ロンググエン：
とさらに推論のための私の訓練を受けKerasモデルをロードしようとしているとき
self.model = tf.keras.models.load_model(filepath=weight_url, custom_objects={
'dice_coef': dice_coef,
'iou_coef': iou_coef,
'bce_dice_coef': bce_dice_coef,
})
私は次のエラーを取得します。これらの3つの機能が私のモデルを訓練するために使用されるのと同じIあり、重量URLが正しいです。エラーメッセージは、私には何も教えてくれありません。
file "/home/long/Desktop/bachelor-thesis/unet/pipeline.py", line 344, in <module>
binary.inference_blood_cell_unet()
File "/home/long/Desktop/bachelor-thesis/unet/pipeline.py", line 305, in inference_blood_cell_unet
self.blood_cell_unet = UNet(weight_url=self.blood_cell_final_name, class_weights=self.blut_koerperchen_classweights, num_classes=2)
File "/home/long/Desktop/bachelor-thesis/unet/model.py", line 39, in __init__
'bce_dice_coef': bce_dice_coef,
File "/home/long/Desktop/bachelor-thesis/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/save.py", line 146, in load_model
return hdf5_format.load_model_from_hdf5(filepath, custom_objects, compile)
File "/home/long/Desktop/bachelor-thesis/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/hdf5_format.py", line 193, in load_model_from_hdf5
model._make_train_function()
File "/home/long/Desktop/bachelor-thesis/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py", line 2116, in _make_train_function
params=self._collected_trainable_weights, loss=self.total_loss)
File "/home/long/Desktop/bachelor-thesis/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/optimizer_v2/optimizer_v2.py", line 500, in get_updates
grads = self.get_gradients(loss, params)
File "/home/long/Desktop/bachelor-thesis/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/optimizer_v2/optimizer_v2.py", line 391, in get_gradients
grads = gradients.gradients(loss, params)
File "/home/long/Desktop/bachelor-thesis/venv/lib/python3.7/site-packages/tensorflow_core/python/ops/gradients_impl.py", line 158, in gradients
unconnected_gradients)
File "/home/long/Desktop/bachelor-thesis/venv/lib/python3.7/site-packages/tensorflow_core/python/ops/gradients_util.py", line 550, in _GradientsHelper
gradient_uid)
File "/home/long/Desktop/bachelor-thesis/venv/lib/python3.7/site-packages/tensorflow_core/python/ops/gradients_util.py", line 166, in _DefaultGradYs
with _maybe_colocate_with(y.op, gradient_uid, colocate_gradients_with_ops):
AttributeError: 'NoneType' object has no attribute 'op'
以下のモデルが構築されてどのように私のコードは次のようになります。
import tensorflow as tf
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, LeakyReLU, Dropout, MaxPooling2D, Conv2DTranspose
from tensorflow.keras.layers import concatenate
from tensorflow.keras.optimizers import Adam
from unet.metrics import dice_coef, iou_coef, cce_dice_coef, weighted_loss, bce_dice_coef
fil_coef = 4
class UNet(UNetBase, DataHandlingBase):
def __init__(self,
class_weights,
weight_url=None,
width=256,
height=256,
channel=3,
learning_rate=0.0005,
num_classes=2,
alpha=0.01,
dropout_rate=0.25):
super().__init__(weight_url, width, height, channel, learning_rate, num_classes, alpha, dropout_rate)
self.class_weights = class_weights
if self.num_classes > 2:
self.loss_function = weighted_loss(cce_dice_coef, weights_list=self.class_weights)
elif self.num_classes == 2:
self.loss_function = bce_dice_coef
else:
raise Exception("Invalid num class: ", self.num_classes)
if weight_url:
self.model = tf.keras.models.load_model(filepath=weight_url, custom_objects={
'dice_coef': dice_coef,
'iou_coef': iou_coef,
'bce_dice_coef': bce_dice_coef,
'loss_function': self.loss_function
})
else:
model_input = Input((self.height, self.width, self.channel))
c1 = Conv2D(filters=16 * fil_coef, kernel_size=(3, 3), padding='same')(model_input)
c1 = BatchNormalization()(c1)
c1 = LeakyReLU(self.alpha)(c1)
c1 = Dropout(self.dropout_rate)(c1)
c1 = Conv2D(filters=16 * fil_coef, kernel_size=(3, 3), padding='same')(c1)
...
c9 = BatchNormalization()(c9)
c9 = LeakyReLU(self.alpha)(c9)
c9 = Dropout(self.dropout_rate)(c9)
if self.num_classes == 2:
output = Conv2D(1, kernel_size=(1, 1), activation='sigmoid')(c9)
else:
output = Conv2D(self.num_classes, kernel_size=(1, 1), activation='softmax')(c9)
self.model = tf.keras.Model(inputs=[model_input], outputs=[output])
self.reinitialize()
def reinitialize(self):
if self.num_classes > 2:
self.model.compile(optimizer=Adam(lr=self.learning_rate),
loss=self.loss_function,
metrics=[dice_coef, iou_coef])
else:
self.model.compile(optimizer=Adam(lr=self.learning_rate),
loss=self.loss_function,
metrics=[dice_coef, iou_coef],
class_weight=self.class_weights)
def fit(self, X, Y, x=None, y=None,
batch_size=params["batch_size"],
epochs=params["epochs"],
validation_split=params["validation_split"],
checkpoint_path="temp/models/model-{epoch:02d}-{val_loss:.2f}.h5",
patience=params["patience"],
min_delta=params["min_delta"]):
checkpoint = ModelCheckpoint(checkpoint_path,
verbose=0,
save_best_only=False,
monitor='val_loss',
mode='auto')
if x is None and y is None:
history = self.model.fit(X, Y,
validation_split=validation_split,
batch_size=batch_size,
epochs=epochs,
shuffle=True,
callbacks=[checkpoint])
return history
elif x is not None and y is not None:
history = self.model.fit(X, Y,
verbose=0,
steps_per_epoch=X.shape[0] // batch_size,
validation_data=[x, y],
validation_steps=x.shape[0] // batch_size,
batch_size=batch_size,
epochs=epochs,
shuffle=True)
return history
def save_weights(self, url):
self.model.save(url)
def predict(self, X):
return self.model.predict(X)
def summary(self):
log.info(f"Learning rate {self.learning_rate} Alpha {self.alpha} Dropout Rate {self.dropout_rate}")
self.model.summary()
そして、ここに私の損失関数がコード化された方法を、次のとおりです。
import tensorflow as tf
import tensorflow.keras.backend as K
def weighted_loss(original_loss_function, weights_list):
"""
Author: https://stackoverflow.com/questions/51793737/custom-loss-function-for-u-net-in-keras-using-class-weights-class-weight-not
"""
def loss_function(true, pred):
axis = -1 # if channels last
# axis= 1 #if channels first
# argmax returns the index of the element with the greatest value
# done in the class axis, it returns the class index
class_selectors = tf.cast(K.argmax(true, axis=axis), tf.int32)
# if your loss is sparse, use only true as classSelectors
# considering weights are ordered by class, for each class
# true(1) if the class index is equal to the weight index
class_selectors = [K.equal(i, class_selectors) for i in range(len(weights_list))]
# casting boolean to float for calculations
# each tensor in the list contains 1 where ground true class is equal to its index
# if you sum all these, you will get a tensor full of ones.
class_selectors = [K.cast(x, K.floatx()) for x in class_selectors]
# for each of the selections above, multiply their respective weight
weights = [sel * w for sel, w in zip(class_selectors, weights_list)]
# sums all the selections
# result is a tensor with the respective weight for each element in predictions
weight_multiplier = weights[0]
for i in range(1, len(weights)):
weight_multiplier = weight_multiplier + weights[i]
# make sure your originalLossFunc only collapses the class axis
# you need the other axes intact to multiply the weights tensor
loss = original_loss_function(true, pred)
loss = loss * weight_multiplier
return loss
return loss_function
@tf.function
def cce_iou_coef(y_true, y_pred, smooth=1, cat_weight=1, iou_weight=1):
return cat_weight * K.categorical_crossentropy(y_true, y_pred) + iou_weight * log_iou_coef(y_true, y_pred, smooth)
@tf.function
def cce_dice_coef(y_true, y_pred, smooth=1, cat_weight=1, dice_weight=1):
return cat_weight * K.categorical_crossentropy(y_true, y_pred) + dice_weight * log_dice_coef(y_true, y_pred, smooth)
@tf.function
def bce_iou_coef(y_true, y_pred, smooth=1, cat_weight=1, iou_weight=1):
return cat_weight * K.binary_crossentropy(y_true, y_pred) + iou_weight * log_iou_coef(y_true, y_pred, smooth)
@tf.function
def bce_dice_coef(y_true, y_pred, smooth=1, cat_weight=1, dice_weight=1):
return cat_weight * K.binary_crossentropy(y_true, y_pred) + dice_weight * log_dice_coef(y_true, y_pred, smooth)
@tf.function
def log_iou_coef(y_true, y_pred, smooth=1):
return - K.log(iou_coef(y_true, y_pred, smooth))
@tf.function
def log_dice_coef(y_true, y_pred, smooth=1):
return -K.log(dice_coef(y_true, y_pred, smooth))
@tf.function
def iou_coef(y_true, y_pred, smooth=1):
intersection = K.sum(K.abs(y_true * y_pred), axis=-1)
union = K.sum(y_true, axis=-1) + K.sum(y_pred, axis=-1) - intersection
iou = K.mean((intersection + smooth) / (union + smooth), axis=0)
return iou
@tf.function
def dice_coef(y_true, y_pred, smooth=1):
intersection = K.sum(y_true * y_pred, axis=-1)
union = K.sum(y_true, axis=-1) + K.sum(y_pred, axis=-1)
dice = K.mean((2. * intersection + smooth) / (union + smooth), axis=0)
return dice
私は、バイナリモデルを訓練された推論に再びそれをロードして保存した後にエラーが発生します。あなたが見ることができるように推論にインポート損失関数は、トレーニングフェーズで同じです。私はエラーを取得する理由だから私は本当に理解していません。そして、私はどちらか、どのようなエラー手段を理解していません。
Sjngohil：
あなたが試すことができますcompile = Falseあなたのload_model呼び出しでパラメータ。これは、オプティマイザと損失関数に関連するメタデータを削除し、あなたが再初期化機能を持っているので、あなたが最後の時間を停止したところからそれを訓練する必要がない場合、それは私が推測する問題になることはありません。
あなたはそれがコンパイル= Falseのと連携言ったように、私は問題はあなたの損失のためのカスタム関数/オプティマイザなどであるかもしれないと思うが、私は正確に問題があるものではないピンポイント、あなたはTFを試すことができます。代わりにtf.keras.backendの。お望みならば。