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[英]ValueError: Error when checking target: expected activation_5 to have shape (1,) but got array with shape (100,)
[英]ValueError: Error when checking target: expected avg_pool to have 4 dimensions, but got array with shape (100, 2)
我尝试运行此代码(二进制分类),但仍然遇到此错误: ValueError: Error when checks target: expected avg_pool to have 4 dimension, but got array with shape (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")
删除这些行,它会工作,
model.layers.pop()
model = Model(input=model.input,output=model.layers[-1].output)
前者是删除最后一个( Dense
)层,字母意味着创建一个 model 没有最后一个( Flatten
,因为Dense
已经弹出)层。
这没有意义,因为您的目标数据是 (100, 2)。 你为什么把它们放在首位?
我也认为这条线
predictions = Dense(1, activation='sigmoid')(x)
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)
avg_pool 的avg_pool
为 4 维,(batch_size, height, width, channel)。 您需要先进行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)
或者
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)
也正如@bit01 所说,将class_mode='categorical'
更改为class_mode='binary'。
在train_generator
和validation_generator
class_mode='categorical'
更改为class_mode='binary'
。
此外,删除以下行,因为您已经创建了 model。
model.layers.pop()
model = Model(input=model.input,output=model.layers[-1].output)
所以,你的 model 就像:
model = ResNet50(include_top=True, weights='imagenet')
x = model.get_layer('avg_pool').output
x = Flatten()(x)
predictions = Dense(1, activation='sigmoid')(x)
model = Model(input = model.input, output = predictions)
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