keras_example.ipynb

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     "text": [
      "2022-03-06 21:40:01.244419: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-03-06 21:40:01.244449: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
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      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow_datasets\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtfds\u001b[39;00m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow_datasets'"
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   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_datasets as tfds"
   ]
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   "source": [
    "## Step 1: Create your input pipeline\n",
    "\n",
    "Start by building an efficient input pipeline using advices from:\n",
    "* The [Performance tips](https://www.tensorflow.org/datasets/performances) guide\n",
    "* The [Better performance with the `tf.data` API](https://www.tensorflow.org/guide/data_performance#optimize_performance) guide\n"
   ]
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   "source": [
    "### Load a dataset\n",
    "\n",
    "Load the MNIST dataset with the following arguments:\n",
    "\n",
    "* `shuffle_files=True`: The MNIST data is only stored in a single file, but for larger datasets with multiple files on disk, it's good practice to shuffle them when training.\n",
    "* `as_supervised=True`: Returns a tuple `(img, label)` instead of a dictionary `{'image': img, 'label': label}`."
   ]
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    {
     "ename": "NameError",
     "evalue": "name 'tfds' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m (ds_train, ds_test), ds_info \u001b[38;5;241m=\u001b[39m \u001b[43mtfds\u001b[49m\u001b[38;5;241m.\u001b[39mload(\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmnist\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m      3\u001b[0m     split\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtest\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m      4\u001b[0m     shuffle_files\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m      5\u001b[0m     as_supervised\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m      6\u001b[0m     with_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m      7\u001b[0m )\n",
      "\u001b[0;31mNameError\u001b[0m: name 'tfds' is not defined"
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    "(ds_train, ds_test), ds_info = tfds.load(\n",
    "    'mnist',\n",
    "    split=['train', 'test'],\n",
    "    shuffle_files=True,\n",
    "    as_supervised=True,\n",
    "    with_info=True,\n",
    ")"
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    "### Build a training pipeline\n",
    "\n",
    "Apply the following transformations:\n",
    "\n",
    "* `tf.data.Dataset.map`: TFDS provide images of type `tf.uint8`, while the model expects `tf.float32`. Therefore, you need to normalize images.\n",
    "* `tf.data.Dataset.cache` As you fit the dataset in memory, cache it before shuffling for a better performance.<br/>\n",
    "__Note:__ Random transformations should be applied after caching.\n",
    "* `tf.data.Dataset.shuffle`: For true randomness, set the shuffle buffer to the full dataset size.<br/>\n",
    "__Note:__ For large datasets that can't fit in memory, use `buffer_size=1000` if your system allows it.\n",
    "* `tf.data.Dataset.batch`: Batch elements of the dataset after shuffling to get unique batches at each epoch.\n",
    "* `tf.data.Dataset.prefetch`: It is good practice to end the pipeline by prefetching [for performance](https://www.tensorflow.org/guide/data_performance#prefetching)."
   ]
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    "def normalize_img(image, label):\n",
    "  \"\"\"Normalizes images: `uint8` -> `float32`.\"\"\"\n",
    "  return tf.cast(image, tf.float32) / 255., label\n",
    "\n",
    "ds_train = ds_train.map(\n",
    "    normalize_img, num_parallel_calls=tf.data.AUTOTUNE)\n",
    "ds_train = ds_train.cache()\n",
    "ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples)\n",
    "ds_train = ds_train.batch(128)\n",
    "ds_train = ds_train.prefetch(tf.data.AUTOTUNE)"
   ]
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   "source": [
    "### Build an evaluation pipeline\n",
    "\n",
    "Your testing pipeline is similar to the training pipeline with small differences:\n",
    "\n",
    " * You don't need to call `tf.data.Dataset.shuffle`.\n",
    " * Caching is done after batching because batches can be the same between epochs."
   ]
  },
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    "ds_test = ds_test.map(\n",
    "    normalize_img, num_parallel_calls=tf.data.AUTOTUNE)\n",
    "ds_test = ds_test.batch(128)\n",
    "ds_test = ds_test.cache()\n",
    "ds_test = ds_test.prefetch(tf.data.AUTOTUNE)"
   ]
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   "source": [
    "## Step 2: Create and train the model\n",
    "\n",
    "Plug the TFDS input pipeline into a simple Keras model, compile the model, and train it."
   ]
  },
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     "text": [
      "Epoch 1/6\n",
      "469/469 [==============================] - 4s 3ms/step - loss: 0.3661 - sparse_categorical_accuracy: 0.8988 - val_loss: 0.1958 - val_sparse_categorical_accuracy: 0.9445\n",
      "Epoch 2/6\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.1666 - sparse_categorical_accuracy: 0.9525 - val_loss: 0.1336 - val_sparse_categorical_accuracy: 0.9607\n",
      "Epoch 3/6\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.1197 - sparse_categorical_accuracy: 0.9657 - val_loss: 0.1163 - val_sparse_categorical_accuracy: 0.9647\n",
      "Epoch 4/6\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0928 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.1000 - val_sparse_categorical_accuracy: 0.9699\n",
      "Epoch 5/6\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0760 - sparse_categorical_accuracy: 0.9775 - val_loss: 0.0862 - val_sparse_categorical_accuracy: 0.9733\n",
      "Epoch 6/6\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.0628 - sparse_categorical_accuracy: 0.9814 - val_loss: 0.0823 - val_sparse_categorical_accuracy: 0.9755\n"
     ]
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     "data": {
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       "<keras.callbacks.History at 0x7fb3b06a4650>"
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   "source": [
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "  tf.keras.layers.Dense(128, activation='relu'),\n",
    "  tf.keras.layers.Dense(10)\n",
    "])\n",
    "model.compile(\n",
    "    optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "    metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],\n",
    ")\n",
    "\n",
    "model.fit(\n",
    "    ds_train,\n",
    "    epochs=6,\n",
    "    validation_data=ds_test,\n",
    ")"
   ]
  }
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