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May 10, 2022 04:15
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My faulty CS50 AI project
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import cv2 | |
import numpy as np | |
import os | |
import sys | |
import tensorflow as tf | |
from sklearn.model_selection import train_test_split | |
EPOCHS = 10 | |
IMG_WIDTH = 30 | |
IMG_HEIGHT = 30 | |
NUM_CATEGORIES = 43 | |
# TEMP | |
TEST_SIZE = 0.3 | |
def main(): | |
# Check command-line arguments | |
if len(sys.argv) not in [2, 3]: | |
sys.exit("Usage: python traffic.py data_directory [model.h5]") | |
# Get image arrays and labels for all image files | |
images, labels = load_data(sys.argv[1]) | |
# Split data into training and testing sets | |
labels = tf.keras.utils.to_categorical(labels) | |
x_train, x_test, y_train, y_test = train_test_split( | |
np.array(images), np.array(labels), test_size=TEST_SIZE | |
) | |
# Get a compiled neural network | |
model = get_model() | |
# Fit model on training data | |
model.fit(x_train, y_train, epochs=EPOCHS) | |
# Evaluate neural network performance | |
model.evaluate(x_test, y_test, verbose=2) | |
# Save model to file | |
if len(sys.argv) == 3: | |
filename = sys.argv[2] | |
model.save(filename) | |
print(f"Model saved to {filename}.") | |
def load_data(data_dir): | |
""" | |
Load image data from directory `data_dir`. | |
Assume `data_dir` has one directory named after each category, numbered | |
0 through NUM_CATEGORIES - 1. Inside each category directory will be some | |
number of image files. | |
Return tuple `(images, labels)`. `images` should be a list of all | |
of the images in the data directory, where each image is formatted as a | |
numpy ndarray with dimensions IMG_WIDTH x IMG_HEIGHT x 3. `labels` should | |
be a list of integer labels, representing the categories for each of the | |
corresponding `images`. | |
""" | |
images = [] | |
labels = [] | |
for i in range(NUM_CATEGORIES): | |
for j in os.listdir(data_dir + os.sep + str(i)): | |
img = cv2.imread(data_dir + os.sep + str(i) + os.sep + j) | |
resized = cv2.resize(img, (IMG_WIDTH, IMG_HEIGHT)) | |
images.append(resized) | |
labels.append(i) | |
# raise NotImplementedError | |
return (images, labels) | |
def get_model(): | |
""" | |
Returns a compiled convolutional neural network model. Assume that the | |
`input_shape` of the first layer is `(IMG_WIDTH, IMG_HEIGHT, 3)`. | |
The output layer should have `NUM_CATEGORIES` units, one for each category. | |
""" | |
model = tf.keras.Sequential([ | |
# The input | |
tf.keras.layers.Conv2D(10, 3, input_shape=(IMG_WIDTH, IMG_HEIGHT, 3), activation="sigmoid"), | |
tf.keras.layers.MaxPool2D(), | |
tf.keras.layers.Flatten(), | |
# tf.keras.layers.Dense(NUM_CATEGORIES+10, activation = "sigmoid"), | |
tf.keras.layers.Dropout(.3), | |
tf.keras.layers.Dense(NUM_CATEGORIES, activation="sigmoid") | |
]) | |
model.compile( | |
optimizer='adam', | |
loss='binary_crossentropy', | |
metrics=["accuracy"] | |
# auc represents roc effectivness | |
# roc is how good the model's at in different usage thresholds | |
) | |
# raise NotImplementedError | |
return model | |
if __name__ == "__main__": | |
main() |
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