Bir görüntü veri kümesine dayalı olarak işaret dilini alfabe harflerine sınıflandırmak için bir model oluşturmak için CNN ve MobileNet modellerini kullanıyorum. Yani, bu çok sınıflı bir sınıflandırma modelidir. Ancak, modeli derledikten ve taktıktan sonra. Yüksek bir doğruluğum var (%98). Ama karışıklık matrisini görselleştirmek istediğimde matrisi gerçekten kaçırdım. Bu, modelin aşırı takıldığı anlamına mı geliyor? ve daha iyi bir matris elde etmek için bunu nasıl düzeltebilirim?
train_path = 'train'
test_path = 'test'
train_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.mobilenet.preprocess_input).flow_from_directory(
directory=train_path, target_size=(64,64), batch_size=10)
test_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.mobilenet.preprocess_input).flow_from_directory(
directory=test_path, target_size=(64,64), batch_size=10)
mobile = tf.keras.applications.mobilenet.MobileNet()
x = mobile.layers[-6].output
output = Dense(units=32, activation='softmax')(x)
model = Model(inputs=mobile.input, outputs=output)
for layer in model.layers[:-23]:
layer.trainable = False
model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
class myCallback(tf.keras.callbacks.Callback):
def on_epoch_end(self,epoch,logs={}):
if(logs.get('val_accuracy')>=0.98):
print('\n Reached to good accuracy')
self.model.stop_training=True
callbacks=myCallback()
model.fit(train_batches,
steps_per_epoch=len(train_batches),
validation_data=test_batches,
validation_steps=len(test_batches),
epochs=10,callbacks=[callbacks])
Epoch 1/10
4498/4498 [==============================] - 979s 217ms/step - loss: 1.3062 - accuracy: 0.6530 - val_loss: 0.1528 - val_accuracy: 0.9594
Epoch 2/10
4498/4498 [==============================] - 992s 221ms/step - loss: 0.1777 - accuracy: 0.9491 - val_loss: 0.1164 - val_accuracy: 0.9691
Epoch 3/10
4498/4498 [==============================] - 998s 222ms/step - loss: 0.1117 - accuracy: 0.9654 - val_loss: 0.0925 - val_accuracy: 0.9734
Epoch 4/10
4498/4498 [==============================] - 1000s 222ms/step - loss: 0.0789 - accuracy: 0.9758 - val_loss: 0.0992 - val_accuracy: 0.9750
Epoch 5/10
4498/4498 [==============================] - 1001s 223ms/step - loss: 0.0626 - accuracy: 0.9805 - val_loss: 0.0818 - val_accuracy: 0.9783
Epoch 6/10
4498/4498 [==============================] - 1007s 224ms/step - loss: 0.0521 - accuracy: 0.9834 - val_loss: 0.0944 - val_accuracy: 0.9789
Epoch 7/10
4498/4498 [==============================] - 1004s 223ms/step - loss: 0.0475 - accuracy: 0.9863 - val_loss: 0.0935 - val_accuracy: 0.9795
Epoch 8/10
4498/4498 [==============================] - 1013s 225ms/step - loss: 0.0371 - accuracy: 0.9880 - val_loss: 0.0854 - val_accuracy: 0.9781
Epoch 9/10
4498/4498 [==============================] - 896s 199ms/step - loss: 0.0365 - accuracy: 0.9879 - val_loss: 0.0766 - val_accuracy: 0.9806
Reached to good accuracy
test_labels = test_batches.classes
predictions = model.predict(x=test_batches, steps=len(test_batches),verbose=0)
cm = confusion_matrix(y_true=test_labels, y_pred=predictions.argmax(axis=1))
cm_plot_labels = ['0','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'
]
plot_confusion_matrix(cm=cm, classes=cm_plot_labels, title='Confusion Matrix')
