我尝试运行此代码（二进制分类），但仍然停留在此错误：ValueError：检查目标时出错：预期avg_pool具有4个维，但是数组的形状为（100，2）
NUM_CLASSES = 2
CHANNELS = 3
IMAGE_RESIZE = 224
RESNET50_POOLING_AVERAGE = 'avg'
DENSE_LAYER_ACTIVATION = 'softmax'
OBJECTIVE_FUNCTION = 'binary_crossentropy'
NUM_EPOCHS = 10
EARLY_STOP_PATIENCE = 3
STEPS_PER_EPOCH_VALIDATION = 10
BATCH_SIZE_TRAINING = 100
BATCH_SIZE_VALIDATION = 100
resnet_weights_path = '../input/resnet50/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
train_data_dir = "C:\\Users\\Desktop\\RESNET"
model = ResNet50(include_top=True, weights='imagenet')
x = model.get_layer('avg_pool').output
predictions = Dense(1, activation='sigmoid')(x)
model = Model(input = model.input, output = predictions)
print(model.summary())
model.layers.pop()
model = Model(input=model.input,output=model.layers[-1].output)
sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=SGD(lr=0.01, momentum=0.9), metrics= ['binary_accuracy'])
data_dir = "C:\\Users\\Desktop\\RESNET"
batch_size = 32
from keras.applications.resnet50 import preprocess_input
from keras.preprocessing.image import ImageDataGenerator
image_size = IMAGE_RESIZE
data_generator = ImageDataGenerator(preprocessing_function=preprocess_input)
def append_ext(fn):
return fn+".jpg"
dir_path = os.path.dirname(os.path.realpath(__file__))
train_dir_path = dir_path + '\data'
onlyfiles = [f for f in listdir(dir_path) if isfile(join(dir_path, f))]
NUM_CLASSES = 2
data_labels = [0, 1]
t = []
maxi = 25145
LieOffset = 15799
i = 0
while i < maxi: # t = tuple
if i <= LieOffset:
t.append(label['Lie'])
else:
t.append(label['Truth'])
i = i+1
train_datagenerator = ImageDataGenerator(rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
validation_split=0.2)
train_generator = train_datagenerator.flow_from_directory(
train_data_dir,
target_size=(image_size, image_size),
batch_size=BATCH_SIZE_TRAINING,
class_mode='categorical', shuffle=False, subset='training') # set as training data
validation_generator = train_datagenerator.flow_from_directory(
train_data_dir, # same directory as training data
target_size=(image_size, image_size),
batch_size=BATCH_SIZE_TRAINING,
class_mode='categorical', shuffle=False, subset='validation')
(BATCH_SIZE_TRAINING, len(train_generator), BATCH_SIZE_VALIDATION, len(validation_generator))
from sklearn.grid_search import ParameterGrid
param_grid = {'epochs': [5, 10, 15], 'steps_per_epoch' : [10, 20, 50]}
grid = ParameterGrid(param_grid)
# Accumulate history of all permutations (may be for viewing trend) and keep watching for lowest val_loss as final model
for params in grid:
fit_history = model.fit_generator(
train_generator,
steps_per_epoch=STEPS_PER_EPOCH_TRAINING,
epochs = NUM_EPOCHS,
validation_data=validation_generator,
validation_steps=STEPS_PER_EPOCH_VALIDATION,
callbacks=[cb_checkpointer, cb_early_stopper]
)
model.load_weights("../working/best.hdf5")
最佳答案
删除这些行，它将起作用，
model.layers.pop()
model = Model(input=model.input,output=model.layers[-1].output)
前者将删除last（
Dense）层，而字母表示创建的模型没有last（Flatten，因为已弹出Dense）层。这没有意义，因为您的目标数据是（100，2）。您为什么首先将它们放在那里？
我也认为这条线
predictions = Dense(1, activation='sigmoid')(x)
将会出错，因为您的目标数据是2通道，如果出错，则将其更改为
predictions = Dense(2, activation='sigmoid')(x)
更新资料
avg_pool的输出为4维（batch_size，height，width，channel）。您需要先执行Flatten或使用GlobalAveragePooling2D而不是AveragePooling2D。喜欢
x = model.get_layer('avg_pool').output
x = keras.layers.Flatten()(x)
predictions = Dense(1, activation='sigmoid')(x)
要么
model = ResNet50(include_top=False, pooling='avg', weights='imagenet') # `pooling='avg'` makes the `ResNet50` include a `GlobalAveragePoiling` layer and `include_top=False` means that you don't include the imagenet's output layer
x = model.output # as I use `include_top=False`, you don't need to care the layer name, just use the model's output right away
predictions = Dense(1, activation='sigmoid')(x)
同样就像@ bit01所说的那样，将
class_mode='categorical'更改为class_mode ='binary'。关于python - ValueError:检查目标时出错:预期avg_pool具有4维，但数组的形状为(100，2)，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/58307737/