{"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\nimport os\nfor 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":"2023-06-08T16:11:00.541293Z","iopub.execute_input":"2023-06-08T16:11:00.541656Z","iopub.status.idle":"2023-06-08T16:12:15.997826Z","shell.execute_reply.started":"2023-06-08T16:11:00.541625Z","shell.execute_reply":"2023-06-08T16:12:15.996436Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow_datasets as tfds\n\nfrom keras import layers\nimport keras","metadata":{"execution":{"iopub.status.busy":"2023-06-08T16:18:16.065006Z","iopub.execute_input":"2023-06-08T16:18:16.065413Z","iopub.status.idle":"2023-06-08T16:18:16.071523Z","shell.execute_reply.started":"2023-06-08T16:18:16.065384Z","shell.execute_reply":"2023-06-08T16:18:16.070036Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"IMG_SIZE = 180\n\nresize_and_rescale = keras.Sequential([\n  layers.Resizing(IMG_SIZE, IMG_SIZE),\n  layers.Rescaling(1./255)\n])\n\nresult = resize_and_rescale()\nplt.axis('off')\nplt.imshow(result);","metadata":{"execution":{"iopub.status.busy":"2023-06-08T16:48:23.021021Z","iopub.execute_input":"2023-06-08T16:48:23.021455Z","iopub.status.idle":"2023-06-08T16:48:23.182083Z","shell.execute_reply.started":"2023-06-08T16:48:23.021425Z","shell.execute_reply":"2023-06-08T16:48:23.180157Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"noise = np.random.normal(0.1,(batch_size,latent_dim))\ngen_imgs = generator.predict(noise)\nimgs = get_batch(batch_size)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"d_loss_r = discriminator.train_on_batch(imgs,ones)\nd_loss_f = discriminator.train_on_batch(gen_imgs,zeros)\nd_loss = np.add(d_loss_r, d_loss_f)*0.5\ng_loss = combined.train_on_the_batch(noise,ones)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}