","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:57:41.500633Z","iopub.execute_input":"2023-03-29T23:57:41.501381Z","iopub.status.idle":"2023-03-29T23:57:41.518636Z","shell.execute_reply.started":"2023-03-29T23:57:41.501336Z","shell.execute_reply":"2023-03-29T23:57:41.517130Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def dcgan_discriminator_3():\n    initializer = tf.random_normal_initializer(0., 0.02)\n\n    #inp = tf.keras.layers.Input(shape=[256, 256, 3], name='input_image')\n    #tar = tf.keras.layers.Input(shape=[256, 256, 3], name='target_image')\n\n    #x = tf.keras.layers.concatenate([inp, tar])  # (batch_size, 256, 256, channels*2)\n    \n    inputs = tf.keras.layers.Input(shape=input_shape)\n    \n    x = inputs\n\n    down_stack = [\n        downsample(128, 5, apply_batchnorm=False),  # (batch_size, 128, 128, 256)\n        downsample(256, 5),  # (batch_size, 64, 64, 512)\n        downsample(512, 5),  # (batch_size, 32, 32, 1024)\n        #downsample(2048, 5),  # (batch_size, 16, 16, 2048)\n        #downsample(4096, 5),  # (batch_size, 8, 8, 4096)\n        #downsample(8192, 5),  # (batch_size, 4, 4, 8192) #too much computation\n    ]\n    \n    # Downsampling through the model\n    \n    for down in down_stack:\n        x = down(x)\n    \n    last = tf.keras.layers.Conv2D(1, 5, strides=2,\n                                  kernel_initializer=initializer)(x)  # (batch_size, , , 1)\n    \n    last2 = tf.keras.layers.Flatten()(last)\n    last3 = tf.keras.layers.Dense(1, activation='sigmoid')(last2)\n\n    return tf.keras.Model(inputs=inputs, outputs=last3, name=\"dcgan_discriminator_3\")","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:57:41.522675Z","iopub.execute_input":"2023-03-29T23:57:41.523070Z","iopub.status.idle":"2023-03-29T23:57:41.535745Z","shell.execute_reply.started":"2023-03-29T23:57:41.523002Z","shell.execute_reply":"2023-03-29T23:57:41.534729Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"disc = dcgan_discriminator_2()\nnp.array_str(disc(example_monet).numpy())","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:57:41.539711Z","iopub.execute_input":"2023-03-29T23:57:41.540079Z","iopub.status.idle":"2023-03-29T23:57:55.407453Z","shell.execute_reply.started":"2023-03-29T23:57:41.540042Z","shell.execute_reply":"2023-03-29T23:57:55.406130Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Too much memory usage\nplt.subplot(1, 2, 1)\nplt.title(\"Original Photo\")\nplt.imshow(example_monet[0] * 0.5 + 0.5)\n\nfor test_discriminator in [basic_discriminator(),pix_discriminator(), dcgan_discriminator(), dcgan_discriminator_2(), dcgan_discriminator_3()]:\n    test_discriminator.summary()\n    \n    decision_monet = test_discriminator(example_monet)\n    #decision_to_monet = test_discriminator(to_monet)\n    print (\"decision_monet: \" + np.array_str(decision_monet.numpy()) + \"\\n\")\n    decision_monet\n    #print (\"decision_to_monet: \" + decision_to_monet)\n","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:57:55.408859Z","iopub.execute_input":"2023-03-29T23:57:55.409249Z","iopub.status.idle":"2023-03-29T23:58:45.430491Z","shell.execute_reply.started":"2023-03-29T23:57:55.409217Z","shell.execute_reply":"2023-03-29T23:58:45.429571Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Choice of Generator and discriminator","metadata":{}},{"cell_type":"code","source":"# i_gen_disc\n\ni_gen_disc = 3\n\nif i_gen_disc ==0:\n    with strategy.scope():\n        test_generator = basic_generator()\n        test_discriminator = basic_discriminator()\nelif i_gen_disc ==1:\n    with strategy.scope():\n        test_generator = pix_generator()\n        test_discriminator = pix_discriminator()\nelif i_gen_disc ==2:\n    with strategy.scope():\n        test_generator = dcgan_generator()\n        test_discriminator = dcgan_discriminator()\nelif i_gen_disc ==3:\n    with strategy.scope():\n        test_generator = dcgan_generator_2()\n        test_discriminator = dcgan_discriminator_2()\nelif i_gen_disc ==4:\n    with strategy.scope():\n        test_generator = dcgan_generator_3()\n        test_discriminator = dcgan_discriminator_3()","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:45.432057Z","iopub.execute_input":"2023-03-29T23:58:45.432661Z","iopub.status.idle":"2023-03-29T23:58:45.838298Z","shell.execute_reply.started":"2023-03-29T23:58:45.432627Z","shell.execute_reply":"2023-03-29T23:58:45.837383Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\"\"\"\n# Exemple of generated images from a photo input passed through ou basic generator\nnoise_input = tf.random.normal([1, 100])\n\nstart = time.time()\nto_monet = test_generator(noise_input)\nprint ('Time for one generated_image from photo is {} sec'.format(time.time()-start))\n\nplt.subplot(1, 2, 1)\nplt.title(\"Original Photo\")\nplt.imshow(example_photo[0] * 0.5 + 0.5)\n\nplt.subplot(1, 2, 2)\nplt.title(\"Monet-esque Photo\")\nplt.imshow(to_monet[0] * 0.5 + 0.5)\nplt.show()\"\"\"","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:45.839853Z","iopub.execute_input":"2023-03-29T23:58:45.840530Z","iopub.status.idle":"2023-03-29T23:58:45.848757Z","shell.execute_reply.started":"2023-03-29T23:58:45.840481Z","shell.execute_reply":"2023-03-29T23:58:45.847667Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Image generated from a random image\nnoise = tf.random.normal([1, 100])\ngenerated_image = test_generator(noise, training=False)\n\nplt.imshow(generated_image[0, :, :, 0])","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:45.852452Z","iopub.execute_input":"2023-03-29T23:58:45.852780Z","iopub.status.idle":"2023-03-29T23:58:46.172384Z","shell.execute_reply.started":"2023-03-29T23:58:45.852751Z","shell.execute_reply":"2023-03-29T23:58:46.171128Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Exemple of generated images from a photo input passed through ou basic generator\nstart = time.time()\nnoise_to_monet = test_generator(noise)\nprint ('Time for one generated_image from noise is {} sec'.format(time.time()-start))\n\nplt.subplot(1, 2, 1)\nplt.title(\"Original noise\")\nplt.imshow(generated_image[0, :, :, 0])\n\nplt.subplot(1, 2, 2)\nplt.title(\"Monet-esque noise\")\nplt.imshow(noise_to_monet[0] * 0.5 + 0.5)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:46.174382Z","iopub.execute_input":"2023-03-29T23:58:46.174751Z","iopub.status.idle":"2023-03-29T23:58:46.533169Z","shell.execute_reply.started":"2023-03-29T23:58:46.174718Z","shell.execute_reply":"2023-03-29T23:58:46.531776Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"generated_image.shape","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:46.534901Z","iopub.execute_input":"2023-03-29T23:58:46.535272Z","iopub.status.idle":"2023-03-29T23:58:46.542301Z","shell.execute_reply.started":"2023-03-29T23:58:46.535239Z","shell.execute_reply":"2023-03-29T23:58:46.541204Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"generated_image[0].shape","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:46.543875Z","iopub.execute_input":"2023-03-29T23:58:46.544200Z","iopub.status.idle":"2023-03-29T23:58:46.555469Z","shell.execute_reply.started":"2023-03-29T23:58:46.544171Z","shell.execute_reply":"2023-03-29T23:58:46.554228Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"decision = test_discriminator(example_photo)\nprint (decision)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:46.556707Z","iopub.execute_input":"2023-03-29T23:58:46.557095Z","iopub.status.idle":"2023-03-29T23:58:50.235137Z","shell.execute_reply.started":"2023-03-29T23:58:46.557060Z","shell.execute_reply":"2023-03-29T23:58:50.233159Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"decision = test_discriminator(generated_image)\nprint (decision)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.236422Z","iopub.execute_input":"2023-03-29T23:58:50.236747Z","iopub.status.idle":"2023-03-29T23:58:50.367570Z","shell.execute_reply.started":"2023-03-29T23:58:50.236718Z","shell.execute_reply":"2023-03-29T23:58:50.365481Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 4.3. Buid the GAN\n\n### 4.3.1. Define the loss and optimizers  \n\nIn this part, we'll focus more on the archhitecture of the GAN, and wich loss functions will be used.  \nAn detailed explanation of how the loss function was chosen can be found here: https://arxiv.org/pdf/1406.2661.pdf  \nLet's build the loss functions:","metadata":{}},{"cell_type":"code","source":"with strategy.scope():\n    # This method returns a helper function to compute cross entropy loss\n    #cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True,reduction=tf.keras.losses.Reduction.NONE)\n    cross_entropy = tf.keras.losses.BinaryCrossentropy(reduction=tf.keras.losses.Reduction.NONE)\n    \n    # from_logits=True if last layer is softmax (model output : -inf;+inf), False by default if if last layer is sigmoid (model output: [0,1])\n    # BinaryCrossentropy: 1 - P with P:[0,1]\n    \n    # we need to use reduction=tf.keras.losses.Reduction.NONE because of the use of strategy.scope()\n    \n    \n    \"\"\"\n    ValueError: Please use `tf.keras.losses.Reduction.SUM` or `tf.keras.losses.Reduction.NONE` for loss reduction when losses are used with `tf.distribute.Strategy` outside of the built-in training loops. You can implement `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` using global batch size like:\n    ```\n    with strategy.scope():\n        loss_obj = tf.keras.losses.CategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE)\n    ....\n        loss = tf.reduce_sum(loss_obj(labels, predictions)) * (1. / global_batch_size)\n    ```\n    Please see https://www.tensorflow.org/tutorials/distribute/custom_training for more details.\n    \"\"\"\n\n# UserWarning: \"`binary_crossentropy` received `from_logits=True`, but the `output` argument was produced by a sigmoid or softmax activation and thus does not represent logits. Was this intended?\"\n#  '\"`binary_crossentropy` received `from_logits=True`, but the `output`'\n# --> If the discriminator end with sigmoid or softmax, the output will be between 0 to 1, or from_logits=True expect value from -inf to inf. If from_logits=False, values expected are from 0 to 1. That's why we could see an error.","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.368894Z","iopub.execute_input":"2023-03-29T23:58:50.369252Z","iopub.status.idle":"2023-03-29T23:58:50.375340Z","shell.execute_reply.started":"2023-03-29T23:58:50.369221Z","shell.execute_reply":"2023-03-29T23:58:50.374394Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with strategy.scope():\n    def discriminator_loss(real_output, fake_output):\n        real_loss = cross_entropy(tf.ones_like(real_output), real_output)\n        fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)\n        total_loss = real_loss + fake_loss\n        return total_loss","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.376335Z","iopub.execute_input":"2023-03-29T23:58:50.377166Z","iopub.status.idle":"2023-03-29T23:58:50.394278Z","shell.execute_reply.started":"2023-03-29T23:58:50.377132Z","shell.execute_reply":"2023-03-29T23:58:50.393285Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with strategy.scope():\n    def generator_loss(fake_output):\n        return cross_entropy(tf.ones_like(fake_output), fake_output)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.395419Z","iopub.execute_input":"2023-03-29T23:58:50.396489Z","iopub.status.idle":"2023-03-29T23:58:50.409765Z","shell.execute_reply.started":"2023-03-29T23:58:50.396454Z","shell.execute_reply":"2023-03-29T23:58:50.408880Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with strategy.scope():\n    generator_optimizer = tf.keras.optimizers.Adam(1e-4)\n    discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.410894Z","iopub.execute_input":"2023-03-29T23:58:50.411665Z","iopub.status.idle":"2023-03-29T23:58:50.423112Z","shell.execute_reply.started":"2023-03-29T23:58:50.411632Z","shell.execute_reply":"2023-03-29T23:58:50.422085Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We can test these functions:","metadata":{}},{"cell_type":"code","source":"generator_loss(noise_to_monet)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.424413Z","iopub.execute_input":"2023-03-29T23:58:50.425277Z","iopub.status.idle":"2023-03-29T23:58:50.462595Z","shell.execute_reply.started":"2023-03-29T23:58:50.425243Z","shell.execute_reply":"2023-03-29T23:58:50.461447Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"discriminator_loss(example_photo,noise_to_monet)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.464735Z","iopub.execute_input":"2023-03-29T23:58:50.465243Z","iopub.status.idle":"2023-03-29T23:58:50.548003Z","shell.execute_reply.started":"2023-03-29T23:58:50.465181Z","shell.execute_reply":"2023-03-29T23:58:50.546644Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"(generator_loss(noise_to_monet) + discriminator_loss(example_photo,noise_to_monet)) /2","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.549808Z","iopub.execute_input":"2023-03-29T23:58:50.550311Z","iopub.status.idle":"2023-03-29T23:58:50.628522Z","shell.execute_reply.started":"2023-03-29T23:58:50.550269Z","shell.execute_reply":"2023-03-29T23:58:50.627363Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 4.3.2. Save checkpoints","metadata":{}},{"cell_type":"code","source":"# Some tools to save the model during training if an exception occured\n\ncheckpoint_dir = './training_checkpoints'\ncheckpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\ncheckpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n                                 discriminator_optimizer=discriminator_optimizer,\n                                 generator=test_generator,\n                                 discriminator=test_discriminator)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.629971Z","iopub.execute_input":"2023-03-29T23:58:50.630383Z","iopub.status.idle":"2023-03-29T23:58:50.637566Z","shell.execute_reply.started":"2023-03-29T23:58:50.630351Z","shell.execute_reply":"2023-03-29T23:58:50.636219Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Saving model directories\n\nmodel_dir = './training_model'\ngen_file = os.path.join(model_dir, \"model_generator\")\ndisc_file = os.path.join(model_dir, \"model_discriminator\")","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.639190Z","iopub.execute_input":"2023-03-29T23:58:50.640068Z","iopub.status.idle":"2023-03-29T23:58:50.649410Z","shell.execute_reply.started":"2023-03-29T23:58:50.640031Z","shell.execute_reply":"2023-03-29T23:58:50.648353Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Tools to save images during training and we'll be used for generating a GIF\n\ndef generate_and_save_images(model, epoch, test_input):\n    # Notice `training` is set to False.\n    # This is so all layers run in inference mode (batchnorm).\n    predictions = model(test_input, training=False)\n\n    fig = plt.figure(figsize=(10, 10))\n\n    for i in range(predictions.shape[0]):\n        raw = int(np.around(num_examples_to_generate/5))\n        col = 5\n        \n        plt.subplot(raw, col, i+1)\n        plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5)\n        plt.axis('off')\n\n    plt.savefig('img_epochs/image_at_epoch_{:04d}.png'.format(epoch))\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.651292Z","iopub.execute_input":"2023-03-29T23:58:50.651732Z","iopub.status.idle":"2023-03-29T23:58:50.662476Z","shell.execute_reply.started":"2023-03-29T23:58:50.651690Z","shell.execute_reply":"2023-03-29T23:58:50.661284Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 4.3.3. Define training loop  \n\nHere we will define the different training steps and loop to train our model.  \nFor better understanding, and because we think of using TPU, different ways to train our models will be implemented and tested.","metadata":{}},{"cell_type":"code","source":"# Some inputs used for training loop\nnoise_dim = 100\nnum_examples_to_generate = 3\nBATCH_SIZE = batch_size # Comme définit en entrée\n#img_sz = 256\n\n# You will reuse this seed overtime (so it's easier)\n# to visualize progress in the animated GIF)\nseed = tf.random.normal([num_examples_to_generate,noise_dim ])","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.664085Z","iopub.execute_input":"2023-03-29T23:58:50.664598Z","iopub.status.idle":"2023-03-29T23:58:50.683035Z","shell.execute_reply.started":"2023-03-29T23:58:50.664566Z","shell.execute_reply":"2023-03-29T23:58:50.682077Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class GAN_class(tf.keras.Model):\n    def __init__(self, generator, discriminator):\n        super(GAN_class, self).__init__()\n        self.generator = generator\n        self.discriminator = discriminator\n        \n    # Notice the use of `tf.function`\n    # This annotation causes the function to be \"compiled\".\n    @tf.function\n    def train_step_to_distribute(self, images):\n        noise = tf.random.normal([BATCH_SIZE, noise_dim])\n\n        with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n            generated_images = self.generator(noise, training=True)\n\n            real_output = self.discriminator(images, training=True)\n            fake_output = self.discriminator(generated_images, training=True)\n\n            gen_loss = generator_loss(fake_output)\n            disc_loss = discriminator_loss(real_output, fake_output)\n\n        gradients_of_generator = gen_tape.gradient(gen_loss, self.generator.trainable_variables)\n        gradients_of_discriminator = disc_tape.gradient(disc_loss, self.discriminator.trainable_variables)\n\n        generator_optimizer.apply_gradients(zip(gradients_of_generator, self.generator.trainable_variables))\n        discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, self.discriminator.trainable_variables))\n\n        # add codes to save losses\n        #return [gen_loss, disc_loss]\n        return (gen_loss + disc_loss)/2\n    \n    @tf.function\n    def distributed_train_step(self, images):\n        #images,generator,discriminator = dataset_inputs\n        per_replica_losses = strategy.run(self.train_step_to_distribute, args=(images,))\n        return strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_losses,\n                             axis=None)\n    \n    def train(self, dataset, epochs):\n        gen_loss_list  = []\n        disc_loss_list = []\n        train_loss_list = []\n        decision_list = []\n        \n        for epoch in range(epochs):\n            start = time.time()\n\n            total_loss = 0.0\n            num_batches = 0\n            for image_batch in dataset:\n                loss = self.distributed_train_step(image_batch)\n                total_loss += tf.reduce_mean(loss)\n                num_batches += 1\n                train_loss = total_loss / num_batches\n                \n            train_loss_list.append(train_loss)\n\n                #[gen_loss, disc_loss] = train_step(image_batch,generator,discriminator)\n                #gen_loss_list.append(gen_loss)\n                #disc_loss_list.append(disc_loss)\n\n            # Produce images for the GIF as you go\n            display.clear_output(wait=True)\n            generate_and_save_images(self.generator,\n                                     epoch + 1,\n                                     seed)\n\n            # Save the model every 15 epochs\n            #if (epoch + 1) % 15 == 0:\n            #    checkpoint.save(file_prefix = checkpoint_prefix)\n\n            print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start)) # each line printed is overwritten. The last one is not visible # -> because of display.clear_output(wait=True)?\n\n\n            # Evaluate the model\n            #loss, acc = generator.evaluate(test_images, test_labels, verbose=2)\n            #print(\"Untrained model, accuracy: {:5.2f}%\".format(100 * acc))\n\n\n        # Generate after the final epoch\n        display.clear_output(wait=True)\n        generate_and_save_images(self.generator,\n                               epochs,\n                               seed)\n\n        # Losses saved in lists\n        #return gen_loss_list, disc_loss_list\n        return train_loss_list\n    ","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.684366Z","iopub.execute_input":"2023-03-29T23:58:50.684699Z","iopub.status.idle":"2023-03-29T23:58:50.702494Z","shell.execute_reply.started":"2023-03-29T23:58:50.684670Z","shell.execute_reply":"2023-03-29T23:58:50.700990Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class GAN_class_bis(tf.keras.Model):\n    def __init__(self, generator, discriminator):\n        super(GAN_class_bis, self).__init__()\n        self.generator = generator\n        self.discriminator = discriminator\n        \n    def compile(self, generator_optimizer, discriminator_optimizer, generator_loss, discriminator_loss):\n        super(GAN_class_bis, self).compile()\n        self.generator_optimizer = generator_optimizer\n        self.discriminator_optimizer = discriminator_optimizer\n        self.generator_loss = generator_loss\n        self.discriminator_loss = discriminator_loss\n        \n    def train_step(self, images):\n        \n        noise = tf.random.normal([BATCH_SIZE, noise_dim])\n\n        with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n            generated_images = self.generator(noise, training=True)\n\n            real_output = self.discriminator(images, training=True)\n            fake_output = self.discriminator(generated_images, training=True)\n\n            gen_loss = self.generator_loss(fake_output)\n            disc_loss = self.discriminator_loss(real_output, fake_output)\n\n        gradients_of_generator = gen_tape.gradient(gen_loss, self.generator.trainable_variables)\n        gradients_of_discriminator = disc_tape.gradient(disc_loss, self.discriminator.trainable_variables)\n\n        self.generator_optimizer.apply_gradients(zip(gradients_of_generator, self.generator.trainable_variables))\n        self.discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, self.discriminator.trainable_variables))\n\n        # add codes to save losses\n        #return [gen_loss, disc_loss]\n        return {\n            \"gen_loss\": gen_loss,\n            \"disc_loss\": disc_loss,\n            \"decision\":fake_output\n            }\n ","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.707350Z","iopub.execute_input":"2023-03-29T23:58:50.707730Z","iopub.status.idle":"2023-03-29T23:58:50.728541Z","shell.execute_reply.started":"2023-03-29T23:58:50.707696Z","shell.execute_reply":"2023-03-29T23:58:50.727131Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class GAN_class_2(tf.keras.Model):\n    def __init__(self, generator, discriminator):\n        super(GAN_class_2, self).__init__()\n        self.generator = generator\n        self.discriminator = discriminator\n        \n    def compile(self, generator_optimizer, discriminator_optimizer, generator_loss, discriminator_loss):\n        super(GAN_class_2, self).compile()\n        self.generator_optimizer = generator_optimizer\n        self.discriminator_optimizer = discriminator_optimizer\n        self.generator_loss = generator_loss\n        self.discriminator_loss = discriminator_loss\n    \n    #@tf.function\n    def train_step(self, inputs):\n        def step_fn(self, images):\n\n            noise = tf.random.normal([BATCH_SIZE, noise_dim])\n\n            with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n                generated_images = self.generator(noise, training=True)\n\n                real_output = self.discriminator(images, training=True)\n                fake_output = self.discriminator(generated_images, training=True)\n\n                gen_loss = self.generator_loss(fake_output)\n                disc_loss = self.discriminator_loss(real_output, fake_output)\n\n            gradients_of_generator = gen_tape.gradient(gen_loss, self.generator.trainable_variables)\n            gradients_of_discriminator = disc_tape.gradient(disc_loss, self.discriminator.trainable_variables)\n\n            self.generator_optimizer.apply_gradients(zip(gradients_of_generator, self.generator.trainable_variables))\n            self.discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, self.discriminator.trainable_variables))\n\n            # add codes to save losses\n            #return [gen_loss, disc_loss]\n            return {\n                \"gen_loss\": gen_loss,\n                \"disc_loss\": disc_loss\n                }\n        per_replica_losses = strategy.run(step_fn, args=(inputs,))\n        return strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_losses,\n                             axis=None)\n        \n        \n        \"\"\"\n            @tf.function\n    def distributed_train_step(self, images):\n        #images,generator,discriminator = dataset_inputs\n        per_replica_losses = strategy.run(self.train_step_to_distribute, args=(images,))\n        return strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_losses,\n                             axis=None)\n        \"\"\"","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.732773Z","iopub.execute_input":"2023-03-29T23:58:50.733176Z","iopub.status.idle":"2023-03-29T23:58:50.747839Z","shell.execute_reply.started":"2023-03-29T23:58:50.733140Z","shell.execute_reply":"2023-03-29T23:58:50.746496Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 5. Train the GAN","metadata":{}},{"cell_type":"code","source":"EPOCHS = 500 #1200\n\nmake_submission = 0 # 0: no generation of images for submission, 1: generate images.zip for submission\n\n\n# Choice of GAN strategy\n\niGAN = 1\n\n# 0: monet_gan from GAN_class using distribute and train\n# 1: monet_gan_bis from GAN_class_bis using compile and fit\n# 2: monet_gan_2 from GAN_class_2 using compile and fit and distribute -> not always working without TPU","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.749437Z","iopub.execute_input":"2023-03-29T23:58:50.749771Z","iopub.status.idle":"2023-03-29T23:58:50.764702Z","shell.execute_reply.started":"2023-03-29T23:58:50.749742Z","shell.execute_reply":"2023-03-29T23:58:50.763529Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#with strategy.scope():\n#[gen_loss_list, disc_loss_list] = train(monet_ds, EPOCHS,test_generator,test_discriminator)\n#train_loss = train(monet_ds, EPOCHS,test_generator,test_discriminator)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.766117Z","iopub.execute_input":"2023-03-29T23:58:50.766438Z","iopub.status.idle":"2023-03-29T23:58:50.775929Z","shell.execute_reply.started":"2023-03-29T23:58:50.766408Z","shell.execute_reply":"2023-03-29T23:58:50.774884Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#len(gen_loss_list)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.777172Z","iopub.execute_input":"2023-03-29T23:58:50.777668Z","iopub.status.idle":"2023-03-29T23:58:50.788607Z","shell.execute_reply.started":"2023-03-29T23:58:50.777635Z","shell.execute_reply":"2023-03-29T23:58:50.787432Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#len(disc_loss_list)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.789952Z","iopub.execute_input":"2023-03-29T23:58:50.790827Z","iopub.status.idle":"2023-03-29T23:58:50.798933Z","shell.execute_reply.started":"2023-03-29T23:58:50.790791Z","shell.execute_reply":"2023-03-29T23:58:50.797830Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\"\"\"\nfig = plt.figure(figsize =(10, 7))\nplt.plot(gen_loss_list)\nplt.plot(disc_loss_list)\n\nplt.plot(np.mean(gen_loss_list),marker='o')\nplt.plot(np.mean(disc_loss_list),marker='o')\n\nplt.plot([0,100],[np.mean(gen_loss_list),np.mean(gen_loss_list)])\nplt.plot([0,100],[np.mean(disc_loss_list),np.mean(disc_loss_list)])\n\nplt.legend(['gen_loss_list','disc_loss_list',\n           'mean_gen_loss_list','mean_disc_loss_list'])\n\nplt.xlabel(\"Image number\")\nplt.ylabel(\"Generator and Discriminator losses\")\n\nplt.show()\n\"\"\"","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.800161Z","iopub.execute_input":"2023-03-29T23:58:50.801054Z","iopub.status.idle":"2023-03-29T23:58:50.814254Z","shell.execute_reply.started":"2023-03-29T23:58:50.800988Z","shell.execute_reply":"2023-03-29T23:58:50.813058Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Restore latest checkpoint","metadata":{}},{"cell_type":"code","source":"if iGAN ==0:\n    import os\n    os.mkdir(\"img_epochs\")","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.815536Z","iopub.execute_input":"2023-03-29T23:58:50.816289Z","iopub.status.idle":"2023-03-29T23:58:50.824766Z","shell.execute_reply.started":"2023-03-29T23:58:50.816245Z","shell.execute_reply":"2023-03-29T23:58:50.823756Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.825980Z","iopub.execute_input":"2023-03-29T23:58:50.827060Z","iopub.status.idle":"2023-03-29T23:58:50.844114Z","shell.execute_reply.started":"2023-03-29T23:58:50.826998Z","shell.execute_reply":"2023-03-29T23:58:50.842789Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if iGAN ==0:\n    with strategy.scope():\n        monet_gan = GAN_class(test_generator, test_discriminator)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.845382Z","iopub.execute_input":"2023-03-29T23:58:50.846584Z","iopub.status.idle":"2023-03-29T23:58:50.855285Z","shell.execute_reply.started":"2023-03-29T23:58:50.846536Z","shell.execute_reply":"2023-03-29T23:58:50.854157Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":" #%debug\nif iGAN ==0:\n    #! mkdir img_epochs\n    train_loss_result = monet_gan.train(monet_ds, EPOCHS)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.856503Z","iopub.execute_input":"2023-03-29T23:58:50.857645Z","iopub.status.idle":"2023-03-29T23:58:50.868893Z","shell.execute_reply.started":"2023-03-29T23:58:50.857601Z","shell.execute_reply":"2023-03-29T23:58:50.867816Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if iGAN ==0:\n    fig = plt.figure(figsize =(10, 7))\n    plt.plot(train_loss_result)\n\n    plt.plot(np.mean(train_loss_result),marker='o')\n\n    plt.plot([0,len(train_loss_result)],[np.mean(train_loss_result),np.mean(train_loss_result)])\n\n    plt.legend(['train_loss_result','mean_train_loss_result'])\n\n    plt.xlabel(\"Image number\")\n    plt.ylabel(\"Generator and Discriminator losses\")\n\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.870249Z","iopub.execute_input":"2023-03-29T23:58:50.871393Z","iopub.status.idle":"2023-03-29T23:58:50.880919Z","shell.execute_reply.started":"2023-03-29T23:58:50.871359Z","shell.execute_reply":"2023-03-29T23:58:50.879979Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if iGAN ==1:\n    with strategy.scope():\n        monet_gan_bis = GAN_class_bis(test_generator, test_discriminator)\n        monet_gan_bis.compile(generator_optimizer, discriminator_optimizer, generator_loss, discriminator_loss)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.882107Z","iopub.execute_input":"2023-03-29T23:58:50.883002Z","iopub.status.idle":"2023-03-29T23:58:50.910525Z","shell.execute_reply.started":"2023-03-29T23:58:50.882970Z","shell.execute_reply":"2023-03-29T23:58:50.909268Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#monet_gan_bis.generator.trainable_variables","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.911853Z","iopub.execute_input":"2023-03-29T23:58:50.912705Z","iopub.status.idle":"2023-03-29T23:58:50.918381Z","shell.execute_reply.started":"2023-03-29T23:58:50.912656Z","shell.execute_reply":"2023-03-29T23:58:50.917102Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if iGAN ==1:\n    history = monet_gan_bis.fit(monet_ds, epochs = EPOCHS)","metadata":{"execution":{"iopub.status.busy":"2023-03-29T23:58:50.931466Z","iopub.execute_input":"2023-03-29T23:58:50.932618Z","iopub.status.idle":"2023-03-30T01:56:11.114580Z","shell.execute_reply.started":"2023-03-29T23:58:50.932573Z","shell.execute_reply":"2023-03-30T01:56:11.113564Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#monet_gan_bis.generator(noise).shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:11.116368Z","iopub.execute_input":"2023-03-30T01:56:11.116971Z","iopub.status.idle":"2023-03-30T01:56:11.121005Z","shell.execute_reply.started":"2023-03-30T01:56:11.116939Z","shell.execute_reply":"2023-03-30T01:56:11.120261Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if iGAN ==1:\n    fig = plt.figure(figsize =(10, 7))\n    #plt.plot([0,len(history.history['gen_loss'])], history.history['gen_loss'])\n    plt.plot(np.mean(history.history['gen_loss'], axis=1))\n    plt.plot(np.mean(history.history['disc_loss'], axis=1))\n    plt.plot(np.mean(history.history['decision'], axis=1))\n\n    plt.plot(np.mean(np.mean(history.history['gen_loss'], axis=1)),marker='o')\n    plt.plot(np.mean(np.mean(history.history['disc_loss'], axis=1)),marker='o')\n    plt.plot(np.mean(np.mean(history.history['decision'], axis=1)),marker='o')\n\n    plt.plot([0,len(np.mean(history.history['gen_loss'], axis=1))],[np.mean(np.mean(history.history['gen_loss'], axis=1)),np.mean(np.mean(history.history['gen_loss'], axis=1))])\n    plt.plot([0,len(np.mean(history.history['disc_loss'], axis=1))],[np.mean(np.mean(history.history['disc_loss'], axis=1)),np.mean(np.mean(history.history['disc_loss'], axis=1))])\n    plt.plot([0,len(np.mean(history.history['decision'], axis=1))],[np.mean(np.mean(history.history['decision'], axis=1)),np.mean(np.mean(history.history['decision'], axis=1))])\n\n\n    plt.legend(['gen_loss','disc_loss','decision','mean_gen_loss','mean_disc_loss','mean_decision'])\n\n    plt.xlabel(\"Image batch number\")\n    plt.ylabel(\"Generator and Discriminator losses\")\n\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:11.122414Z","iopub.execute_input":"2023-03-30T01:56:11.122988Z","iopub.status.idle":"2023-03-30T01:56:11.430574Z","shell.execute_reply.started":"2023-03-30T01:56:11.122956Z","shell.execute_reply":"2023-03-30T01:56:11.429421Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#history.history['gen_loss']","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:11.432242Z","iopub.execute_input":"2023-03-30T01:56:11.432867Z","iopub.status.idle":"2023-03-30T01:56:11.436826Z","shell.execute_reply.started":"2023-03-30T01:56:11.432832Z","shell.execute_reply":"2023-03-30T01:56:11.435915Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#np.mean(history.history['gen_loss'], axis=1)","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:11.438350Z","iopub.execute_input":"2023-03-30T01:56:11.439038Z","iopub.status.idle":"2023-03-30T01:56:11.453312Z","shell.execute_reply.started":"2023-03-30T01:56:11.438982Z","shell.execute_reply":"2023-03-30T01:56:11.451931Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#history.history['gen_loss'][0]","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:11.455407Z","iopub.execute_input":"2023-03-30T01:56:11.455786Z","iopub.status.idle":"2023-03-30T01:56:11.467560Z","shell.execute_reply.started":"2023-03-30T01:56:11.455753Z","shell.execute_reply":"2023-03-30T01:56:11.466276Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#history.history['gen_loss'][0][-1]","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:11.469437Z","iopub.execute_input":"2023-03-30T01:56:11.470262Z","iopub.status.idle":"2023-03-30T01:56:11.482268Z","shell.execute_reply.started":"2023-03-30T01:56:11.470212Z","shell.execute_reply":"2023-03-30T01:56:11.481131Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#history.history['decision']","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:11.484107Z","iopub.execute_input":"2023-03-30T01:56:11.484929Z","iopub.status.idle":"2023-03-30T01:56:11.497486Z","shell.execute_reply.started":"2023-03-30T01:56:11.484878Z","shell.execute_reply":"2023-03-30T01:56:11.496273Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#np.mean(history.history['decision'], axis=1)","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:11.499293Z","iopub.execute_input":"2023-03-30T01:56:11.502773Z","iopub.status.idle":"2023-03-30T01:56:11.513107Z","shell.execute_reply.started":"2023-03-30T01:56:11.502711Z","shell.execute_reply":"2023-03-30T01:56:11.511833Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#history.history['gen_loss'][][-1]","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:11.517346Z","iopub.execute_input":"2023-03-30T01:56:11.517967Z","iopub.status.idle":"2023-03-30T01:56:11.525746Z","shell.execute_reply.started":"2023-03-30T01:56:11.517905Z","shell.execute_reply":"2023-03-30T01:56:11.524235Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if iGAN ==2:\n    with strategy.scope():\n        monet_gan_2 = GAN_class_2(test_generator, test_discriminator)\n        monet_gan_2.compile(generator_optimizer, discriminator_optimizer, generator_loss, discriminator_loss)","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:11.527525Z","iopub.execute_input":"2023-03-30T01:56:11.527995Z","iopub.status.idle":"2023-03-30T01:56:11.539515Z","shell.execute_reply.started":"2023-03-30T01:56:11.527945Z","shell.execute_reply":"2023-03-30T01:56:11.538309Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if iGAN ==2:\n    monet_gan_2.fit(monet_ds, epochs = EPOCHS)","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:11.541625Z","iopub.execute_input":"2023-03-30T01:56:11.542609Z","iopub.status.idle":"2023-03-30T01:56:11.552007Z","shell.execute_reply.started":"2023-03-30T01:56:11.542547Z","shell.execute_reply":"2023-03-30T01:56:11.551067Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# #Stop runing\n#import sys\n#sys.exit()","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:11.554132Z","iopub.execute_input":"2023-03-30T01:56:11.554920Z","iopub.status.idle":"2023-03-30T01:56:11.567701Z","shell.execute_reply.started":"2023-03-30T01:56:11.554869Z","shell.execute_reply":"2023-03-30T01:56:11.566705Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 6. Test the GAN ","metadata":{}},{"cell_type":"code","source":"if iGAN == 0:\n    generator_to_test = monet_gan.generator\nelif iGAN == 1:\n    generator_to_test = monet_gan_bis.generator\nelif iGAN == 2:\n    generator_to_test = monet_gan_2.generator\n\n\nfig = plt.figure(figsize=(10, 10))\n\n#num_examples_to_generate = 10\nfor i in range(generator_to_test(seed).shape[0]):\n    raw = int(np.around(num_examples_to_generate/5))\n    col = 5\n\n    plt.subplot(raw, col, i+1)\n    plt.imshow(generator_to_test(seed)[i, :, :, 0] * 127.5 + 127.5)\n    plt.axis('off')","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:11.569793Z","iopub.execute_input":"2023-03-30T01:56:11.570696Z","iopub.status.idle":"2023-03-30T01:56:12.911070Z","shell.execute_reply.started":"2023-03-30T01:56:11.570647Z","shell.execute_reply":"2023-03-30T01:56:12.909935Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Création de GIF","metadata":{}},{"cell_type":"code","source":"if iGAN ==0:\n    # Display a single image using the epoch number\n    def display_image(epoch_no):\n        return PIL.Image.open('img_epochs/image_at_epoch_{:04d}.png'.format(epoch_no))","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:12.912825Z","iopub.execute_input":"2023-03-30T01:56:12.913545Z","iopub.status.idle":"2023-03-30T01:56:12.919311Z","shell.execute_reply.started":"2023-03-30T01:56:12.913500Z","shell.execute_reply":"2023-03-30T01:56:12.918224Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if iGAN ==0:\n    display_image(EPOCHS)","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:12.921250Z","iopub.execute_input":"2023-03-30T01:56:12.921696Z","iopub.status.idle":"2023-03-30T01:56:12.934266Z","shell.execute_reply.started":"2023-03-30T01:56:12.921653Z","shell.execute_reply":"2023-03-30T01:56:12.933082Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Animated Gif","metadata":{}},{"cell_type":"code","source":"if iGAN ==0:\n    import imageio.v2 as imageio\n\n    anim_file = 'dcgan.gif'\n\n    with imageio.get_writer(anim_file, mode='I') as writer:\n        filenames = glob.glob('img_epochs/image*.png')\n        filenames = sorted(filenames)\n        for filename in filenames:\n            image = imageio.imread(filename)\n            writer.append_data(image)\n        image = imageio.imread(filename)\n        writer.append_data(image)","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:12.936181Z","iopub.execute_input":"2023-03-30T01:56:12.936532Z","iopub.status.idle":"2023-03-30T01:56:12.946770Z","shell.execute_reply.started":"2023-03-30T01:56:12.936502Z","shell.execute_reply":"2023-03-30T01:56:12.945683Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if iGAN ==0:\n    # To generate GIFs\n    ! pip install imageio\n    ! pip install git+https://github.com/tensorflow/docs","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:12.948149Z","iopub.execute_input":"2023-03-30T01:56:12.948485Z","iopub.status.idle":"2023-03-30T01:56:12.959468Z","shell.execute_reply.started":"2023-03-30T01:56:12.948454Z","shell.execute_reply":"2023-03-30T01:56:12.958225Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if iGAN ==0:\n    import tensorflow_docs.vis.embed as embed\n    embed.embed_file(anim_file)","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:12.961209Z","iopub.execute_input":"2023-03-30T01:56:12.962129Z","iopub.status.idle":"2023-03-30T01:56:12.971072Z","shell.execute_reply.started":"2023-03-30T01:56:12.962081Z","shell.execute_reply":"2023-03-30T01:56:12.970086Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 7. Save model","metadata":{}},{"cell_type":"code","source":"# Saving model\n\nmodel_dir = './training_model'\ngen_file = os.path.join(model_dir, \"model_generator\")\ndisc_file = os.path.join(model_dir, \"model_discriminator\")","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:12.972591Z","iopub.execute_input":"2023-03-30T01:56:12.973035Z","iopub.status.idle":"2023-03-30T01:56:12.985441Z","shell.execute_reply.started":"2023-03-30T01:56:12.972971Z","shell.execute_reply":"2023-03-30T01:56:12.984373Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Methods for saving the generated folder of saved trained model in a file that we can easily download in kaggle\nfrom zipfile import ZipFile\nimport os\n\ndef save_model_zip(test_folder,zip_file):\n\n    #zip_file = \"test_folder.zip\"  # zip file name\n    directory = test_folder\n\n    with ZipFile(zip_file, 'w') as zip_obj:\n        for path, directories, files in os.walk(directory):\n            for file in files:\n                file_name = os.path.join(path, file)\n\n                zip_obj.write(file_name) # zipping the file\n\n    print(\"Contents of the zip file:\")\n    with ZipFile(zip_file, 'r') as zip_obj:\n        zip_obj.printdir()","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:12.986860Z","iopub.execute_input":"2023-03-30T01:56:12.987297Z","iopub.status.idle":"2023-03-30T01:56:12.999831Z","shell.execute_reply.started":"2023-03-30T01:56:12.987266Z","shell.execute_reply":"2023-03-30T01:56:12.998518Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Saving actions if using CPU/GPU\n\"\"\"   \nmonet_gan.generator.save(gen_file) # + str(epoch))\nmonet_gan.discriminator.save(disc_file) # + str(epoch))\n\nzip_file = os.path.join(model_dir, \"gan_model_file.zip\")\n#save_model_zip(gen_file,zip_file)\n#save_model_zip(disc_file,zip_file) # cet enregistrement écrase le précédent\nsave_model_zip(model_dir,zip_file)\"\"\"\n\n#from IPython.display import FileLink\n#FileLink(gen_file)\n#FileLink(disc_file)","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:13.001816Z","iopub.execute_input":"2023-03-30T01:56:13.002206Z","iopub.status.idle":"2023-03-30T01:56:13.019467Z","shell.execute_reply.started":"2023-03-30T01:56:13.002172Z","shell.execute_reply":"2023-03-30T01:56:13.018442Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Saving actions if using TPU\n\nsave_locally = tf.saved_model.SaveOptions(experimental_io_device='/job:localhost')\n\nif iGAN ==0:\n    monet_gan.generator.save(gen_file, options=save_locally) # + str(epoch))\n    monet_gan.discriminator.save(disc_file, options=save_locally) \nelif iGAN ==1:\n    monet_gan_bis.generator.save(gen_file, options=save_locally) # + str(epoch))\n    monet_gan_bis.discriminator.save(disc_file, options=save_locally) \nelif iGAN ==2:\n    monet_gan_2.generator.save(gen_file, options=save_locally) # + str(epoch))\n    monet_gan_2.discriminator.save(disc_file, options=save_locally)     \n    \nzip_file = os.path.join(model_dir, \"gan_model_file.zip\")\n#save_model_zip(gen_file,zip_file)\n#save_model_zip(disc_file,zip_file) # cet enregistrement écrase le précédent\nsave_model_zip(model_dir,zip_file)","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:13.021074Z","iopub.execute_input":"2023-03-30T01:56:13.021391Z","iopub.status.idle":"2023-03-30T01:56:20.014337Z","shell.execute_reply.started":"2023-03-30T01:56:13.021363Z","shell.execute_reply":"2023-03-30T01:56:20.013000Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 8. Create submission file","metadata":{}},{"cell_type":"code","source":"if make_submission == 1:\n    ! mkdir ../images","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:20.016315Z","iopub.execute_input":"2023-03-30T01:56:20.017129Z","iopub.status.idle":"2023-03-30T01:56:20.023334Z","shell.execute_reply.started":"2023-03-30T01:56:20.017080Z","shell.execute_reply":"2023-03-30T01:56:20.022088Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if make_submission == 1:\n    i = 1\n    nb_img2gen = 7000\n    for img in range(nb_img2gen):\n        noise = tf.random.normal([1, 100])\n\n        if iGAN ==0:\n            prediction = monet_gan.generator(noise, training=False)[0].numpy()\n        elif iGAN ==1:\n            prediction = monet_gan_bis.generator(noise, training=False)[0].numpy()\n        elif iGAN ==2:\n            prediction = monet_gan_2.generator(noise, training=False)[0].numpy()\n\n        prediction = (prediction * 127.5 + 127.5).astype(np.uint8)\n        im = PIL.Image.fromarray(prediction)\n        im.save(\"../images/\" + str(i) + \".jpg\")\n        i += 1","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:20.025192Z","iopub.execute_input":"2023-03-30T01:56:20.026100Z","iopub.status.idle":"2023-03-30T01:56:20.041909Z","shell.execute_reply.started":"2023-03-30T01:56:20.026062Z","shell.execute_reply":"2023-03-30T01:56:20.040400Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if make_submission == 1:\n    import shutil\n    shutil.make_archive(\"/kaggle/working/images\", 'zip', \"/kaggle/images\")","metadata":{"execution":{"iopub.status.busy":"2023-03-30T01:56:20.043326Z","iopub.execute_input":"2023-03-30T01:56:20.043691Z","iopub.status.idle":"2023-03-30T01:56:20.060418Z","shell.execute_reply.started":"2023-03-30T01:56:20.043659Z","shell.execute_reply":"2023-03-30T01:56:20.059102Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}