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eac85ba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | import os
import numpy as np
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import layers, models
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, classification_report
import tensorflow as tf
# Set the paths to your image folders
BASE_DIR = os.path.join(os.getcwd(), 'dataset')
train_dir = os.path.join(BASE_DIR, 'train')
validation_dir = os.path.join(BASE_DIR, 'validation')
test_dir = os.path.join(BASE_DIR, 'test')
# Set the parameters for the data generators
batch_size = 32
img_height, img_width = 256, 256
# Create data generators with data augmentation for training and validation
train_datagen = ImageDataGenerator(
rescale=1.0 / 255.0,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
vertical_flip=True
)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='categorical'
)
validation_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
validation_generator = validation_datagen.flow_from_directory(
validation_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='categorical'
)
# Create a CNN model
cnn_model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(img_height, img_width, 3)),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(128, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(train_generator.num_classes, activation='softmax')
])
# Compile the CNN model
cnn_model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
# Train the CNN model
epochs = 10
history = cnn_model.fit(
train_generator,
epochs=epochs,
validation_data=validation_generator
)
# Save the trained model
model_path = os.path.join(os.getcwd(), 'models', 'rose_model.h5')
os.makedirs(os.path.dirname(model_path), exist_ok=True)
cnn_model.save(model_path)
# Save class names
class_names = list(train_generator.class_indices.keys())
class_names_path = os.path.join(os.getcwd(), 'models', 'class_names.json')
import json
with open(class_names_path, 'w') as f:
json.dump(class_names, f)
# Evaluate the model
test_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
test_generator = test_datagen.flow_from_directory(
test_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='categorical',
shuffle=False
)
# Make predictions
test_predictions = cnn_model.predict(test_generator)
predicted_labels = np.argmax(test_predictions, axis=1)
true_labels = test_generator.classes
# Calculate and print metrics
print("\nClassification Report:")
print(classification_report(true_labels, predicted_labels, target_names=class_names))
# Plot training history
plt.figure(figsize=(12, 4))
# Plot training & validation accuracy
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')
# Plot training & validation loss
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')
plt.tight_layout()
plt.savefig(os.path.join(os.getcwd(), 'models', 'training_history.png'))
plt.close()
print("\nModel saved to:", model_path)
print("Class names saved to:", class_names_path)
print("Training history plot saved to:", os.path.join(os.getcwd(), 'models', 'training_history.png')) |