Io sto usando la CNN e MobileNet modelli per la costruzione di un modello per classificare la lingua dei segni per le lettere dell'alfabeto basato su una delle immagini del set di dati. Quindi, si tratta di un multi-classe modello di classificazione. Tuttavia, dopo la compilazione e il montaggio del modello. Ho avuto un elevato grado di precisione (98%). Ma quando si desidera visualizzare la matrice di confusione ho davvero perso matrice. Questo significa che il modello è l'overfitting? e come posso risolvere il problema per ottenere una migliore matrix?
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')