def evaluate():
args = parse_args()
model_name = args.model_name
dataset_path = args.dataset_path
Model, preprocess_input, size = get_model_artifacts(model_name)
idg = ImageDataGenerator(preprocessing_function=preprocess_input)
model = Model(weights="imagenet")
model.compile("adam", "categorical_crossentropy", metrics=["accuracy"])
out = model.evaluate(
idg.flow_from_directory(dataset_path, target_size=(size, size)))
print(out)
print('--> Starting evalutation...')
from keras.preprocessing.image import ImageDataGenerator
from keras import metrics
def in_top_k(y_true, y_pred):
return metrics.top_k_categorical_accuracy(y_true, y_pred, k=5)
val_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)
validation_generator = val_datagen.flow_from_directory(
'./imagenet-data/validation',
target_size=(224, 224),
batch_size=10,
class_mode='categorical',
shuffle=False)
model.trainable = False
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy', in_top_k])
results = model.evaluate(validation_generator,
steps=5000,
workers=1,
max_queue_size=1)
print('--> Results for ' + sys.argv[1])
print(model.metrics_names)
print(results)
#########################################
model.compile(optimizer=SGD(lr=1e-4, momentum=0.9, nesterov=True),
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
"""# Modelin Oluşturulması"""
history = model.fit_generator(datagen.flow(x_test,
y_test,
batch_size=batch_size),
validation_data=(x_test, y_test),
steps_per_epoch=len(x_train) // batch_size,
epochs=epochs)
"""# Sonuçların Görselleştirilmesi"""
score = model.evaluate(x_test, y_test, verbose=0)
print('Test Loss:', score[0])
print('Test Accuracy:', score[1])
print(history.history.keys())
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
lb = LabelBinarizer()
lb.fit(np.asarray(data['primary_microconstituent']))
y = lb.transform(labels)
print('\nLabels Binarized, converting array')
input = np.asarray(processed_imgs)
X_train, X_test, y_train, y_test = train_test_split(
input, y, test_size=0.1, random_state=42)
model = MobileNet(weights=None, classes = 7)
model.summary()
model.compile(loss = 'categorical_crossentropy', optimizer = 'sgd', metrics = ['accuracy'])
time_callback = TimeHistory()
model.fit(X_train, y_train, epochs = 5, batch_size = 32, validation_data=(X_test, y_test), callbacks=[time_callback])
name = 'results/UHCS_MobileNet_Weights'
score = model.evaluate(X_test, y_test)
print('Test score:', score[0])
print('Test accuracy:', score[1])
model.save_weights(name+'.h5')
times = time_callback.times
file = open('MobileNet.txt', 'w')
file.write('Test score:'+ str(score[0])+'\n')
file.write('Test accuracy:'+ str(score[1])+'\n')
file.write(times)
file.close()
