mirror of
https://github.com/fchollet/deep-learning-with-python-notebooks.git
synced 2021-07-27 01:28:40 +03:00
245 lines
6.7 KiB
Plaintext
245 lines
6.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\n\nThis notebook was generated for TensorFlow 2.6."
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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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"colab_type": "text"
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},
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"source": [
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"## DeepDream"
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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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"colab_type": "text"
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},
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"source": [
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"### Implementing DeepDream in Keras"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"from tensorflow import keras\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"base_image_path = keras.utils.get_file(\n",
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" \"coast.jpg\", origin=\"https://img-datasets.s3.amazonaws.com/coast.jpg\")\n",
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"\n",
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"plt.axis(\"off\")\n",
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"plt.imshow(keras.utils.load_img(base_image_path))"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"from tensorflow.keras.applications import inception_v3\n",
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"model = inception_v3.InceptionV3(weights=\"imagenet\", include_top=False)"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"layer_settings = {\n",
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" \"mixed4\": 1.0,\n",
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" \"mixed5\": 1.5,\n",
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" \"mixed6\": 2.0,\n",
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" \"mixed7\": 2.5,\n",
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"}\n",
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"outputs_dict = dict(\n",
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" [\n",
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" (layer.name, layer.output)\n",
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" for layer in [model.get_layer(name) for name in layer_settings.keys()]\n",
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" ]\n",
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")\n",
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"feature_extractor = keras.Model(inputs=model.inputs, outputs=outputs_dict)"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"def compute_loss(input_image):\n",
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" features = feature_extractor(input_image)\n",
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" loss = tf.zeros(shape=())\n",
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" for name in features.keys():\n",
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" coeff = layer_settings[name]\n",
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" activation = features[name]\n",
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" loss += coeff * tf.reduce_mean(tf.square(activation[:, 2:-2, 2:-2, :]))\n",
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" return loss"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"\n",
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"@tf.function\n",
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"def gradient_ascent_step(image, learning_rate):\n",
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" with tf.GradientTape() as tape:\n",
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" tape.watch(image)\n",
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" loss = compute_loss(image)\n",
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" grads = tape.gradient(loss, image)\n",
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" grads = tf.math.l2_normalize(grads)\n",
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" image += learning_rate * grads\n",
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" return loss, image\n",
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"\n",
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"\n",
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"def gradient_ascent_loop(image, iterations, learning_rate, max_loss=None):\n",
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" for i in range(iterations):\n",
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" loss, image = gradient_ascent_step(image, learning_rate)\n",
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" if max_loss is not None and loss > max_loss:\n",
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" break\n",
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" print(f\"... Loss value at step {i}: {loss:.2f}\")\n",
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" return image"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"step = 20.\n",
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"num_octave = 3\n",
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"octave_scale = 1.4\n",
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"iterations = 30\n",
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"max_loss = 15."
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"def preprocess_image(image_path):\n",
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" img = keras.utils.load_img(image_path)\n",
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" img = keras.utils.img_to_array(img)\n",
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" img = np.expand_dims(img, axis=0)\n",
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" img = keras.applications.inception_v3.preprocess_input(img)\n",
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" return img\n",
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"\n",
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"def deprocess_image(img):\n",
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" img = img.reshape((img.shape[1], img.shape[2], 3))\n",
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" img /= 2.0\n",
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" img += 0.5\n",
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" img *= 255.\n",
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" img = np.clip(img, 0, 255).astype(\"uint8\")\n",
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" return img"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"original_img = preprocess_image(base_image_path)\n",
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"original_shape = original_img.shape[1:3]\n",
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"\n",
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"successive_shapes = [original_shape]\n",
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"for i in range(1, num_octave):\n",
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" shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape])\n",
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" successive_shapes.append(shape)\n",
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"successive_shapes = successive_shapes[::-1]\n",
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"\n",
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"shrunk_original_img = tf.image.resize(original_img, successive_shapes[0])\n",
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"\n",
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"img = tf.identity(original_img)\n",
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"for i, shape in enumerate(successive_shapes):\n",
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" print(f\"Processing octave {i} with shape {shape}\")\n",
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" img = tf.image.resize(img, shape)\n",
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" img = gradient_ascent_loop(\n",
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" img, iterations=iterations, learning_rate=step, max_loss=max_loss\n",
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" )\n",
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" upscaled_shrunk_original_img = tf.image.resize(shrunk_original_img, shape)\n",
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" same_size_original = tf.image.resize(original_img, shape)\n",
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" lost_detail = same_size_original - upscaled_shrunk_original_img\n",
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" img += lost_detail\n",
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" shrunk_original_img = tf.image.resize(original_img, shape)\n",
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"\n",
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"keras.utils.save_img(\"dream.png\", deprocess_image(img.numpy()))"
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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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"colab_type": "text"
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},
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"source": [
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"### Wrapping up"
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]
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}
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],
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"metadata": {
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"colab": {
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"collapsed_sections": [],
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"name": "chapter12_part02_deep-dream.i",
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"private_outputs": false,
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"provenance": [],
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"toc_visible": true
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.7.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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} |