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October 13, 2020 20:50
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Algoritmos e implementações para diversas redes neurais
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| import numpy as np | |
| import gym | |
| from keras.models import Sequential | |
| from keras.layers import Dense, Activation, Flatten | |
| from keras.optimizers import Adam | |
| from rl.agents.dqn import DQNAgent | |
| from rl.policy import EpsGreedyQPolicy | |
| from rl.memory import SequentialMemory | |
| ENV_NAME = 'CartPole-v0' | |
| # Get the environment and extract the number of actions. | |
| env = gym.make(ENV_NAME) | |
| np.random.seed(123) | |
| env.seed(123) | |
| nb_actions = env.action_space.n | |
| # Next, we build a very simple model. | |
| model = Sequential() | |
| model.add(Flatten(input_shape=(1, ) + env.observation_space.shape)) | |
| model.add(Dense(16)) | |
| model.add(Activation('relu')) | |
| model.add(Dense(16)) | |
| model.add(Activation('relu')) | |
| model.add(Dense(16)) | |
| model.add(Activation('relu')) | |
| model.add(Dense(nb_actions)) | |
| model.add(Activation('linear')) | |
| print(model.summary()) | |
| # Finally, we configure and compile our agent. You can use every built-in Keras optimizer and | |
| # even the metrics! | |
| memory = SequentialMemory(limit=50000, window_length=1) | |
| policy = EpsGreedyQPolicy() | |
| dqn = DQNAgent(model=model, | |
| nb_actions=nb_actions, | |
| memory=memory, | |
| nb_steps_warmup=10, | |
| target_model_update=0.01, | |
| policy=policy) | |
| dqn.compile(Adam(lr=1e-3), metrics=['mae']) | |
| dqn.load_weights('dqn_CartPole-v0_weights.h5f') | |
| dqn.test(env, nb_episodes=15, visualize=True, verbose=2) |
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| import gym | |
| import time | |
| import numpy as np | |
| import random | |
| env = gym.make('FrozenLake8x8-v0') # selecionar o ambiente | |
| Q = np.ones( | |
| (env.observation_space.n, env.action_space.n), dtype=np.float32 | |
| ) # construir a tabela Q onde as linhas sao os estados possiveis e as colunas as ações | |
| epsilon = 1 | |
| epsilon_decay = 0.9995 | |
| epsilon_min = 0.01 | |
| alpha = 1 # taxa de aprendizagem inicial | |
| alpha_decay = 0.9995 | |
| alpha_min = 0.01 # taxa de aprendizagem minima | |
| gamma = 0.9 # gamma para recompensas | |
| MAX_NUMBER_STEPS = 10000 | |
| steps = 0 | |
| eta = max(alpha_min, alpha) | |
| eps = max(epsilon_min, epsilon) | |
| total_reward = 0 | |
| state = env.reset() # inicializa o ambiente | |
| while steps < MAX_NUMBER_STEPS: | |
| if random.uniform( | |
| 0, 1) < eps: # se um numero aleatorio for menor que epsilon | |
| action = env.action_space.sample( | |
| ) # escolha uma ação aleatoria (exploration) | |
| else: | |
| action = np.argmax( | |
| Q[state, ] | |
| ) # escolha a ação que vai da o maior retorno para aquele estado (exploitation) | |
| new_state, reward, done, info = env.step( | |
| action) # execute a ação e receba uma nova observação do ambiente | |
| total_reward += reward | |
| if done: | |
| target = reward | |
| else: | |
| target = reward + gamma * np.max(Q[new_state, ]) | |
| Q[state, action] = (1 - eta) * Q[state, action] + eta * ( | |
| target) # atualize a tabela Q com a recompensa recebida pela ação | |
| state = new_state # atualize o estado | |
| if done: | |
| print( | |
| f'steps: {steps} total_reward: {total_reward} alpha: {eta:.4f} epsilon: {eps:.4f} ' | |
| ) | |
| total_reward = 0 | |
| state = env.reset() # inicializa o ambiente | |
| steps += 1 | |
| alpha = alpha * alpha_decay | |
| eta = max(alpha_min, | |
| alpha) # faz o decaimento da taxa de aprendizagem | |
| epsilon = epsilon * epsilon_decay | |
| eps = max(epsilon_min, epsilon) | |
| print("Testando...") | |
| # teste apos treinamento | |
| done = False | |
| state = env.reset() | |
| total_reward = 0 | |
| while not done: | |
| env.render() | |
| action = np.argmax(Q[state, ]) | |
| state, reward, done, info = env.step(action) | |
| print(reward) | |
| time.sleep(1 / 24) |
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| import gym | |
| import time | |
| import numpy as np | |
| import random | |
| env = gym.make('FrozenLake-v0') # selecionar o ambiente | |
| Q = np.ones( | |
| (env.observation_space.n, env.action_space.n), dtype=np.float32 | |
| ) # construir a tabela Q onde as linhas sao os estados possiveis e as colunas as ações | |
| epsilon = 1 | |
| epsilon_decay = 0.99 | |
| epsilon_min = 0.01 | |
| alpha = 1 # taxa de aprendizagem inicial | |
| alpha_decay = 0.999 | |
| alpha_min = 0.01 # taxa de aprendizagem minima | |
| gamma = 0.9999 # gamma para recompensas | |
| MAX_NUMBER_STEPS = 10000 | |
| steps = 0 | |
| eta = max(alpha_min, alpha) | |
| eps = max(epsilon_min, epsilon) | |
| total_reward = 0 | |
| state = env.reset() # inicializa o ambiente | |
| while steps < MAX_NUMBER_STEPS: | |
| if random.uniform( | |
| 0, 1) < eps: # se um numero aleatorio for menor que epsilon | |
| action = env.action_space.sample( | |
| ) # escolha uma ação aleatoria (exploration) | |
| else: | |
| action = np.argmax( | |
| Q[state, ] | |
| ) # escolha a ação que vai da o maior retorno para aquele estado (exploitation) | |
| new_state, reward, done, info = env.step( | |
| action) # execute a ação e receba uma nova observação do ambiente | |
| total_reward += reward | |
| if done: | |
| target = reward | |
| else: | |
| target = reward + gamma * np.max(Q[new_state, ]) | |
| Q[state, action] = (1 - eta) * Q[state, action] + eta * ( | |
| target) # atualize a tabela Q com a recompensa recebida pela ação | |
| state = new_state # atualize o estado | |
| if done: | |
| print( | |
| f'total_reward: {total_reward} steps: {steps} alpha: {eta} epsilon: {eps}' | |
| ) | |
| total_reward = 0 | |
| state = env.reset() # inicializa o ambiente | |
| steps += 1 | |
| alpha = alpha * alpha_decay | |
| eta = max(alpha_min, | |
| alpha) # faz o decaimento da taxa de aprendizagem | |
| epsilon = epsilon * epsilon_decay | |
| eps = max(epsilon_min, epsilon) | |
| print("Testando...") | |
| # teste apos treinamento | |
| done = False | |
| state = env.reset() | |
| total_reward = 0 | |
| while not done: | |
| env.render() | |
| action = np.argmax(Q[state, ]) | |
| state, reward, done, info = env.step(action) | |
| print(reward) | |
| time.sleep(1 / 24) |
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| import numpy as np | |
| import gym | |
| import time | |
| from keras.models import Sequential | |
| from keras.layers import Dense, Activation, Flatten | |
| from keras.optimizers import Adam | |
| from rl.agents.dqn import DQNAgent | |
| from rl.policy import EpsGreedyQPolicy | |
| from rl.memory import SequentialMemory | |
| ENV_NAME = 'MountainCar-v0' | |
| # Get the environment and extract the number of actions. | |
| env = gym.make(ENV_NAME) | |
| np.random.seed(123) | |
| env.seed(123) | |
| nb_actions = env.action_space.n | |
| # Next, we build a very simple model. | |
| model = Sequential() | |
| model.add(Flatten(input_shape=(1, ) + env.observation_space.shape)) | |
| model.add(Dense(16)) | |
| model.add(Activation('relu')) | |
| model.add(Dense(16)) | |
| model.add(Activation('relu')) | |
| model.add(Dense(16)) | |
| model.add(Activation('relu')) | |
| model.add(Dense(nb_actions)) | |
| model.add(Activation('linear')) | |
| print(model.summary()) | |
| # Finally, we configure and compile our agent. You can use every built-in Keras optimizer and | |
| # even the metrics! | |
| memory = SequentialMemory(limit=50000, window_length=1) | |
| policy = EpsGreedyQPolicy() | |
| dqn = DQNAgent(model=model, | |
| nb_actions=nb_actions, | |
| memory=memory, | |
| nb_steps_warmup=10, | |
| target_model_update=0.01, | |
| policy=policy) | |
| dqn.compile(Adam(lr=1e-3), metrics=['mae']) | |
| dqn.load_weights('dqn_MountainCar-v0_weights.h5f') | |
| dqn.test(env, nb_episodes=15, visualize=True) |
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "pokemonDCGAN.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [] | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "accelerator": "GPU" | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "u71-PRBYsIbs", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "import tensorflow as tf\n", | |
| "import IPython.display as display\n", | |
| "import tensorflow.keras as keras\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "import datetime\n", | |
| "import imageio\n", | |
| "import time\n", | |
| "import os\n", | |
| "from tqdm import tqdm\n", | |
| "from google.colab import drive" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "F3IkTKWsuGVR", | |
| "colab_type": "code", | |
| "outputId": "061ec980-7328-4218-dd10-4a4ef6be746c", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| } | |
| }, | |
| "source": [ | |
| "tf.config.list_physical_devices('GPU')" | |
| ], | |
| "execution_count": 2, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 2 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "SwKBSG7QZUGj", | |
| "colab_type": "code", | |
| "outputId": "2c9daef9-cbf5-4c1b-f9d5-c91ed849b4df", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 306 | |
| } | |
| }, | |
| "source": [ | |
| "!nvidia-smi" | |
| ], | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Sun May 17 13:59:51 2020 \n", | |
| "+-----------------------------------------------------------------------------+\n", | |
| "| NVIDIA-SMI 440.82 Driver Version: 418.67 CUDA Version: 10.1 |\n", | |
| "|-------------------------------+----------------------+----------------------+\n", | |
| "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", | |
| "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", | |
| "|===============================+======================+======================|\n", | |
| "| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |\n", | |
| "| N/A 35C P8 28W / 149W | 11MiB / 11441MiB | 0% Default |\n", | |
| "+-------------------------------+----------------------+----------------------+\n", | |
| " \n", | |
| "+-----------------------------------------------------------------------------+\n", | |
| "| Processes: GPU Memory |\n", | |
| "| GPU PID Type Process name Usage |\n", | |
| "|=============================================================================|\n", | |
| "| No running processes found |\n", | |
| "+-----------------------------------------------------------------------------+\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "xZrti9KFshPj", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 122 | |
| }, | |
| "outputId": "d81e2068-7621-4676-aeed-113165de80dc" | |
| }, | |
| "source": [ | |
| "if not os.path.exists('/content/pokemon/'):\n", | |
| " drive.mount('/content/drive')\n", | |
| " %cp '/content/drive/My Drive/pokemon.zip' .\n", | |
| " !unzip pokemon.zip > /dev/null\n", | |
| " !rm pokemon.zip" | |
| ], | |
| "execution_count": 4, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n", | |
| "\n", | |
| "Enter your authorization code:\n", | |
| "··········\n", | |
| "Mounted at /content/drive\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "g1SSIwAZs34M", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "images_path = \"/content/pokemon/pokemon\"" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "3K7p2uO_SYlE", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "# set seed\n", | |
| "tf.random.set_seed(1)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "J2hASuqa4u8q", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "!rm -rf /content/images\n", | |
| "!mkdir /content/images" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "xE-kHRtBSoSe", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "# Load the TensorBoard notebook extension\n", | |
| "%load_ext tensorboard" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "1CWrYX-3CgsU", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "# clear keras backend\n", | |
| "tf.keras.backend.clear_session()\n", | |
| "tf.compat.v1.reset_default_graph()" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "87Z3wX0uSuw8", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "!rm -rf logs/gradient_tape\n", | |
| "current_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n", | |
| "train_log_dir = 'logs/gradient_tape/' + current_time + '/train'\n", | |
| "train_summary_writer = tf.summary.create_file_writer(train_log_dir)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "5zVsqvFyxLQQ", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "len_images = len(os.listdir(images_path))\n", | |
| "BATCH_SIZE = 128\n", | |
| "REPEAT = 10\n", | |
| "number_channels = 3\n", | |
| "SHUFFLE_SIZE = REPEAT*len_images\n", | |
| "input_shape = (64,64,number_channels)\n", | |
| "noise_dim = 100\n", | |
| "learning_rate = 0.0002\n", | |
| "beta1 = 0.5\n", | |
| "beta2 = 0.999\n", | |
| "EPOCHS = 150\n", | |
| "features_map = 64\n", | |
| "checkpoint_folder = 'pokemon_ckpts_1'\n" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "MHfpwto59l-0", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "seed = tf.random.normal([16,noise_dim])" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "5MGswx2YuIXP", | |
| "colab_type": "code", | |
| "outputId": "c9f8f813-98ca-4206-9d71-dc567b8b27b6", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| } | |
| }, | |
| "source": [ | |
| "\n", | |
| "@tf.function\n", | |
| "def proccess_image(file_path):\n", | |
| " image = tf.io.read_file(file_path)\n", | |
| " image = tf.image.decode_png(image,channels=number_channels)\n", | |
| " return image\n", | |
| "\n", | |
| "@tf.function\n", | |
| "def data_augmentation(image):\n", | |
| " image = tf.image.convert_image_dtype(image,tf.float32)\n", | |
| " image = tf.image.resize(image,size=[64,64])\n", | |
| " image = tf.image.resize_with_crop_or_pad(image, 70, 70)\n", | |
| " image = tf.image.random_crop(image, size=[64, 64, 3])\n", | |
| " image = tf.image.random_flip_left_right(image)\n", | |
| " image = tf.image.random_brightness(image,max_delta=0.2)\n", | |
| " image = tf.image.random_contrast(image,lower=0.75,upper=2)\n", | |
| " image = tf.image.random_saturation(image,lower=0.75,upper=1.5)\n", | |
| " image = tf.image.random_hue(image,max_delta=0.1)\n", | |
| " image = (image - tf.reduce_min(image))/(tf.reduce_max(image) - tf.reduce_min(image))\n", | |
| " image = 2*image - 1\n", | |
| " return image\n", | |
| "\n", | |
| "\n", | |
| "dataset = tf.data.Dataset.list_files(images_path+\"/*\")\n", | |
| "dataset = (dataset\n", | |
| " .map(proccess_image,num_parallel_calls=tf.data.experimental.AUTOTUNE)\n", | |
| " .repeat(REPEAT)\n", | |
| " .map(data_augmentation,num_parallel_calls=tf.data.experimental.AUTOTUNE)\n", | |
| " .cache('/content/cache')\n", | |
| " .shuffle(SHUFFLE_SIZE)\n", | |
| " .batch(BATCH_SIZE)\n", | |
| " .prefetch(tf.data.experimental.AUTOTUNE)\n", | |
| " )\n", | |
| "\n", | |
| "dataset.element_spec" | |
| ], | |
| "execution_count": 13, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "TensorSpec(shape=(None, 64, 64, 3), dtype=tf.float32, name=None)" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 13 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "eC8iFJHVuxvJ", | |
| "colab_type": "code", | |
| "outputId": "e0a8b7a3-e8ac-426d-89b5-4a132e678651", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 337 | |
| } | |
| }, | |
| "source": [ | |
| "print(\"Dataset example\")\n", | |
| "for batch in dataset.take(1):\n", | |
| " plt.figure(figsize=(10,5))\n", | |
| " for index,image in enumerate(batch[0:32,:]):\n", | |
| " image = image * 0.5 + 0.5\n", | |
| " plt.subplot(4,8,index+1)\n", | |
| " plt.imshow(image)\n", | |
| " plt.axis('off')\n", | |
| " print(image.shape,image.numpy().min(),image.numpy().max())" | |
| ], | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Dataset example\n", | |
| "(64, 64, 3) 0.0 1.0\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 720x360 with 32 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [], | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "_tHnbSCO-Y7e", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "def plot_images_per_epoch_and_save(generator_model,discriminator_model,seed,epoch):\n", | |
| " images = generator_model(seed,training=False)\n", | |
| " pred = discriminator_model(images,training=False)\n", | |
| " generator_loss = calculate_generator_loss(pred)\n", | |
| " fig = plt.figure(1,(6,6))\n", | |
| " for i in range(16):\n", | |
| " plt.subplot(4,4,i+1)\n", | |
| " plt.imshow(images[i,:,:,:]*0.5+0.5)\n", | |
| " plt.axis('off')\n", | |
| " plt.savefig(f'/content/images/epoch-{epoch:04d}.png')\n", | |
| " plt.show()\n", | |
| " print(f\"generator_loss(seed): {generator_loss:.2f}\")\n" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "9V3bg33eUySG", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "def Conv2DTranspose(**kwargs):\n", | |
| " return keras.layers.Conv2DTranspose(\n", | |
| " kernel_initializer=tf.random_normal_initializer(mean=0.0,stddev=0.02),\n", | |
| " use_bias=False,\n", | |
| " **kwargs\n", | |
| " )\n", | |
| "\n", | |
| "def Conv2D(**kwargs):\n", | |
| " return keras.layers.Conv2D(\n", | |
| " kernel_initializer=tf.random_normal_initializer(mean=0.0,stddev=0.02),\n", | |
| " use_bias=False,\n", | |
| " **kwargs\n", | |
| " )\n", | |
| " \n", | |
| "def BatchNormalization(**kwargs):\n", | |
| " return keras.layers.BatchNormalization(\n", | |
| " # moving_mean_initializer = tf.random_normal_initializer(mean=1.0,stddev=0.02),\n", | |
| " # moving_variance_initializer = tf.random_normal_initializer(mean=1.0,stddev=0.02),\n", | |
| " **kwargs\n", | |
| " )" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "QW4nDf6O4Dq2", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "def make_generator_model():\n", | |
| " model = keras.Sequential([\n", | |
| " keras.layers.Reshape((2,2,25),input_shape=(100,)),\n", | |
| " \n", | |
| " Conv2DTranspose(filters=features_map*16,kernel_size=5,strides=2,padding='same'),\n", | |
| " BatchNormalization(),\n", | |
| " keras.layers.ReLU(),\n", | |
| "\n", | |
| " Conv2DTranspose(filters=features_map*8,kernel_size=5,strides=2,padding='same'),\n", | |
| " BatchNormalization(),\n", | |
| " keras.layers.ReLU(),\n", | |
| "\n", | |
| " Conv2DTranspose(filters=features_map*4,kernel_size=5,strides=2,padding='same'),\n", | |
| " BatchNormalization(),\n", | |
| " keras.layers.ReLU(),\n", | |
| "\n", | |
| " Conv2DTranspose(filters=features_map*2,kernel_size=5,strides=2,padding='same'),\n", | |
| " BatchNormalization(),\n", | |
| " keras.layers.ReLU(),\n", | |
| " \n", | |
| " Conv2DTranspose(\n", | |
| " filters=number_channels,\n", | |
| " kernel_size=5,\n", | |
| " strides=2,\n", | |
| " padding='same',\n", | |
| " activation='tanh'\n", | |
| " ) \n", | |
| " ])\n", | |
| " return model" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "PDwcg3cn7RHQ", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "def make_discriminator_model():\n", | |
| " model = keras.Sequential([\n", | |
| " Conv2D(filters=features_map*2,kernel_size=(5,5),strides=2,padding='same',input_shape=input_shape, name='Conv2D_1'),\n", | |
| " keras.layers.LeakyReLU(0.2, name='LeakyRelu_1'),\n", | |
| " \n", | |
| " Conv2D(filters=features_map*4,kernel_size=(5,5),strides=2,padding='same', name='Conv2D_2'),\n", | |
| " BatchNormalization(name='BatchNorm_2'),\n", | |
| " keras.layers.LeakyReLU(0.2, name='LeakyRelu_2'),\n", | |
| " \n", | |
| " Conv2D(filters=features_map*8,kernel_size=(5,5),strides=2,padding='same', name='Conv2D_3'),\n", | |
| " BatchNormalization(name='BatchNorm_3'),\n", | |
| " keras.layers.LeakyReLU(0.2, name='LeakyRelu_3'),\n", | |
| "\n", | |
| " Conv2D(filters=features_map*16,kernel_size=(5,5),strides=2,padding='same', name='Conv2D_4'),\n", | |
| " BatchNormalization(name='BatchNorm_4'),\n", | |
| " keras.layers.LeakyReLU(0.2, name='LeakyRelu_4'),\n", | |
| " \n", | |
| " keras.layers.Flatten(),\n", | |
| " keras.layers.Dense(1, name='Output') \n", | |
| " ])\n", | |
| " return model\n" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "dfKz-4-S9uEA", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "def calculate_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", | |
| " return real_loss + fake_loss" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "905Y0emS9w9G", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "def calculate_generator_loss(fake_output):\n", | |
| " return cross_entropy(tf.ones_like(fake_output),fake_output)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "gOMlflkl9biV", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "generator_optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate,beta_1=beta1)\n", | |
| "discriminator_optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate,beta_1=beta1)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "cbFYwVSJ9qiA", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "IM7eHpYn92XG", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "generator = make_generator_model()\n", | |
| "discriminator = make_discriminator_model()" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "kl9glXO1bqDQ", | |
| "colab_type": "code", | |
| "outputId": "d75d9563-a947-4812-8090-89beafa0a426", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 629 | |
| } | |
| }, | |
| "source": [ | |
| "generator.summary()" | |
| ], | |
| "execution_count": 24, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Model: \"sequential\"\n", | |
| "_________________________________________________________________\n", | |
| "Layer (type) Output Shape Param # \n", | |
| "=================================================================\n", | |
| "reshape (Reshape) (None, 2, 2, 25) 0 \n", | |
| "_________________________________________________________________\n", | |
| "conv2d_transpose (Conv2DTran (None, 4, 4, 1024) 640000 \n", | |
| "_________________________________________________________________\n", | |
| "batch_normalization (BatchNo (None, 4, 4, 1024) 4096 \n", | |
| "_________________________________________________________________\n", | |
| "re_lu (ReLU) (None, 4, 4, 1024) 0 \n", | |
| "_________________________________________________________________\n", | |
| "conv2d_transpose_1 (Conv2DTr (None, 8, 8, 512) 13107200 \n", | |
| "_________________________________________________________________\n", | |
| "batch_normalization_1 (Batch (None, 8, 8, 512) 2048 \n", | |
| "_________________________________________________________________\n", | |
| "re_lu_1 (ReLU) (None, 8, 8, 512) 0 \n", | |
| "_________________________________________________________________\n", | |
| "conv2d_transpose_2 (Conv2DTr (None, 16, 16, 256) 3276800 \n", | |
| "_________________________________________________________________\n", | |
| "batch_normalization_2 (Batch (None, 16, 16, 256) 1024 \n", | |
| "_________________________________________________________________\n", | |
| "re_lu_2 (ReLU) (None, 16, 16, 256) 0 \n", | |
| "_________________________________________________________________\n", | |
| "conv2d_transpose_3 (Conv2DTr (None, 32, 32, 128) 819200 \n", | |
| "_________________________________________________________________\n", | |
| "batch_normalization_3 (Batch (None, 32, 32, 128) 512 \n", | |
| "_________________________________________________________________\n", | |
| "re_lu_3 (ReLU) (None, 32, 32, 128) 0 \n", | |
| "_________________________________________________________________\n", | |
| "conv2d_transpose_4 (Conv2DTr (None, 64, 64, 3) 9600 \n", | |
| "=================================================================\n", | |
| "Total params: 17,860,480\n", | |
| "Trainable params: 17,856,640\n", | |
| "Non-trainable params: 3,840\n", | |
| "_________________________________________________________________\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ili_YMNzbxlx", | |
| "colab_type": "code", | |
| "outputId": "520b1410-9a9d-4bce-e1d2-20f08fef9a39", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 595 | |
| } | |
| }, | |
| "source": [ | |
| "discriminator.summary()" | |
| ], | |
| "execution_count": 25, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Model: \"sequential_1\"\n", | |
| "_________________________________________________________________\n", | |
| "Layer (type) Output Shape Param # \n", | |
| "=================================================================\n", | |
| "Conv2D_1 (Conv2D) (None, 32, 32, 128) 9600 \n", | |
| "_________________________________________________________________\n", | |
| "LeakyRelu_1 (LeakyReLU) (None, 32, 32, 128) 0 \n", | |
| "_________________________________________________________________\n", | |
| "Conv2D_2 (Conv2D) (None, 16, 16, 256) 819200 \n", | |
| "_________________________________________________________________\n", | |
| "BatchNorm_2 (BatchNormalizat (None, 16, 16, 256) 1024 \n", | |
| "_________________________________________________________________\n", | |
| "LeakyRelu_2 (LeakyReLU) (None, 16, 16, 256) 0 \n", | |
| "_________________________________________________________________\n", | |
| "Conv2D_3 (Conv2D) (None, 8, 8, 512) 3276800 \n", | |
| "_________________________________________________________________\n", | |
| "BatchNorm_3 (BatchNormalizat (None, 8, 8, 512) 2048 \n", | |
| "_________________________________________________________________\n", | |
| "LeakyRelu_3 (LeakyReLU) (None, 8, 8, 512) 0 \n", | |
| "_________________________________________________________________\n", | |
| "Conv2D_4 (Conv2D) (None, 4, 4, 1024) 13107200 \n", | |
| "_________________________________________________________________\n", | |
| "BatchNorm_4 (BatchNormalizat (None, 4, 4, 1024) 4096 \n", | |
| "_________________________________________________________________\n", | |
| "LeakyRelu_4 (LeakyReLU) (None, 4, 4, 1024) 0 \n", | |
| "_________________________________________________________________\n", | |
| "flatten (Flatten) (None, 16384) 0 \n", | |
| "_________________________________________________________________\n", | |
| "Output (Dense) (None, 1) 16385 \n", | |
| "=================================================================\n", | |
| "Total params: 17,236,353\n", | |
| "Trainable params: 17,232,769\n", | |
| "Non-trainable params: 3,584\n", | |
| "_________________________________________________________________\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "sutm_kkU98fK", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "@tf.function\n", | |
| "def train_step(real_images_batch,epoch):\n", | |
| " with tf.GradientTape() as D_tape, tf.GradientTape() as G_tape:\n", | |
| " noise = tf.random.normal([BATCH_SIZE,noise_dim])\n", | |
| " fake_images_batch = generator(noise,training=True)\n", | |
| " \n", | |
| " fake_output = discriminator(fake_images_batch,training=True)\n", | |
| " real_output = discriminator(real_images_batch,training=True)\n", | |
| " \n", | |
| " discriminator_loss = calculate_discriminator_loss(real_output,fake_output)\n", | |
| " generator_loss = calculate_generator_loss(fake_output)\n", | |
| " \n", | |
| " discriminator_grads = D_tape.gradient(discriminator_loss,discriminator.trainable_variables)\n", | |
| " generator_grads = G_tape.gradient(generator_loss,generator.trainable_variables)\n", | |
| "\n", | |
| " generator_optimizer.apply_gradients(zip(generator_grads,generator.trainable_variables))\n", | |
| " discriminator_optimizer.apply_gradients(zip(discriminator_grads,discriminator.trainable_variables))\n", | |
| "\n", | |
| " with train_summary_writer.as_default():\n", | |
| " tf.summary.scalar('generator_loss',generator_loss,step=epoch)\n", | |
| " tf.summary.scalar('discriminator_loss',discriminator_loss,step=epoch)\n" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "gHhJtn9JJFxV", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "# create checkpoint\n", | |
| "checkpoint = tf.train.Checkpoint(\n", | |
| " generator=generator,\n", | |
| " discriminator=discriminator,\n", | |
| " generator_optimizer=generator_optimizer,\n", | |
| " discriminator_optimizer=discriminator_optimizer,\n", | |
| " step=tf.Variable(1)\n", | |
| ")\n", | |
| "manager = tf.train.CheckpointManager(checkpoint, f'/content/checkpoints/{checkpoint_folder}/', max_to_keep=2)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "THuB0TCFJH7H", | |
| "colab_type": "code", | |
| "outputId": "105cd167-a28f-4d6a-a1a2-575151e522af", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| } | |
| }, | |
| "source": [ | |
| "checkpoint.restore(manager.latest_checkpoint)\n", | |
| "if manager.latest_checkpoint:\n", | |
| " print(\"Restored from {}\".format(manager.latest_checkpoint))\n", | |
| "else:\n", | |
| " print(\"Initializing from scratch.\")" | |
| ], | |
| "execution_count": 28, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Initializing from scratch.\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "AzzAuUTuTM6s", | |
| "colab_type": "code", | |
| "outputId": "cf33b1a6-d3a8-49d8-887c-92ce680caa20", | |
| "colab": { | |
| "resources": { | |
| "https://localhost:6006/": { |
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