examples/autoencoders/intro.ipynb

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  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ce734de3",
   "metadata": {},
   "source": [
    "### Intro to AutoEncoders and MNIST Dataset\n",
    "\n",
    "\"Today two interesting practical applications of autoencoders are data denoising, and dimensionality reduction for data visualization\" ...  \n",
    "\"autoencoders can learn data projections that are more interesting than PCA\" [source](https://blog.keras.io/building-autoencoders-in-keras.html)  \n",
    "\n",
    "\"one reason why they have attracted so much research and attention is because they have long been thought to be a potential avenue for solving the problem of unsupervised learning, i.e. the learning of useful representations without the need for labels\" ...  \n",
    "\"there is significant evidence that focusing on the reconstruction of a picture at the pixel level, for instance, is not conductive to learning interesting, abstract features of the kind that label-supervized learning induces\" [source](https://blog.keras.io/building-autoencoders-in-keras.html)  \n",
    "\n",
    "Previous example: [/examples/nlp/intro.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/nlp/intro.ipynb)  \n",
    "Modified from: [blog.keras.io/building-autoencoders-in-keras](https://blog.keras.io/building-autoencoders-in-keras.html)  \n",
    "Next example: [/examples/autoencoders/sparse.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/autoencoders/sparse.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "74e32580",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "import keras\n",
    "from keras import layers\n",
    "from keras.datasets import mnist\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dc147a67",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('https://blog.keras.io/img/ae/autoencoder_schema.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e48d3a91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M2\n",
      "\n",
      "systemMemory: 8.00 GB\n",
      "maxCacheSize: 2.67 GB\n",
      "\n",
      "autoencoder:\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 784)]             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 32)                25120     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 784)               25872     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 50,992\n",
      "Trainable params: 50,992\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "\n",
      "encoder:\n",
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 784)]             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 32)                25120     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 25,120\n",
      "Trainable params: 25,120\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "\n",
      "decoder:\n",
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 32)]              0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 784)               25872     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 25,872\n",
      "Trainable params: 25,872\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-30 13:42:31.196444: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2022-12-30 13:42:31.196555: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    }
   ],
   "source": [
    "# This is the size of our encoded representations\n",
    "encoding_dim = 32 # 32 floats -> compression of factor 24.5, assuming the input is 784 floats\n",
    "\n",
    "# This is our input image\n",
    "input_img = keras.Input(shape=(784,))\n",
    "# \"encoded\" is the encoded representation of the input\n",
    "encoded = layers.Dense(encoding_dim, activation='relu')(input_img)\n",
    "# \"decoded\" is the lossy reconstruction of the input\n",
    "decoded = layers.Dense(784, activation='sigmoid')(encoded)\n",
    "\n",
    "# This model maps an input to its reconstruction\n",
    "autoencoder = keras.Model(input_img, decoded)\n",
    "\n",
    "# This model maps an input to its encoded representation\n",
    "encoder = keras.Model(input_img, encoded)\n",
    "\n",
    "# This is our encoded (32-dimensional) input\n",
    "encoded_input = keras.Input(shape=(encoding_dim,))\n",
    "# Retrieve the last layer of the autoencoder model\n",
    "decoder_layer = autoencoder.layers[-1]\n",
    "# Create the decoder model\n",
    "decoder = keras.Model(encoded_input, decoder_layer(encoded_input))\n",
    "\n",
    "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n",
    "\n",
    "print('autoencoder:'); autoencoder.summary(); print()\n",
    "print('encoder:'); encoder.summary(); print()\n",
    "print('decoder:'); decoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d1d5efa0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train.shape = (60000, 784)\n",
      "x_test.shape = (10000, 784)\n"
     ]
    }
   ],
   "source": [
    "(x_train, _), (x_test, _) = mnist.load_data() # Labels ignored\n",
    "x_train = x_train.astype('float32') / 255.\n",
    "x_test = x_test.astype('float32') / 255.\n",
    "x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))\n",
    "x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))\n",
    "print(f'{x_train.shape = }')\n",
    "print(f'{x_test.shape = }')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0b95fd83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-30 13:42:31.628818: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2022-12-30 13:42:31.781726: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "235/235 [==============================] - 2s 6ms/step - loss: 0.2767 - val_loss: 0.1929\n",
      "Epoch 2/50\n",
      "  1/235 [..............................] - ETA: 1s - loss: 0.1940"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-30 13:42:33.119903: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "235/235 [==============================] - 1s 5ms/step - loss: 0.1730 - val_loss: 0.1554\n",
      "Epoch 3/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.1461 - val_loss: 0.1357\n",
      "Epoch 4/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.1303 - val_loss: 0.1231\n",
      "Epoch 5/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.1199 - val_loss: 0.1145\n",
      "Epoch 6/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.1127 - val_loss: 0.1084\n",
      "Epoch 7/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.1074 - val_loss: 0.1041\n",
      "Epoch 8/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.1036 - val_loss: 0.1007\n",
      "Epoch 9/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.1007 - val_loss: 0.0983\n",
      "Epoch 10/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0985 - val_loss: 0.0964\n",
      "Epoch 11/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0969 - val_loss: 0.0949\n",
      "Epoch 12/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0958 - val_loss: 0.0940\n",
      "Epoch 13/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0951 - val_loss: 0.0935\n",
      "Epoch 14/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0946 - val_loss: 0.0930\n",
      "Epoch 15/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0942 - val_loss: 0.0928\n",
      "Epoch 16/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0939 - val_loss: 0.0925\n",
      "Epoch 17/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0937 - val_loss: 0.0923\n",
      "Epoch 18/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0936 - val_loss: 0.0923\n",
      "Epoch 19/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0935 - val_loss: 0.0921\n",
      "Epoch 20/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0933 - val_loss: 0.0920\n",
      "Epoch 21/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0933 - val_loss: 0.0920\n",
      "Epoch 22/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0932 - val_loss: 0.0919\n",
      "Epoch 23/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0931 - val_loss: 0.0918\n",
      "Epoch 24/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0931 - val_loss: 0.0917\n",
      "Epoch 25/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0930 - val_loss: 0.0917\n",
      "Epoch 26/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0930 - val_loss: 0.0917\n",
      "Epoch 27/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0929 - val_loss: 0.0917\n",
      "Epoch 28/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0929 - val_loss: 0.0917\n",
      "Epoch 29/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0929 - val_loss: 0.0916\n",
      "Epoch 30/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0928 - val_loss: 0.0916\n",
      "Epoch 31/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0928 - val_loss: 0.0916\n",
      "Epoch 32/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0928 - val_loss: 0.0916\n",
      "Epoch 33/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0928 - val_loss: 0.0915\n",
      "Epoch 34/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0928 - val_loss: 0.0915\n",
      "Epoch 35/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0927 - val_loss: 0.0916\n",
      "Epoch 36/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0927 - val_loss: 0.0915\n",
      "Epoch 37/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0927 - val_loss: 0.0915\n",
      "Epoch 38/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0927 - val_loss: 0.0915\n",
      "Epoch 39/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0927 - val_loss: 0.0915\n",
      "Epoch 40/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0927 - val_loss: 0.0915\n",
      "Epoch 41/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0926 - val_loss: 0.0914\n",
      "Epoch 42/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0926 - val_loss: 0.0915\n",
      "Epoch 43/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0926 - val_loss: 0.0914\n",
      "Epoch 44/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0926 - val_loss: 0.0915\n",
      "Epoch 45/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0926 - val_loss: 0.0916\n",
      "Epoch 46/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0926 - val_loss: 0.0914\n",
      "Epoch 47/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0926 - val_loss: 0.0915\n",
      "Epoch 48/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0926 - val_loss: 0.0914\n",
      "Epoch 49/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0926 - val_loss: 0.0914\n",
      "Epoch 50/50\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0926 - val_loss: 0.0916\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x29df06bf0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoencoder.fit(x_train, x_train, epochs=50, batch_size=256, shuffle=True, validation_data=(x_test, x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0ad16e1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-30 13:43:29.926242: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-12-30 13:43:30.264892: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "encoded_imgs.mean() = 8.946427\n",
      "encoded_imgs.std() = 4.61266\n"
     ]
    }
   ],
   "source": [
    "# Encode and decode some digits\n",
    "# Note that we take them from the *test* set\n",
    "encoded_imgs = encoder.predict(x_test, verbose=0)\n",
    "decoded_imgs = decoder.predict(encoded_imgs, verbose=0)\n",
    "print(f'{encoded_imgs.mean() = }')\n",
    "print(f'{encoded_imgs.std() = }')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c2ab5fea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x200 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_of_digits_to_display = 10\n",
    "plt.style.use('dark_background')\n",
    "fig = plt.figure(figsize=(10, 2))\n",
    "fig.subplots_adjust(hspace=0)\n",
    "for digit_idx in range(num_of_digits_to_display):\n",
    "    # Display original\n",
    "    ax = plt.subplot(2, num_of_digits_to_display, digit_idx + 1)\n",
    "    plt.imshow(x_test[digit_idx].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "\n",
    "    # Display reconstruction\n",
    "    ax = plt.subplot(2, num_of_digits_to_display, digit_idx + 1 + num_of_digits_to_display)\n",
    "    plt.imshow(decoded_imgs[digit_idx].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c484cb68",
   "metadata": {},
   "source": [
    "Previous example: [/examples/nlp/intro.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/nlp/intro.ipynb)  \n",
    "Modified from: [blog.keras.io/building-autoencoders-in-keras](https://blog.keras.io/building-autoencoders-in-keras.html)  \n",
    "Next example: [/examples/autoencoders/sparse.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/autoencoders/sparse.ipynb)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 ('study')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8 (main, Nov 24 2022, 08:08:27) [Clang 14.0.6 ]"
  },
  "vscode": {
   "interpreter": {
    "hash": "29d06b973d1ddb34db3279b24f9b5152402e688db937648b736e855ec4de60c0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}