model_4.py

model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(64,64,3)),
    tf.keras.layers.MaxPool2D(pool_size=(2,2)),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Conv2D(32, 3, activation='relu'),
    tf.keras.layers.MaxPool2D(pool_size=(2,2)),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Conv2D(32, 3, activation='relu'),
    tf.keras.layers.MaxPool2D(pool_size=(2,2)),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Conv2D(32, 3, activation='relu'),
    tf.keras.layers.MaxPool2D(pool_size=(2,2)),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(2, activation='softmax')
])

model.summary()

# Compile the model
model.compile(loss=tf.keras.losses.CategoricalCrossentropy(),
              optimizer=opt1,
              metrics=['accuracy'])

# Fit the model
history = model.fit(training_set,
                    epochs=100,
                    steps_per_epoch=len(training_set),
                    validation_data=testing_set,
                    validation_steps=len(testing_set),
                    callbacks=callbacks)