Chapter 14 – Deep Computer Vision Using Convolutional Neural Networks
This notebook contains all the sample code in chapter 14.
Run in Google ColabSetup¶
First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0.
In [1]:
# Python ≥3.5 is required
import sys
assert sys.version_info >= (3, 5)
# Scikit-Learn ≥0.20 is required
import sklearn
assert sklearn.__version__ >= "0.20"
try:
# %tensorflow_version only exists in Colab.
%tensorflow_version 2.x
IS_COLAB = True
except Exception:
IS_COLAB = False
# TensorFlow ≥2.0 is required
import tensorflow as tf
from tensorflow import keras
assert tf.__version__ >= "2.0"
if not tf.config.list_physical_devices('GPU'):
print("No GPU was detected. CNNs can be very slow without a GPU.")
if IS_COLAB:
print("Go to Runtime > Change runtime and select a GPU hardware accelerator.")
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
tf.random.set_seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)
# Where to save the figures
PROJECT_ROOT_DIR = "."
CHAPTER_ID = "cnn"
IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID)
os.makedirs(IMAGES_PATH, exist_ok=True)
def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300):
path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension)
print("Saving figure", fig_id)
if tight_layout:
plt.tight_layout()
plt.savefig(path, format=fig_extension, dpi=resolution)
A couple utility functions to plot grayscale and RGB images:
In [2]:
def plot_image(image):
plt.imshow(image, cmap="gray", interpolation="nearest")
plt.axis("off")
def plot_color_image(image):
plt.imshow(image, interpolation="nearest")
plt.axis("off")
What is a Convolution?¶
In [3]:
import numpy as np
from sklearn.datasets import load_sample_image
# Load sample images
china = load_sample_image("china.jpg") / 255
flower = load_sample_image("flower.jpg") / 255
images = np.array([china, flower])
batch_size, height, width, channels = images.shape
# Create 2 filters
filters = np.zeros(shape=(7, 7, channels, 2), dtype=np.float32)
filters[:, 3, :, 0] = 1 # vertical line
filters[3, :, :, 1] = 1 # horizontal line
outputs = tf.nn.conv2d(images, filters, strides=1, padding="SAME")
plt.imshow(outputs[0, :, :, 1], cmap="gray") # plot 1st image's 2nd feature map
plt.axis("off") # Not shown in the book
plt.show()
In [4]:
for image_index in (0, 1):
for feature_map_index in (0, 1):
plt.subplot(2, 2, image_index * 2 + feature_map_index + 1)
plot_image(outputs[image_index, :, :, feature_map_index])
plt.show()
In [5]:
def crop(images):
return images[150:220, 130:250]
In [6]:
plot_image(crop(images[0, :, :, 0]))
save_fig("china_original", tight_layout=False)
plt.show()
for feature_map_index, filename in enumerate(["china_vertical", "china_horizontal"]):
plot_image(crop(outputs[0, :, :, feature_map_index]))
save_fig(filename, tight_layout=False)
plt.show()
In [7]:
plot_image(filters[:, :, 0, 0])
plt.show()
plot_image(filters[:, :, 0, 1])
plt.show()
Convolutional Layer¶
Using keras.layers.Conv2D():
In [8]:
conv = keras.layers.Conv2D(filters=32, kernel_size=3, strides=1,
padding="SAME", activation="relu")
VALID vs SAME padding¶
Confusingly, "VALID" padding means no padding at all.
In [10]:
kernel_size = 7
strides = 2
conv_valid = keras.layers.Conv2D(filters=1, kernel_size=kernel_size, strides=strides, padding="VALID")
conv_same = keras.layers.Conv2D(filters=1, kernel_size=kernel_size, strides=strides, padding="SAME")
Pooling layer¶
Max pooling¶
In [11]:
max_pool = keras.layers.MaxPool2D(pool_size=2)
In [12]:
cropped_images = np.array([crop(image) for image in images], dtype=np.float32)
output = max_pool(cropped_images)
In [13]:
fig = plt.figure(figsize=(12, 8))
gs = mpl.gridspec.GridSpec(nrows=1, ncols=2, width_ratios=[2, 1])
ax1 = fig.add_subplot(gs[0, 0])
ax1.set_title("Input", fontsize=14)
ax1.imshow(cropped_images[0]) # plot the 1st image
ax1.axis("off")
ax2 = fig.add_subplot(gs[0, 1])
ax2.set_title("Output", fontsize=14)
ax2.imshow(output[0]) # plot the output for the 1st image
ax2.axis("off")
save_fig("china_max_pooling")
plt.show()
Average pooling¶
In [14]:
avg_pool = keras.layers.AvgPool2D(pool_size=2)
In [15]:
output_avg = avg_pool(cropped_images)
In [16]:
fig = plt.figure(figsize=(12, 8))
gs = mpl.gridspec.GridSpec(nrows=1, ncols=2, width_ratios=[2, 1])
ax1 = fig.add_subplot(gs[0, 0])
ax1.set_title("Input", fontsize=14)
ax1.imshow(cropped_images[0]) # plot the 1st image
ax1.axis("off")
ax2 = fig.add_subplot(gs[0, 1])
ax2.set_title("Output", fontsize=14)
ax2.imshow(output_avg[0]) # plot the output for the 1st image
ax2.axis("off")
plt.show()
Tackling Fashion MNIST With a CNN¶
In [17]:
(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()
X_train, X_valid = X_train_full[:-5000], X_train_full[-5000:]
y_train, y_valid = y_train_full[:-5000], y_train_full[-5000:]
X_mean = X_train.mean(axis=0, keepdims=True)
X_std = X_train.std(axis=0, keepdims=True) + 1e-7
X_train = (X_train - X_mean) / X_std
X_valid = (X_valid - X_mean) / X_std
X_test = (X_test - X_mean) / X_std
X_train = X_train[..., np.newaxis]
X_valid = X_valid[..., np.newaxis]
X_test = X_test[..., np.newaxis]
Note : Partial functions allow one to derive a function with x parameters to a function with fewer parameters and fixed values set for the more limited function.
In [18]:
from functools import partial
DefaultConv2D = partial(keras.layers.Conv2D,
kernel_size=3, activation='relu', padding="SAME")
model = keras.models.Sequential([
DefaultConv2D(filters=64, kernel_size=7, input_shape=[28, 28, 1]),
keras.layers.MaxPooling2D(pool_size=2),
DefaultConv2D(filters=128),
DefaultConv2D(filters=128),
keras.layers.MaxPooling2D(pool_size=2),
DefaultConv2D(filters=256),
DefaultConv2D(filters=256),
keras.layers.MaxPooling2D(pool_size=2),
keras.layers.Flatten(),
keras.layers.Dense(units=128, activation='relu'),
keras.layers.Dropout(0.5),
keras.layers.Dense(units=64, activation='relu'),
keras.layers.Dropout(0.5),
keras.layers.Dense(units=10, activation='softmax'),
])
In [19]:
model.compile(loss="sparse_categorical_crossentropy", optimizer="nadam", metrics=["accuracy"])
history = model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid))
score = model.evaluate(X_test, y_test)
X_new = X_test[:10] # pretend we have new images
y_pred = model.predict(X_new)
Using a Pretrained Model¶
In [32]:
model = keras.applications.resnet50.ResNet50(weights="imagenet")
In [33]:
images_resized = tf.image.resize(images, [224, 224])
plot_color_image(images_resized[0])
plt.show()
In [34]:
images_resized = tf.image.resize_with_pad(images, 224, 224, antialias=True)
plot_color_image(images_resized[0])
In [35]:
images_resized = tf.image.resize_with_crop_or_pad(images, 224, 224)
plot_color_image(images_resized[0])
plt.show()
In [36]:
china_box = [0, 0.03, 1, 0.68]
flower_box = [0.19, 0.26, 0.86, 0.7]
images_resized = tf.image.crop_and_resize(images, [china_box, flower_box], [0, 1], [224, 224])
plot_color_image(images_resized[0])
plt.show()
plot_color_image(images_resized[1])
plt.show()
In [37]:
inputs = keras.applications.resnet50.preprocess_input(images_resized * 255)
Y_proba = model.predict(inputs)
In [38]:
Y_proba.shape
Out[38]:
In [39]:
top_K = keras.applications.resnet50.decode_predictions(Y_proba, top=3)
for image_index in range(len(images)):
print("Image #{}".format(image_index))
for class_id, name, y_proba in top_K[image_index]:
print(" {} - {:12s} {:.2f}%".format(class_id, name, y_proba * 100))
print()
In [ ]: