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April 13, 2023 16:56
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import tensorflow as tf | |
from tensorflow.keras.models import Sequential | |
from tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, Dropout | |
from tensorflow.keras.preprocessing.image import ImageDataGenerator | |
import numpy as np | |
import os | |
import cv2 | |
# Configuration | |
img_width, img_height = 224, 224 | |
input_shape = (img_width, img_height, 3) | |
batch_size = 32 | |
no_epochs = 20 | |
no_classes = 1 | |
validation_split = 0.2 | |
verbosity = 1 | |
# Load data using ImageDataGenerator | |
datagen = ImageDataGenerator(rescale=1./255, validation_split=validation_split) | |
train_generator = datagen.flow_from_directory( | |
'hotdog/train/', | |
target_size=(img_width, img_height), | |
batch_size=batch_size, | |
class_mode='binary', | |
subset='training') | |
val_generator = datagen.flow_from_directory( | |
'hotdog/train/', | |
target_size=(img_width, img_height), | |
batch_size=batch_size, | |
class_mode='binary', | |
subset='validation') | |
# Model creation | |
def create_model(): | |
model = Sequential() | |
model.add(Conv2D(16, kernel_size=(3, 3), activation='relu', input_shape=input_shape)) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Flatten()) | |
model.add(Dense(256, activation='relu')) | |
model.add(Dropout(0.5)) | |
model.add(Dense(1, activation='sigmoid')) | |
return model | |
# Model compilation | |
def compile_model(model): | |
model.compile(loss='binary_crossentropy', | |
optimizer=tf.keras.optimizers.Adam(), | |
metrics=['accuracy']) | |
return model | |
# Model training | |
def train_model(model, train_generator, val_generator): | |
model.fit(train_generator, | |
epochs=no_epochs, | |
verbose=verbosity, | |
validation_data=val_generator) | |
return model | |
# Model testing | |
def test_model(model, test_generator): | |
score = model.evaluate(test_generator, verbose=0) | |
print(f'Test loss: {score[0]} / Test accuracy: {score[1]}') | |
return model | |
# Predict | |
def predict_model(model, image_path): | |
# Load and preprocess image | |
img = cv2.imread(image_path) | |
resized_img = cv2.resize(img, (img_width, img_height)) | |
input_img = np.array(resized_img).reshape(1, img_width, img_height, 3)/255. | |
# Predict class probabilities | |
class_probabilities = model.predict(input_img) | |
# Check if image contains a hotdog | |
contains_hotdog = class_probabilities[0, 0] > 0.5 | |
print(f'The image {image_path} contains a hotdog: {contains_hotdog}') | |
# Create and train the model | |
model = create_model() | |
model = compile_model(model) | |
model = train_model(model, train_generator, val_generator) | |
# Load test data using ImageDataGenerator | |
test_datagen = ImageDataGenerator(rescale=1./255) | |
test_generator = test_datagen.flow_from_directory | |
predict_model(model, 'classic-hot-dog.png') | |
predict_model(model, 'person.png') | |
predict_model(model, 'burger.jpg') |
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