我是ML的新手。我正在尝试制作一个包含数字的图像分类的基本示例。我创建了自己的数据集,但准确性差(11%)。我有246个训练项目和62个测试项目。 这是我的代码:
#TRAINING
def load_data(input_path, img_height, img_width):
data = []
labels = []
for imagePath in os.listdir(input_path):
labels_path = os.path.join(input_path, imagePath)
if os.path.isdir(labels_path):
for img_path in os.listdir(labels_path):
labels.append(imagePath)
img_full_path = os.path.join(labels_path, img_path)
img = image.load_img(img_full_path, target_size=(img_height, img_width))
img = image.img_to_array(img)
data.append(img)
return data, labels
train_data = []
train_labels = []
test_data = []
test_labels = []
train_data, train_labels = load_data(train_path, 28, 28)
test_data, test_labels = load_data(test_path, 28, 28)
train_data = np.array(train_data)
train_data = train_data / 255.0
train_data = tf.reshape(train_data, train_data.shape[:3])
train_labels = np.array(train_labels)
train_labels = np.asfarray(train_labels,float)
test_data = np.array(test_data)
test_data = tf.reshape(test_data, test_data.shape[:3])
test_data = test_data / 255.0
test_labels = np.array(test_labels)
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_data, train_labels, batch_size=10, epochs=5, steps_per_epoch=246)
test_loss, test_acc = model.evaluate(test_data, test_labels, steps=1)
print('Test accuracy:', test_acc)
#CLASSIFICATION
def classify(input_path):
if os.path.isdir(input_path):
images = []
for file_path in os.listdir(input_path):
full_path = os.path.join(input_path, file_path)
img_tensor = preprocess_images(full_path, 28, 28, "L")
images.append(img_tensor)
images = np.array(images)
images = tf.reshape(images,(images.shape[0],images.shape[2],images.shape[3]))
predictions = model.predict(images, steps = 1)
for i in range(len(predictions)):
print("Image", i , "is", np.argmax(predictions[i]))
def preprocess_images(image_path, img_height, img_width, mode):
img = image.load_img(image_path, target_size=(img_height, img_width))
#convert 3-channel image to 1-channel
img = img.convert(mode)
img_tensor = image.img_to_array(img)
img_tensor = np.expand_dims(img_tensor, axis=0)
img_tensor /= 255.0
img_tensor = tf.reshape(img_tensor, img_tensor.shape[:3])
return tf.keras.backend.eval(img_tensor)
进行预测时,总是得到结果“ Image is 5”。所以,我有两个问题: -如何获得其他[0-9]类作为输出? -通过增加数据数量可以提高准确性吗?
谢谢。
答案 0 :(得分:1)
您的load_data()
函数是罪魁祸首-您需要将数据集的标签作为整数而不是字符串filepath返回
能否通过增加数据数量来获得更好的准确性?
通常,是的。
您的模型本质上没有任何错误。我显然无权访问您创建的数据集,但可以在MNIST数据集(您的数据集可能正在尝试镜像)上对其进行测试:
(train_data, train_labels),(test_data, test_labels) = tf.keras.datasets.mnist.load_data()
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_data, train_labels, batch_size=10, epochs=5)
test_loss, test_acc = model.evaluate(test_data, test_labels)
print('Test accuracy:', test_acc)
这样做,我们可以训练到大约93%的精度:
Test accuracy: 0.9275
然后,您的推断代码也可以在测试数据上按预期工作:
predictions = model.predict(test_data)
for i in range(len(predictions)):
print("Image", i , "is", np.argmax(predictions[i]))
提供输出,您期望:
Image 0 is 7
Image 1 is 2
Image 2 is 1
Image 3 is 0
Image 4 is 4
...
所以我们知道模型可以工作。那么,与MNIST(60000)相比,性能差异是否仅降低到数据集(246)的大小?
这很容易测试-我们可以将大小相同的MNIST数据切片并重复练习:
train_data = train_data[:246]
train_labels = train_labels[:246]
test_data = test_data[:62]
test_labels = test_labels[:62]
因此,这次我看到了准确性的大幅降低(这次约为66%),但是即使子集很小,我也可以训练模型达到比您看到的更高的准确性。
因此,问题必须出在您的数据预处理(或数据集本身)上。
实际上,看着您的load_data()
函数,我可以看到问题出在您生成的标签上。您的labels
刚出现在图片路径中吗?你有这个:
# --snip--
for img_path in os.listdir(labels_path):
labels.append(imagePath) ## <-- this does not look right!
# --snip--
您需要使用图像所属类别的整数值填充labels
(对于整数,这是介于0和9之间的整数)