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train.py
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train.py
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from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from data_preprocessing import load_dataset
# Define the model architecture
def build_model(input_shape):
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
return model
# Load the dataset
train_images, train_labels = load_dataset('path/to/training_data', target_size=(128, 128))
# Define the model
model = build_model(input_shape=(128, 128, 3))
# Compile the model
model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])
# Define callbacks (optional)
checkpoint = ModelCheckpoint('model_weights.h5', monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')
# Train the model
history = model.fit(train_images, train_labels, epochs=10, batch_size=32, validation_split=0.2, callbacks=[checkpoint])
# Save the trained model
model.save('brain_tumor_detection_cnn_model.h5')
print("Training completed and model saved.")