Keras Autoencoder Jupyter Notebook Tutorials

convolutional_autoencoder.ipynb

deep_autoencoder.ipynb

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   "source": [
    "# Building Autoencoders in Keras\n",
    "The following `Jupyter Notebook` has been *adapted* from the [Keras blog article](https://blog.keras.io/building-autoencoders-in-keras.html) written by *F. Chollet* on [autoencoders](https://en.wikipedia.org/wiki/Autoencoder)."
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    "## Deep Autoencoder\n",
    "We do not have to limit ourselves to a single layer as encoder or decoder, we could instead use a stack of layers, such as:"
   ]
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   "source": [
    "# get initial libs\n",
    "import keras\n",
    "from keras import layers\n",
    "from keras.datasets import mnist\n",
    "import numpy as np\n",
    "import visualization.image"
   ]
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   "source": [
    "input_img = keras.Input(shape=(784,))\n",
    "encoded = layers.Dense(128, activation='relu')(input_img)\n",
    "encoded = layers.Dense(64, activation='relu')(encoded)\n",
    "encoded = layers.Dense(32, activation='relu')(encoded)\n",
    "\n",
    "decoded = layers.Dense(64, activation='relu')(encoded)\n",
    "decoded = layers.Dense(128, activation='relu')(decoded)\n",
    "decoded = layers.Dense(784, activation='sigmoid')(decoded)"
   ]
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   "source": [
    "(x_train, _), (x_test, _) = mnist.load_data()"
   ]
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   "source": [
    "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(x_train.shape)\n",
    "print(x_test.shape)"
   ]
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   "source": [
    "Now train:"
   ]
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   "source": [
    "autoencoder = keras.Model(input_img, decoded)\n",
    "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n",
    "\n",
    "autoencoder.fit(x_train, x_train,\n",
    "                epochs=100,\n",
    "                batch_size=256,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test, x_test))"
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   "execution_count": null,
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   "source": [
    "visualization.image.compare_results(x_test, autoencoder.predict(x_test));"
   ]
  },
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   "cell_type": "markdown",
   "id": "9d6d8eb8-8737-44b6-a9a4-c6551dd3f4f5",
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   "source": [
    "After 100 epochs, it reaches a train and validation loss of ~0.08, a bit better than our previous models. Our reconstructed digits look a bit better too."
   ]
  }
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minimal_autoencoder.ipynb

variational_autoencoder.ipynb