Add solution to exercise 9
Aurélien Geron
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f86635b233
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@ -1603,6 +1603,203 @@
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" plt.imshow(image, cmap=\"binary\")\n",
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" plt.axis(\"off\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Exercise Solutions"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. to 8.\n",
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"\n",
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"See Appendix A."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 9.\n",
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"_Exercise: Try using a denoising autoencoder to pretrain an image classifier. You can use MNIST (the simplest option), or a more complex image dataset such as [CIFAR10](https://homl.info/122) if you want a bigger challenge. Regardless of the dataset you're using, follow these steps:_\n",
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"* Split the dataset into a training set and a test set. Train a deep denoising autoencoder on the full training set.\n",
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"* Check that the images are fairly well reconstructed. Visualize the images that most activate each neuron in the coding layer.\n",
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"* Build a classification DNN, reusing the lower layers of the autoencoder. Train it using only 500 images from the training set. Does it perform better with or without pretraining?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 144,
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"metadata": {},
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"outputs": [],
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"source": [
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"[X_train, y_train], [X_test, y_test] = keras.datasets.cifar10.load_data()\n",
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"X_train = X_train / 255\n",
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"X_test = X_test / 255"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 203,
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"metadata": {},
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"outputs": [],
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"source": [
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"tf.random.set_seed(42)\n",
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"np.random.seed(42)\n",
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"\n",
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"denoising_encoder = keras.models.Sequential([\n",
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" keras.layers.GaussianNoise(0.1, input_shape=[32, 32, 3]),\n",
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" keras.layers.Conv2D(32, kernel_size=3, padding=\"same\", activation=\"relu\"),\n",
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" keras.layers.MaxPool2D(),\n",
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" keras.layers.Flatten(),\n",
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" keras.layers.Dense(512, activation=\"relu\"),\n",
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"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 204,
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"metadata": {},
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"outputs": [],
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"source": [
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"denoising_encoder.summary()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 205,
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"metadata": {},
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"outputs": [],
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"source": [
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"denoising_decoder = keras.models.Sequential([\n",
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" keras.layers.Dense(16 * 16 * 32, activation=\"relu\", input_shape=[512]),\n",
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" keras.layers.Reshape([16, 16, 32]),\n",
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" keras.layers.Conv2DTranspose(filters=3, kernel_size=3, strides=2,\n",
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" padding=\"same\", activation=\"sigmoid\")\n",
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"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 206,
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"metadata": {},
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"outputs": [],
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"source": [
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"denoising_decoder.summary()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 207,
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"metadata": {},
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"outputs": [],
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"source": [
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"denoising_ae = keras.models.Sequential([denoising_encoder, denoising_decoder])\n",
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"denoising_ae.compile(loss=\"binary_crossentropy\", optimizer=keras.optimizers.Nadam(),\n",
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" metrics=[\"mse\"])\n",
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"history = denoising_ae.fit(X_train, X_train, epochs=10,\n",
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" validation_data=(X_test, X_test))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 208,
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"metadata": {},
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"outputs": [],
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"source": [
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"n_images = 5\n",
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"new_images = X_test[:n_images]\n",
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"new_images_noisy = new_images + np.random.randn(n_images, 32, 32, 3) * 0.1\n",
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"new_images_denoised = denoising_ae.predict(new_images_noisy)\n",
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"\n",
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"plt.figure(figsize=(6, n_images * 2))\n",
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"for index in range(n_images):\n",
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" plt.subplot(n_images, 3, index * 3 + 1)\n",
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" plt.imshow(new_images[index])\n",
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" plt.axis('off')\n",
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" if index == 0:\n",
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" plt.title(\"Original\")\n",
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" plt.subplot(n_images, 3, index * 3 + 2)\n",
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" plt.imshow(np.clip(new_images_noisy[index], 0., 1.))\n",
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" plt.axis('off')\n",
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" if index == 0:\n",
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" plt.title(\"Noisy\")\n",
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" plt.subplot(n_images, 3, index * 3 + 3)\n",
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" plt.imshow(new_images_denoised[index])\n",
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" plt.axis('off')\n",
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" if index == 0:\n",
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" plt.title(\"Denoised\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 10.\n",
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"_Exercise: Train a variational autoencoder on the image dataset of your choice, and use it to generate images. Alternatively, you can try to find an unlabeled dataset that you are interested in and see if you can generate new samples._\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 11.\n",
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"_Exercise: Train a DCGAN to tackle the image dataset of your choice, and use it to generate images. Add experience replay and see if this helps. Turn it into a conditional GAN where you can control the generated class._\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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@ -1621,7 +1818,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.6"
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"version": "3.7.9"
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},
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"nav_menu": {
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"height": "381px",
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