{"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":"markdown","source":"# Transfer Learning, GPUs e TPUs\nEsse notebook foi criado com base no tutorial de Ryan Holbrook, disponível no próprio Kaggle: https://www.kaggle.com/ryanholbrook/create-your-first-submission\n\nA competição de Flower Classification on TPU foi criada para ensinar os usuários a utilizarem os recursos de aceleradores do Kaggle, pois treinar uma rede neural no dataset dessa competição usando só a CPU seria extremamente demorado.","metadata":{}},{"cell_type":"markdown","source":"## 0 - Importações","metadata":{}},{"cell_type":"code","source":"# Usados na importação e tratamento dos dados\nimport math, re, os\nimport numpy as np\n\n# Biblioteca de redes neurais\nimport tensorflow as tf\n\n# Necessário para rodar em TPU\nfrom kaggle_datasets import KaggleDatasets","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-10-27T23:48:23.989140Z","iopub.execute_input":"2021-10-27T23:48:23.989901Z","iopub.status.idle":"2021-10-27T23:48:28.883947Z","shell.execute_reply.started":"2021-10-27T23:48:23.989782Z","shell.execute_reply":"2021-10-27T23:48:28.883282Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Existem 3 modos de executar seus códigos no Kaggle:\n* **CPU** (sem acelerador): Processador comum, é suficiente para algumas aplicações, mas quase nunca dá conta para redes neurais grandes, que usam muitos dados no treinamento.\n* **GPU**: Unidade de processamento especializada em processar blocos de dados em paralelo. Torna o treinamento das redes muito mais rápido, pois as bibliotecas tomam proveito dessa propriedade para executar multiplas operações simultâneamente (Vectorization).\n* **TPU**: Hardware especializado (ASIC) para executar código do TensorFlow. Por ser especializado tende a ter a melhor performance, mas também tem várias particularidades.\n\nEntão, **como eu uso esses aceleradores?**\n\nNo caso do seu próprio PC o tensorflow a CPU já funciona sem mudar nada, mas se você quiser usar uma GPU você deve seguir a documentação para garantir que a biblioteca consiga utilizar esse recurso:\nhttps://www.tensorflow.org/install/gpu\n\nObs: Por padrão o tensorflow só funciona com placas de vídeo da Nvidia (inclusive pode funcionar só de ter os drivers instalados). Para placas da AMD existem meios alternativos, mas nenhum oficial do TensorFlow.\n\nNo Kaggle o procedimento é mais simples: é só clicar nos três pontos do canto superior direto, depois em \"Accelerator\" e escolher um acelerador. \n\n**Mas por quê esses recursos aceleram o treinamento?**\n\n![diagrama_matrizes.png](attachment:ae5fca89-a8e3-4273-8e69-d88eac53eada.png)","metadata":{},"attachments":{"ae5fca89-a8e3-4273-8e69-d88eac53eada.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"## 1 - Configurações iniciais e importação dos dados\nPrimeiramente, como queremos ter a possibilidade de rodar o código em uma TPU, precisamos definir uma estratégia de distribuição. Isso é necessário, pois rodar algo em TPU é como rodar em várias GPUs ou CPUs, e essa definição distribui o treinamento nos vários núcleos da TPU.","metadata":{}},{"cell_type":"code","source":"# Tenta se conectar em uma TPU\ntry:\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver() \n    print('Running on TPU ', tpu.master())\nexcept ValueError:\n    tpu = None\n\n# Se consegui configura para rodar na TPU\nif tpu:\n    tf.config.experimental_connect_to_cluster(tpu)\n    tf.tpu.experimental.initialize_tpu_system(tpu)\n    strategy = tf.distribute.experimental.TPUStrategy(tpu)\n    \n# Senão define uma estratégia que funciona para CPU ou GPU\nelse:\n    strategy = tf.distribute.get_strategy() \n\nprint(\"REPLICAS: \", strategy.num_replicas_in_sync)","metadata":{"execution":{"iopub.status.busy":"2021-10-27T23:48:28.885762Z","iopub.execute_input":"2021-10-27T23:48:28.886217Z","iopub.status.idle":"2021-10-27T23:48:35.187166Z","shell.execute_reply.started":"2021-10-27T23:48:28.886177Z","shell.execute_reply":"2021-10-27T23:48:35.186191Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Para executar o código em uma TPU também é necessário usar um dataset armazenado em um Google Cloud Storage Bucket. No caso de dados disponíveis no Kaggle, é possível conseguir o path adequado usando a biblioteca kaggle_datasets.","metadata":{}},{"cell_type":"code","source":"# Adquire o path do Google Cloud Storage para o dataset dessa competição \nGCS_DS_PATH = KaggleDatasets().get_gcs_path('tpu-getting-started')\nprint(GCS_DS_PATH)","metadata":{"execution":{"iopub.status.busy":"2021-10-27T23:48:35.188480Z","iopub.execute_input":"2021-10-27T23:48:35.188792Z","iopub.status.idle":"2021-10-27T23:48:35.639672Z","shell.execute_reply.started":"2021-10-27T23:48:35.188761Z","shell.execute_reply":"2021-10-27T23:48:35.638818Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Em seguida, é preciso importar os dados e transformar as imagens para elas ficarem em um formato adequado para serem utilizadas pela rede neural. Nesse notebook o código para isso é o mesmo apresentado por Ryan Holbrook em: https://www.kaggle.com/ryanholbrook/create-your-first-submission.\n\nRecomendo muito ler esse tutorial para ter mais detalhes essa etapa.","metadata":{}},{"cell_type":"code","source":"IMAGE_SIZE = [192, 192]\nGCS_PATH = GCS_DS_PATH + '/tfrecords-jpeg-192x192'\nAUTO = tf.data.experimental.AUTOTUNE\n\nTRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec')\nVALIDATION_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')\nTEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec') \n\nCLASSES = ['pink primrose',    'hard-leaved pocket orchid', 'canterbury bells', 'sweet pea',     'wild geranium',     'tiger lily',           'moon orchid',              'bird of paradise', 'monkshood',        'globe thistle',         # 00 - 09\n           'snapdragon',       \"colt's foot\",               'king protea',      'spear thistle', 'yellow iris',       'globe-flower',         'purple coneflower',        'peruvian lily',    'balloon flower',   'giant white arum lily', # 10 - 19\n           'fire lily',        'pincushion flower',         'fritillary',       'red ginger',    'grape hyacinth',    'corn poppy',           'prince of wales feathers', 'stemless gentian', 'artichoke',        'sweet william',         # 20 - 29\n           'carnation',        'garden phlox',              'love in the mist', 'cosmos',        'alpine sea holly',  'ruby-lipped cattleya', 'cape flower',              'great masterwort', 'siam tulip',       'lenten rose',           # 30 - 39\n           'barberton daisy',  'daffodil',                  'sword lily',       'poinsettia',    'bolero deep blue',  'wallflower',           'marigold',                 'buttercup',        'daisy',            'common dandelion',      # 40 - 49\n           'petunia',          'wild pansy',                'primula',          'sunflower',     'lilac hibiscus',    'bishop of llandaff',   'gaura',                    'geranium',         'orange dahlia',    'pink-yellow dahlia',    # 50 - 59\n           'cautleya spicata', 'japanese anemone',          'black-eyed susan', 'silverbush',    'californian poppy', 'osteospermum',         'spring crocus',            'iris',             'windflower',       'tree poppy',            # 60 - 69\n           'gazania',          'azalea',                    'water lily',       'rose',          'thorn apple',       'morning glory',        'passion flower',           'lotus',            'toad lily',        'anthurium',             # 70 - 79\n           'frangipani',       'clematis',                  'hibiscus',         'columbine',     'desert-rose',       'tree mallow',          'magnolia',                 'cyclamen ',        'watercress',       'canna lily',            # 80 - 89\n           'hippeastrum ',     'bee balm',                  'pink quill',       'foxglove',      'bougainvillea',     'camellia',             'mallow',                   'mexican petunia',  'bromelia',         'blanket flower',        # 90 - 99\n           'trumpet creeper',  'blackberry lily',           'common tulip',     'wild rose']                                                                                                                                               # 100 - 102\n\n\ndef decode_image(image_data):\n    image = tf.image.decode_jpeg(image_data, channels=3)\n    image = tf.cast(image, tf.float32) / 255.0  # convert image to floats in [0, 1] range\n    image = tf.reshape(image, [*IMAGE_SIZE, 3]) # explicit size needed for TPU\n    return image\n\ndef read_labeled_tfrecord(example):\n    LABELED_TFREC_FORMAT = {\n        \"image\": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring\n        \"class\": tf.io.FixedLenFeature([], tf.int64),  # shape [] means single element\n    }\n    example = tf.io.parse_single_example(example, LABELED_TFREC_FORMAT)\n    image = decode_image(example['image'])\n    label = tf.cast(example['class'], tf.int32)\n    return image, label # returns a dataset of (image, label) pairs\n\ndef read_unlabeled_tfrecord(example):\n    UNLABELED_TFREC_FORMAT = {\n        \"image\": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring\n        \"id\": tf.io.FixedLenFeature([], tf.string),  # shape [] means single element\n        # class is missing, this competitions's challenge is to predict flower classes for the test dataset\n    }\n    example = tf.io.parse_single_example(example, UNLABELED_TFREC_FORMAT)\n    image = decode_image(example['image'])\n    idnum = example['id']\n    return image, idnum # returns a dataset of image(s)\n\ndef load_dataset(filenames, labeled=True, ordered=False):\n    # Read from TFRecords. For optimal performance, reading from multiple files at once and\n    # disregarding data order. Order does not matter since we will be shuffling the data anyway.\n\n    ignore_order = tf.data.Options()\n    if not ordered:\n        ignore_order.experimental_deterministic = False # disable order, increase speed\n\n    dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO) # automatically interleaves reads from multiple files\n    dataset = dataset.with_options(ignore_order) # uses data as soon as it streams in, rather than in its original order\n    dataset = dataset.map(read_labeled_tfrecord if labeled else read_unlabeled_tfrecord, num_parallel_calls=AUTO)\n    # returns a dataset of (image, label) pairs if labeled=True or (image, id) pairs if labeled=False\n    return dataset","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-10-27T23:48:35.643553Z","iopub.execute_input":"2021-10-27T23:48:35.643819Z","iopub.status.idle":"2021-10-27T23:48:35.979536Z","shell.execute_reply.started":"2021-10-27T23:48:35.643789Z","shell.execute_reply":"2021-10-27T23:48:35.978689Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def data_augment(image, label):\n    # Thanks to the dataset.prefetch(AUTO)\n    # statement in the next function (below), this happens essentially\n    # for free on TPU. Data pipeline code is executed on the \"CPU\"\n    # part of the TPU while the TPU itself is computing gradients.\n    image = tf.image.random_flip_left_right(image)\n    #image = tf.image.random_saturation(image, 0, 2)\n    return image, label   \n\ndef get_training_dataset():\n    dataset = load_dataset(TRAINING_FILENAMES, labeled=True)\n    dataset = dataset.map(data_augment, num_parallel_calls=AUTO)\n    dataset = dataset.repeat() # the training dataset must repeat for several epochs\n    dataset = dataset.shuffle(2048)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)\n    return dataset\n\ndef get_validation_dataset(ordered=False):\n    dataset = load_dataset(VALIDATION_FILENAMES, labeled=True, ordered=ordered)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.cache()\n    dataset = dataset.prefetch(AUTO)\n    return dataset\n\ndef get_test_dataset(ordered=False):\n    dataset = load_dataset(TEST_FILENAMES, labeled=False, ordered=ordered)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.prefetch(AUTO)\n    return dataset\n\ndef count_data_items(filenames):\n    # the number of data items is written in the name of the .tfrec\n    # files, i.e. flowers00-230.tfrec = 230 data items\n    n = [int(re.compile(r\"-([0-9]*)\\.\").search(filename).group(1)) for filename in filenames]\n    return np.sum(n)\n\nNUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES)\nNUM_VALIDATION_IMAGES = count_data_items(VALIDATION_FILENAMES)\nNUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)\nprint('Dataset: {} training images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-10-27T23:48:35.981034Z","iopub.execute_input":"2021-10-27T23:48:35.981371Z","iopub.status.idle":"2021-10-27T23:48:35.995152Z","shell.execute_reply.started":"2021-10-27T23:48:35.981344Z","shell.execute_reply":"2021-10-27T23:48:35.994213Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Define o tamanho do batch, baseando no número de núcleos de TPU disponíveis\nBATCH_SIZE = 16 * strategy.num_replicas_in_sync\n\n# Gera os datasets separados em treino, validação e teste\nds_train = get_training_dataset()\nds_valid = get_validation_dataset()\nds_test = get_test_dataset()","metadata":{"execution":{"iopub.status.busy":"2021-10-27T23:48:35.997857Z","iopub.execute_input":"2021-10-27T23:48:35.998349Z","iopub.status.idle":"2021-10-27T23:48:36.332411Z","shell.execute_reply.started":"2021-10-27T23:48:35.998303Z","shell.execute_reply":"2021-10-27T23:48:36.331572Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 2 - Definindo e treinando o modelo\nEm geral é difícil decidir uma arquitetura de rede neural do zero, por isso, em muitos casos compensa adotar uma arquitetura já existente para o problema que queremos resolver. Essas redes podem ser encontradas pesquisando artigos recentes sobre alguma área específica, por exemplo, classificação de imagens que é o caso desse dataset.\n\n#### Transfer Learning\nPodemos usar pedaço de um modelo já treinado em algum dataset para começar na frente em algum dataset novo.\nIsso é feito reutilizando os parâmetros treinados como inicialização para a rede invés de iniciar os pesos e viéses do zero. Também é comum \"congelar\" camadas para que não sejam treinadas. Isso também ajuda a acelerar o treinamento.\n\nEsse procedimento funciona pois as features aprendidas pela rede são reutilizadas ao usar os pesos de uma rede já treinada em um problema parecido. É comum achar redes de artigos já treinadas em datasets como o ImageNet para o problema de classificação de imagens, inclusive várias estão implementadas no próprio TensorFlow.\n\nAlgumas redes implementadas no Keras podem ser encontradas em tf.keras.applications: https://www.tensorflow.org/api_docs/python/tf/keras/applications","metadata":{}},{"cell_type":"code","source":"with strategy.scope():\n    # Importando o modelo já treinado\n    model = tf.keras.applications.xception.Xception(\n        weights='imagenet',\n        # Exclui a última camada, vai ser trocada para ser específica para o problema \n        include_top=False ,\n        # Define o tamanho do dado de entrada\n        input_shape=[*IMAGE_SIZE, 3]\n    )\n    \n    # \"Congela\" os pesos da rede, assim só a última camada nova vai ser treinada\n    model.trainable = False\n    \n    model = tf.keras.Sequential([\n        # O modelo base está treinado com ImageNet\n        model,\n        # O final da rede é alterado para se adequar ao problema\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(len(CLASSES), activation='softmax')\n    ])","metadata":{"execution":{"iopub.status.busy":"2021-10-27T23:48:36.333649Z","iopub.execute_input":"2021-10-27T23:48:36.333896Z","iopub.status.idle":"2021-10-27T23:48:46.168388Z","shell.execute_reply.started":"2021-10-27T23:48:36.333842Z","shell.execute_reply":"2021-10-27T23:48:46.167494Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Definindo o otimizador, função perda e métricas\nmodel.compile(\n    optimizer='adam',\n    loss = 'sparse_categorical_crossentropy',\n    metrics=['sparse_categorical_accuracy'],\n)\n\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2021-10-27T23:48:46.170173Z","iopub.execute_input":"2021-10-27T23:48:46.170780Z","iopub.status.idle":"2021-10-27T23:48:46.231767Z","shell.execute_reply.started":"2021-10-27T23:48:46.170736Z","shell.execute_reply":"2021-10-27T23:48:46.229943Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"EPOCHS = 10\nSTEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE\n\nhistory = model.fit(\n    ds_train,\n    validation_data=ds_valid,\n    epochs=EPOCHS,\n    steps_per_epoch=STEPS_PER_EPOCH,\n)","metadata":{"execution":{"iopub.status.busy":"2021-10-27T23:48:46.233303Z","iopub.execute_input":"2021-10-27T23:48:46.233651Z","iopub.status.idle":"2021-10-27T23:49:57.865523Z","shell.execute_reply.started":"2021-10-27T23:48:46.233615Z","shell.execute_reply":"2021-10-27T23:49:57.864677Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Comparação de tempo para cada acelerador:\n* **CPU**: Aproximadamente 13 minutos por época\n* **GPU**: 33-34 segundos por época.\n* **TPU**: 5-6 segundos por época.\n\nSugiro rodar esse notebook mudando o acelerador do Kaggle para observar a diferença no tempo de treinamento. ","metadata":{}},{"cell_type":"markdown","source":"#### Sem transfer Learning:","metadata":{"execution":{"iopub.status.busy":"2021-10-27T22:05:00.64523Z","iopub.execute_input":"2021-10-27T22:05:00.645603Z","iopub.status.idle":"2021-10-27T22:05:00.649968Z","shell.execute_reply.started":"2021-10-27T22:05:00.645567Z","shell.execute_reply":"2021-10-27T22:05:00.649188Z"}}},{"cell_type":"code","source":"with strategy.scope():\n    model = tf.keras.applications.xception.Xception(\n        include_top=False ,\n        input_shape=[*IMAGE_SIZE, 3]\n    )\n    \n    \n    model = tf.keras.Sequential([\n        model,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(len(CLASSES), activation='softmax')\n    ])\nmodel.compile(\n    optimizer='adam',\n    loss = 'sparse_categorical_crossentropy',\n    metrics=['sparse_categorical_accuracy'],\n)\nhistory = model.fit(\n    ds_train,\n    validation_data=ds_valid,\n    epochs=EPOCHS,\n    steps_per_epoch=STEPS_PER_EPOCH,\n)","metadata":{"execution":{"iopub.status.busy":"2021-10-27T23:49:57.867577Z","iopub.execute_input":"2021-10-27T23:49:57.867803Z","iopub.status.idle":"2021-10-27T23:52:12.699861Z","shell.execute_reply.started":"2021-10-27T23:49:57.867778Z","shell.execute_reply":"2021-10-27T23:52:12.698868Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"É possível ver que o modelo teve uma acurácia maior sem aplicar transfer learning, isso provavelmente é uma consequência de todos os pesos serem treinados para esse problema específico. Apesar disso, usar transfer learning fez com que o modelo tivesse uma acurácia melhor nas primeiras épocas. Tendo isso em vista, pode ser interessante treinar o modelo com pesos iniciais da ImageNet, mas todas as camadas treináveis.","metadata":{}},{"cell_type":"markdown","source":"#### Transfer learning sem congelar as camadas iniciais:","metadata":{}},{"cell_type":"code","source":"with strategy.scope():\n    model = tf.keras.applications.xception.Xception(\n        weights='imagenet',\n        include_top=False ,\n        input_shape=[*IMAGE_SIZE, 3]\n    )\n    \n    \n    model = tf.keras.Sequential([\n        model,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(len(CLASSES), activation='softmax')\n    ])\nmodel.compile(\n    optimizer='adam',\n    loss = 'sparse_categorical_crossentropy',\n    metrics=['sparse_categorical_accuracy'],\n)\nhistory = model.fit(\n    ds_train,\n    validation_data=ds_valid,\n    epochs=EPOCHS,\n    steps_per_epoch=STEPS_PER_EPOCH,\n)","metadata":{"execution":{"iopub.status.busy":"2021-10-27T23:52:12.701997Z","iopub.execute_input":"2021-10-27T23:52:12.702353Z","iopub.status.idle":"2021-10-27T23:54:29.720007Z","shell.execute_reply.started":"2021-10-27T23:52:12.702312Z","shell.execute_reply":"2021-10-27T23:54:29.719163Z"},"trusted":true},"execution_count":null,"outputs":[]}]}