我使用 Python 和 Keras 制作了一个卷积神经网络。我在测试集上测试我的模型,每个类的图像数量是随机的(1 个文件夹包含 x 数量的图像)。我能够获得一个数据框,其中显示图像和目录的文件名以及预测。我想从文件名中删除目录。它随机显示 350 张图像/dogs1.tif,我希望它只显示 dogs1.tif。
#import my model
new_model = tf.keras.models.load_model('model folder')
#upload my test data
train_datagen = ImageDataGenerator(rescale=1./255)
test_batches = train_datagen.flow_from_directory(
'folder containing random images',
target_size=(224, 224),
batch_size=10,
classes = None,
class_mode = None,
shuffle = False)
#my prediction
predictions = new_model.predict(test_batches, steps=35, verbose=0)
#rounding my predctions
rounded_predictions = np.argmax(predictions, axis = -1)
#converting one hot encoded labels to categorical labels
labels =["dog","cat","horse"]
names = [0,1,2]
labels_name = dict(zip(names, labels))
#joining them together
labels_name = dict((v,k) for k,v in labels_name.items())
predictions = [labels[k] for k in rounded_predictions]
#getting files names for the images
filenames= test_batches.filenames
#creating the dataframe
results=pd.DataFrame({"file":filenames,"pr":predictions})
12345678_0001
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