monitor='val_acc',
verbose=1,
save_best_only=True,
save_weights_only=True)
hist = model.fit(train_set_R1,
Y_train,
validation_data=(test_set_R1, Y_test),
batch_size=16,
nb_epoch=jumEpoch,
shuffle=True,
verbose=1,
callbacks=[checkpointer])
# Evaluate the model
# load best model
model.load_weights(nama_filenya)
Y_pred = model.predict(test_set_R1, batch_size=8)
#print(Y_pred)
k_val = 1
Y_pred_label = []
for idt in range(len(Y_pred)):
Y_pred_label.append(np.argmax(Y_pred[idt]))
print Y_test.shape
print Y_pred.shape
print np.array(Y_pred_label).shape
print np.argmax(Y_test, axis=1)
print("Skor Model:")
accScore = accuracy_score(np.argmax(Y_test, axis=1), Y_pred_label)
print(accScore)
# Some output
model.summary()
plot(model, to_file="architecture.png", show_shapes=True)
if __name__ == '__main__':
# output predicted labels in separate folder for easy viewing
for i in range(10):
os.system("mkdir -p predicted_images/" + str(i))
# select which data you want to evaluate on (validation or testing)
X_evaluation = X_test
Y_evaluation = Y_test
y_evaluation = y_test
with tf.device('/cpu:0'):
model.load_weights(
os.path.join(MODEL_PATH, 'WRN-16-2-own-81accuracy.h5'))
model.compile(optimizer=sgd,
loss="categorical_crossentropy",
metrics=['accuracy'])
validation_datagen = ImageDataGenerator(
featurewise_center=True,
featurewise_std_normalization=True,
zca_whitening=True)
validation_datagen.fit(X_train)
generator = validation_datagen.flow(X_evaluation,
Y_evaluation,
batch_size=1,
shuffle=False)
total_correct = 0
