CNN Resnet分类

Question

我是这里的新手，所以如果我事先写错了什么，对不起。

我试图使用Resnet50模型对皮肤病进行分类。当我试图运行模型时，会出现一个错误。首先，我考虑了将图像转换为数据类型的原因，并将它们转换为张量，但没有工作。

这是我的密码

directory_infection_negative = glob.glob("/content/drive/MyDrive/DFU_Classification_Datasets/DFU_Classification_Datasets/PartB_DFU_Dataset/Infection/Aug-Negative/*")
for img in directory_infection_negative:
    image = cv.imread(img)
    imageRGB = cv.cvtColor(image, cv.COLOR_BGR2RGB)
    img = cv2.resize(imageRGB, (size,size), interpolation = cv2.INTER_AREA)
    imgT = tf.convert_to_tensor(img)
    datasets.append(imgT)
    labels.append(0)

def resnet(input_shape, n_classes):
  
  def conv_bn_rl(x, f, k=1, s=1, p='same'):
    x = Conv2D(f, k, strides=s, padding=p)(x)
    x = BatchNormalization()(x)
    x = ReLU()(x)
    return x
  
  
  def identity_block(tensor, f):
    x = conv_bn_rl(tensor, f)
    x = conv_bn_rl(x, f, 3)
    x = Conv2D(4*f, 1)(x)
    x = BatchNormalization()(x)
    
    x = Add()([x, tensor])
    output = ReLU()(x)
    return output
  
  
  def conv_block(tensor, f, s):
    x = conv_bn_rl(tensor, f)
    x = conv_bn_rl(x, f, 3, s)
    x = Conv2D(4*f, 1)(x)
    x = BatchNormalization()(x)
    
    shortcut = Conv2D(4*f, 1, strides=s)(tensor)
    shortcut = BatchNormalization()(shortcut)
    
    x = Add()([x, shortcut])
    output = ReLU()(x)
    return output
  
  
  def resnet_block(x, f, r, s=2):
    x = conv_block(x, f, s)
    for _ in range(r-1):
      x = identity_block(x, f)
    return x
    
  
  input = Input(input_shape)
  
  x = conv_bn_rl(input, 64, 7, 2)
  x = MaxPool2D(3, strides=2, padding='same')(x)
  
  x = resnet_block(x, 64, 3, 1)
  x = resnet_block(x, 128, 4)
  x = resnet_block(x, 256, 6)
  x = resnet_block(x, 512, 3)
  
  x = GlobalAvgPool2D()(x)
  
  output = Dense(n_classes, activation='softmax')(x)
  
  model = Model(input, output)
  return model
     
        

INPUT_SHAPE = 224,224,3
N_CLASSES = 4

K.clear_session()
model = resnet(INPUT_SHAPE, N_CLASSES)
model.compile(optimizer = 'adam',
              loss = 'categorical_crossentropy',
              metrics=['accuracy'])
model.summary()

from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical

X_train, X_test, y_train, y_test = train_test_split(datasets, to_categorical(np.array(labels)), test_size=0.2, random_state=0)

y_train = to_categorical(y_train, 4)
y_test = to_categorical(y_test, 4)
y_train.shape

print(len(X_train))
print(len(X_test))
print(len(y_train))
print(len(y_test))

print(type(X_train))
print(type(X_test))

print(type(X_train[0]))

history = model.fit((X_train), (y_train), batch_size = 32, verbose = 1, epochs= 50, validation_split=0.1, shuffle = False)

print('Test Accuracy: {:.2f}%'.format(model.evaluate(np.array(X_test), np.array(y_test))[1]*100))

f,(ax1, ax2) = plt.subplots(1,2, figsize=(12,4))
t = f.suptitle('CNN Performance', fontsize=12)
f.subplots_adjust(top =0.85, wspace=0.3)

max_epoch = len(history.history['accuracy'])+1
epoch_list = list(range(1,max_epoch))
ax1.plot(epoch_list, history.history['accuracy'], label='Train Accuracy')
ax1.plot(epoch_list, history.history['val_accuracy'], label='Validation Accuracy')
ax1.set_xticks(np.arange(1, max_epoch,5))
ax1.set_ylabel('Accuracy Value')
ax1.set_xlabel('Epoch')
ax1.set_title('Accuracy')
l1 = ax1.legend(loc="best")

ax2.plot(epoch_list, history.history['loss'], label='Train Loss')
ax2.plot(epoch_list, history.history['val_loss'], label='Validation Loss')
ax2.set_xticks(np.arange(1, max_epoch,5))
ax2.set_ylabel('Loss Value')
ax2.set_xlabel('Epoch')
ax2.set_title('Loss')
l2 = ax2.legend(loc="best")

model.save('DFU Classifier - Resnet50')

错误说：

/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
   1127           except Exception as e:  # pylint:disable=broad-except
   1128             if hasattr(e, "ag_error_metadata"):
-> 1129               raise e.ag_error_metadata.to_exception(e)
   1130             else:
   1131               raise

ValueError:在用户代码中：

File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 878, in train_function  *
    return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 867, in step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 860, in run_step  **
    outputs = model.train_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 808, in train_step
    y_pred = self(x, training=True)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler
    raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py", line 199, in assert_input_compatibility
    raise ValueError(f'Layer "{layer_name}" expects {len(input_spec)} input(s),'

ValueError: Layer "model" expects 1 input(s), but it received 12608 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:1' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:2' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:3' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:4' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:5' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:6' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:7' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:8' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:9' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:10' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:11' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:12' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:13' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:14' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:15' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:16' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:17' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:18' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:19' shape=(None, 224, 3) dtype=uint8>, <tf.Tensor 'IteratorGetNext:20' shape=(None, 224, 3) dtype=uin...

提前谢谢你

Answer 1

所以，我会数数问题：

1. 如果您使用fit，则不需要(不应该)将图像转换为张量。因此，删除： imgT = tf.convert_to_tensor(img)
2. 在此之后，您应该转换数据集，或者至少将输入到网络np.array或np.array列表的任何内容。Fit期望数组或np.array本身的列表，否则它会与它们的算法逻辑 .So相反，将数据集转换成合适的格式将解决许多输入问题： 数据集=np.array(数据集，dtype=np.float32)