Cats_vs_Dogs_Classifier/Cats_vs_Dogs_Classifier.py at main · engHasan1/Cats_vs_Dogs_Classifier · GitHub

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import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import VGG16
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.callbacks import EarlyStopping
import matplotlib.pyplot as plt

train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=60,
    width_shift_range=0.4,
    height_shift_range=0.4,
    shear_range=0.4,
    zoom_range=0.4,
    brightness_range=[0.6, 1.4],
    horizontal_flip=True,
    fill_mode='nearest',
    validation_split=0.2
)

train_generator = train_datagen.flow_from_directory(
    '/content/dogs_vs_cats/train',
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary',
    subset='training'
)

validation_generator = train_datagen.flow_from_directory(
    '/content/dogs_vs_cats/train',
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary',
    subset='validation'
)

base_model = VGG16(weights='imagenet', include_top=False, input_shape=(150, 150, 3))

x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(512, activation='relu')(x)
predictions = Dense(1, activation='sigmoid')(x)

model = Model(inputs=base_model.input, outputs=predictions)

for layer in base_model.layers:
    layer.trainable = False

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),
              loss='binary_crossentropy',
              metrics=['accuracy'])

history = model.fit(
    train_generator,
    epochs=5,
    validation_data=validation_generator
)


for layer in base_model.layers[-4:]:
    layer.trainable = True


model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-5),
              loss='binary_crossentropy',
              metrics=['accuracy'])

early_stopping = EarlyStopping(monitor='val_accuracy', patience=3, restore_best_weights=True)


history = model.fit(
    train_generator,
    epochs=20,
    validation_data=validation_generator,
    callbacks=[early_stopping]
)


model.save('/content/drive/MyDrive/cats_vs_dogs_model_vgg16_finetuned.keras')


plt.plot(history.history['accuracy'], label='Training Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.savefig('/content/drive/MyDrive/accuracy_plot_vgg16_finetuned.png')
plt.show()