TypeError: 'retval_' has dtype int32 in the main branch, but dtype float32 in the else branch
TypeError: 'retval_' has dtype int32 in the main branch, but dtype float32 in the else branch
我正在训练我的模型来解决图像分类问题，我将 1000 张图像分类为 4 类。在训练模型时出现“类型错误”，我已经多次检查我的代码并且不知道我在代码中的哪里犯了错误，如果可能的话有人可以告诉我错误的原因，我正在发布下面生成的代码和错误供您参考
"""
from tensorflow.keras.layers import Input, Concatenate, Dropout, Flatten, Dense, GlobalAveragePooling2D, Conv2D
from tensorflow.keras import backend as K
#from tensorflow.keras.utils import np_utils
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import optimizers
from tensorflow.keras.metrics import top_k_categorical_accuracy
from tensorflow.keras.models import Sequential, Model, load_model
import tensorflow as tf
from tensorflow.keras.initializers import he_uniform
from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping, CSVLogger, ReduceLROnPlateau
#from tensorflow.compat.keras.backend import KTF
#import keras.backend.tensorflow_backend as KTF
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.inception_v3 import InceptionV3
import os
import matplotlib.pylab as plt
import numpy as np
import pandas as pd
#import numpy as np, Pillow, skimage, imageio, matplotlib
#from scipy.misc import imresize
from skimage.transform import resize
from tqdm import tqdm
# Path to class files
classes_file = "/home/DEV/Downloads/IMAGE_1000_CLASSES"
data_classes = pd.read_csv(classes_file, header=None)
# Instances with targets
targets = data_classes[1].tolist()
# Split data according to their classes
class_0 = data_classes[data_classes[1] == 0]
class_1 = data_classes[data_classes[1] == 1]
class_2 = data_classes[data_classes[1] == 2]
class_3 = data_classes[data_classes[1] == 3]
# Holdout split train/test set (Other options are k-folds or leave-one-out)
split_proportion = 0.9
split_size_0 = int(len(class_0)*split_proportion)
split_size_1 = int(len(class_1)*split_proportion)
split_size_2 = int(len(class_2)*split_proportion)
split_size_3 = int(len(class_3)*split_proportion)
new_class_0_train = np.random.choice(len(class_0), split_size_0, replace=False)
new_class_0_train = class_0.iloc[new_class_0_train]
new_class_0_test = ~class_0.iloc[:][0].isin(new_class_0_train.iloc[:][0])
new_class_0_test = class_0[new_class_0_test]
new_class_1_train = np.random.choice(len(class_1), split_size_1, replace=False)
new_class_1_train = class_1.iloc[new_class_1_train]
new_class_1_test = ~class_1.iloc[:][0].isin(new_class_1_train.iloc[:][0])
new_class_1_test = class_1[new_class_1_test]
new_class_2_train = np.random.choice(len(class_2), split_size_2, replace=False)
new_class_2_train = class_2.iloc[new_class_2_train]
new_class_2_test = ~class_2.iloc[:][0].isin(new_class_2_train.iloc[:][0])
new_class_2_test = class_2[new_class_2_test]
new_class_3_train = np.random.choice(len(class_3), split_size_3, replace=False)
new_class_3_train = class_3.iloc[new_class_3_train]
new_class_3_test = ~class_3.iloc[:][0].isin(new_class_3_train.iloc[:][0])
new_class_3_test = class_3[new_class_3_test]
x_train_list = pd.concat(
[new_class_0_train, new_class_1_train, new_class_2_train, new_class_3_train])
x_test_list = pd.concat(
[new_class_0_test, new_class_1_test, new_class_2_test, new_class_3_test])
# Load files
imagePath = "/home/DEV/Downloads/IMAGE_SET_1000/"
x_train = []
y_train = []
for index, row in tqdm(x_train_list.iterrows(), total=x_train_list.shape[0]):
try:
loadedImage = plt.imread(imagePath + str(row[0]) + ".jpg")
x_train.append(loadedImage)
y_train.append(row[1])
except:
# Try with .png file format if images are not properly loaded
try:
loadedImage = plt.imread(imagePath + str(row[0]) + ".png")
x_train.append(loadedImage)
y_train.append(row[1])
except:
# Print file names whenever it is impossible to load image files
print(imagePath + str(row[0]))
x_test = []
y_test = []
for index, row in tqdm(x_test_list.iterrows(), total=x_test_list.shape[0]):
try:
loadedImage = plt.imread(imagePath + str(row[0]) + ".jpg")
x_test.append(loadedImage)
y_test.append(row[1])
except:
# Try with .png file format if images are not properly loaded
try:
loadedImage = plt.imread(imagePath + str(row[0]) + ".png")
x_test.append(loadedImage)
y_test.append(row[1])
except:
# Print file names whenever it is impossible to load image files
print(imagePath + str(row[0]))
img_width, img_height = 139, 139
index = 0
for image in tqdm(x_train):
#aux = imresize(image, (img_width, img_height, 3), "bilinear")
aux = resize(image, (img_width, img_height))
x_train[index] = aux / 255.0 # Normalization
index += 1
index = 0
for image in tqdm(x_test):
#aux = imresize(image, (img_width, img_height, 3), "bilinear")
aux = resize(image, (img_width, img_height))
x_test[index] = aux / 255.0 # Normalization
index += 1
os.environ["KERAS_BACKEND"] = "tensorflow"
RANDOM_STATE = 42
def get_session(gpu_fraction=0.8):
num_threads = os.environ.get('OMP_NUM_THREADS')
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_fraction)
if num_threads:
return tf.Session(config=tf.ConfigProto(
gpu_options=gpu_options, intra_op_parallelism_threads=num_threads))
else:
return tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
#KTF.set_session(get_session())
#k.tensorflow.set_session(get_session())
def precision(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
def recall(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def fbeta_score(y_true, y_pred, beta=1):
if beta < 0:
raise ValueError('The lowest choosable beta is zero (only precision).')
# Set F-score as 0 if there are no true positives (sklearn-like).
if K.sum(K.round(K.clip(y_true, 0, 1))) == 0:
return 0
p = precision(y_true, y_pred)
r = recall(y_true, y_pred)
bb = beta ** 2
fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon())
return fbeta_score
nb_classes = 4
final_model = []
# Option = InceptionV3
model = InceptionV3(weights="imagenet", include_top=False,
input_shape=(img_width, img_height, 3), classifier_activation="softmax")
model.summary()
# Creating new outputs for the model
x = model.output
x = Flatten()(x)
#x = GlobalAveragePooling2D()(x)
x = Dense(512, activation="relu")(x)
x = Dropout(0.5)(x)
x = Dense(512, activation="relu")(x)
x = Dropout(0.5)(x)
predictions = Dense(nb_classes, activation='softmax')(x)
final_model = Model(inputs=model.input, outputs=predictions)
# Metrics
learningRate = 0.001
#optimizer = model.compile(optimizer= 'adam' , loss= tensorflow.keras.losses.binary_crossentropy, metrics=['accuracy'])
optimizer = tf.keras.optimizers.SGD(learning_rate=learningRate, momentum=0.88, nesterov=True)
#optimizer = tf.keras.Adam(learning_rate=learningRate, momentum=0.88, nesterov=True)
# Compiling the model...
final_model.compile(loss="categorical_crossentropy", optimizer=optimizer,
metrics=["accuracy", fbeta_score])
#x_train = np.array(x_train)
x_train = np.asarray(x_train).astype(np.float32)
#x_test = np.array(x_test)
x_test = np.asarray(x_test).astype(np.float32)
# Defining targets...
y_train = np.concatenate([np.full((new_class_0_train.shape[0]), 0), np.full((new_class_1_train.shape[0]), 1),
np.full((new_class_2_train.shape[0]), 2), np.full((new_class_3_train.shape[0]), 3)])
y_test = np.concatenate([np.full((new_class_0_test.shape[0]), 0), np.full((new_class_1_test.shape[0]), 1),
np.full((new_class_2_test.shape[0]), 2), np.full((new_class_3_test.shape[0]), 3)])
#y_train = np_utils.to_categorical(y_train)
y_train = tf.keras.utils.to_categorical(y_train)
#y_test = np_utils.to_categorical(y_test)
y_test = tf.keras.utils.to_categorical(y_test)
modelFilename = "./model_inception.h5"
trainingFilename = "./training.csv"
nb_train_samples = y_train.shape[0]
nb_test_samples = y_test.shape[0]
#epochs = 10000
epochs = 1000
batch_size = 24
trainingPatience = 200
decayPatience = trainingPatience / 4
# Setting the data generator...
train_datagen = ImageDataGenerator(
horizontal_flip=True,
fill_mode="reflect",
zoom_range=0.2
)
train_generator = train_datagen.flow(x_train, y_train, batch_size=batch_size)
# Saving the model
checkpoint = ModelCheckpoint(modelFilename,
monitor='val_acc',
verbose=1,
save_best_only=True,
save_weights_only=False,
mode='auto',
save_freq=1)
adaptativeLearningRate = ReduceLROnPlateau(monitor='val_acc',
factor=0.5,
patience=decayPatience,
verbose=1,
mode='auto',
min_delta=0.0001,
cooldown=0,
min_lr=1e-8)
early = EarlyStopping(monitor='val_acc',
min_delta=0,
patience=trainingPatience,
verbose=1,
mode='auto')
csv_logger = CSVLogger(trainingFilename, separator=",", append=False)
# Callbacks
callbacks = [checkpoint, early, csv_logger, adaptativeLearningRate]
# Training of the model
final_model.fit(train_generator,
steps_per_epoch=nb_train_samples / batch_size,
epochs=epochs,
shuffle=True,
validation_data=(x_test, y_test),
validation_steps=nb_test_samples / batch_size,
callbacks=callbacks)
错误：
File "/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 986, in wrapper
raise e.ag_error_metadata.to_exception(e)
TypeError: in user code:
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:855 train_function *
return step_function(self, iterator)
/home/DEV/Downloads/codes/Classification_model.py:212 fbeta_score *
if K.sum(K.round(K.clip(y_true, 0, 1))) == 0:
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:1170 if_stmt
_tf_if_stmt(cond, body, orelse, get_state, set_state, symbol_names, nouts)
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:1216 _tf_if_stmt
final_cond_vars = control_flow_ops.cond(
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/util/deprecation.py:535 new_func
return func(*args, **kwargs)
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/control_flow_ops.py:1254 cond
return cond_v2.cond_v2(pred, true_fn, false_fn, name)
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/cond_v2.py:90 cond_v2
false_graph = func_graph_module.func_graph_from_py_func(
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:999 func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:1213 aug_orelse
_verify_tf_cond_vars(new_body_vars_[0], new_orelse_vars, symbol_names)
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:365 _verify_tf_cond_vars
nest.map_structure(
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/util/nest.py:867 map_structure
structure[0], [func(*x) for x in entries],
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/util/nest.py:867 <listcomp>
structure[0], [func(*x) for x in entries],
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:335 verify_single_cond_var
raise TypeError(
TypeError: 'retval_' has dtype int32 in the main branch, but dtype float32 in the else branch
如错误所示，您在主分支中返回了 int32 (return 0)，但在其他分支中返回了 float32 (return fbeta_score)，这发生在fbeta_score 函数。
因此，更改 return 0 => return 0.0。
我正在训练我的模型来解决图像分类问题，我将 1000 张图像分类为 4 类。在训练模型时出现“类型错误”，我已经多次检查我的代码并且不知道我在代码中的哪里犯了错误，如果可能的话有人可以告诉我错误的原因，我正在发布下面生成的代码和错误供您参考 """
from tensorflow.keras.layers import Input, Concatenate, Dropout, Flatten, Dense, GlobalAveragePooling2D, Conv2D
from tensorflow.keras import backend as K
#from tensorflow.keras.utils import np_utils
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import optimizers
from tensorflow.keras.metrics import top_k_categorical_accuracy
from tensorflow.keras.models import Sequential, Model, load_model
import tensorflow as tf
from tensorflow.keras.initializers import he_uniform
from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping, CSVLogger, ReduceLROnPlateau
#from tensorflow.compat.keras.backend import KTF
#import keras.backend.tensorflow_backend as KTF
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.inception_v3 import InceptionV3
import os
import matplotlib.pylab as plt
import numpy as np
import pandas as pd
#import numpy as np, Pillow, skimage, imageio, matplotlib
#from scipy.misc import imresize
from skimage.transform import resize
from tqdm import tqdm
# Path to class files
classes_file = "/home/DEV/Downloads/IMAGE_1000_CLASSES"
data_classes = pd.read_csv(classes_file, header=None)
# Instances with targets
targets = data_classes[1].tolist()
# Split data according to their classes
class_0 = data_classes[data_classes[1] == 0]
class_1 = data_classes[data_classes[1] == 1]
class_2 = data_classes[data_classes[1] == 2]
class_3 = data_classes[data_classes[1] == 3]
# Holdout split train/test set (Other options are k-folds or leave-one-out)
split_proportion = 0.9
split_size_0 = int(len(class_0)*split_proportion)
split_size_1 = int(len(class_1)*split_proportion)
split_size_2 = int(len(class_2)*split_proportion)
split_size_3 = int(len(class_3)*split_proportion)
new_class_0_train = np.random.choice(len(class_0), split_size_0, replace=False)
new_class_0_train = class_0.iloc[new_class_0_train]
new_class_0_test = ~class_0.iloc[:][0].isin(new_class_0_train.iloc[:][0])
new_class_0_test = class_0[new_class_0_test]
new_class_1_train = np.random.choice(len(class_1), split_size_1, replace=False)
new_class_1_train = class_1.iloc[new_class_1_train]
new_class_1_test = ~class_1.iloc[:][0].isin(new_class_1_train.iloc[:][0])
new_class_1_test = class_1[new_class_1_test]
new_class_2_train = np.random.choice(len(class_2), split_size_2, replace=False)
new_class_2_train = class_2.iloc[new_class_2_train]
new_class_2_test = ~class_2.iloc[:][0].isin(new_class_2_train.iloc[:][0])
new_class_2_test = class_2[new_class_2_test]
new_class_3_train = np.random.choice(len(class_3), split_size_3, replace=False)
new_class_3_train = class_3.iloc[new_class_3_train]
new_class_3_test = ~class_3.iloc[:][0].isin(new_class_3_train.iloc[:][0])
new_class_3_test = class_3[new_class_3_test]
x_train_list = pd.concat(
[new_class_0_train, new_class_1_train, new_class_2_train, new_class_3_train])
x_test_list = pd.concat(
[new_class_0_test, new_class_1_test, new_class_2_test, new_class_3_test])
# Load files
imagePath = "/home/DEV/Downloads/IMAGE_SET_1000/"
x_train = []
y_train = []
for index, row in tqdm(x_train_list.iterrows(), total=x_train_list.shape[0]):
try:
loadedImage = plt.imread(imagePath + str(row[0]) + ".jpg")
x_train.append(loadedImage)
y_train.append(row[1])
except:
# Try with .png file format if images are not properly loaded
try:
loadedImage = plt.imread(imagePath + str(row[0]) + ".png")
x_train.append(loadedImage)
y_train.append(row[1])
except:
# Print file names whenever it is impossible to load image files
print(imagePath + str(row[0]))
x_test = []
y_test = []
for index, row in tqdm(x_test_list.iterrows(), total=x_test_list.shape[0]):
try:
loadedImage = plt.imread(imagePath + str(row[0]) + ".jpg")
x_test.append(loadedImage)
y_test.append(row[1])
except:
# Try with .png file format if images are not properly loaded
try:
loadedImage = plt.imread(imagePath + str(row[0]) + ".png")
x_test.append(loadedImage)
y_test.append(row[1])
except:
# Print file names whenever it is impossible to load image files
print(imagePath + str(row[0]))
img_width, img_height = 139, 139
index = 0
for image in tqdm(x_train):
#aux = imresize(image, (img_width, img_height, 3), "bilinear")
aux = resize(image, (img_width, img_height))
x_train[index] = aux / 255.0 # Normalization
index += 1
index = 0
for image in tqdm(x_test):
#aux = imresize(image, (img_width, img_height, 3), "bilinear")
aux = resize(image, (img_width, img_height))
x_test[index] = aux / 255.0 # Normalization
index += 1
os.environ["KERAS_BACKEND"] = "tensorflow"
RANDOM_STATE = 42
def get_session(gpu_fraction=0.8):
num_threads = os.environ.get('OMP_NUM_THREADS')
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_fraction)
if num_threads:
return tf.Session(config=tf.ConfigProto(
gpu_options=gpu_options, intra_op_parallelism_threads=num_threads))
else:
return tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
#KTF.set_session(get_session())
#k.tensorflow.set_session(get_session())
def precision(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
def recall(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def fbeta_score(y_true, y_pred, beta=1):
if beta < 0:
raise ValueError('The lowest choosable beta is zero (only precision).')
# Set F-score as 0 if there are no true positives (sklearn-like).
if K.sum(K.round(K.clip(y_true, 0, 1))) == 0:
return 0
p = precision(y_true, y_pred)
r = recall(y_true, y_pred)
bb = beta ** 2
fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon())
return fbeta_score
nb_classes = 4
final_model = []
# Option = InceptionV3
model = InceptionV3(weights="imagenet", include_top=False,
input_shape=(img_width, img_height, 3), classifier_activation="softmax")
model.summary()
# Creating new outputs for the model
x = model.output
x = Flatten()(x)
#x = GlobalAveragePooling2D()(x)
x = Dense(512, activation="relu")(x)
x = Dropout(0.5)(x)
x = Dense(512, activation="relu")(x)
x = Dropout(0.5)(x)
predictions = Dense(nb_classes, activation='softmax')(x)
final_model = Model(inputs=model.input, outputs=predictions)
# Metrics
learningRate = 0.001
#optimizer = model.compile(optimizer= 'adam' , loss= tensorflow.keras.losses.binary_crossentropy, metrics=['accuracy'])
optimizer = tf.keras.optimizers.SGD(learning_rate=learningRate, momentum=0.88, nesterov=True)
#optimizer = tf.keras.Adam(learning_rate=learningRate, momentum=0.88, nesterov=True)
# Compiling the model...
final_model.compile(loss="categorical_crossentropy", optimizer=optimizer,
metrics=["accuracy", fbeta_score])
#x_train = np.array(x_train)
x_train = np.asarray(x_train).astype(np.float32)
#x_test = np.array(x_test)
x_test = np.asarray(x_test).astype(np.float32)
# Defining targets...
y_train = np.concatenate([np.full((new_class_0_train.shape[0]), 0), np.full((new_class_1_train.shape[0]), 1),
np.full((new_class_2_train.shape[0]), 2), np.full((new_class_3_train.shape[0]), 3)])
y_test = np.concatenate([np.full((new_class_0_test.shape[0]), 0), np.full((new_class_1_test.shape[0]), 1),
np.full((new_class_2_test.shape[0]), 2), np.full((new_class_3_test.shape[0]), 3)])
#y_train = np_utils.to_categorical(y_train)
y_train = tf.keras.utils.to_categorical(y_train)
#y_test = np_utils.to_categorical(y_test)
y_test = tf.keras.utils.to_categorical(y_test)
modelFilename = "./model_inception.h5"
trainingFilename = "./training.csv"
nb_train_samples = y_train.shape[0]
nb_test_samples = y_test.shape[0]
#epochs = 10000
epochs = 1000
batch_size = 24
trainingPatience = 200
decayPatience = trainingPatience / 4
# Setting the data generator...
train_datagen = ImageDataGenerator(
horizontal_flip=True,
fill_mode="reflect",
zoom_range=0.2
)
train_generator = train_datagen.flow(x_train, y_train, batch_size=batch_size)
# Saving the model
checkpoint = ModelCheckpoint(modelFilename,
monitor='val_acc',
verbose=1,
save_best_only=True,
save_weights_only=False,
mode='auto',
save_freq=1)
adaptativeLearningRate = ReduceLROnPlateau(monitor='val_acc',
factor=0.5,
patience=decayPatience,
verbose=1,
mode='auto',
min_delta=0.0001,
cooldown=0,
min_lr=1e-8)
early = EarlyStopping(monitor='val_acc',
min_delta=0,
patience=trainingPatience,
verbose=1,
mode='auto')
csv_logger = CSVLogger(trainingFilename, separator=",", append=False)
# Callbacks
callbacks = [checkpoint, early, csv_logger, adaptativeLearningRate]
# Training of the model
final_model.fit(train_generator,
steps_per_epoch=nb_train_samples / batch_size,
epochs=epochs,
shuffle=True,
validation_data=(x_test, y_test),
validation_steps=nb_test_samples / batch_size,
callbacks=callbacks)
错误：
File "/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 986, in wrapper
raise e.ag_error_metadata.to_exception(e)
TypeError: in user code:
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:855 train_function *
return step_function(self, iterator)
/home/DEV/Downloads/codes/Classification_model.py:212 fbeta_score *
if K.sum(K.round(K.clip(y_true, 0, 1))) == 0:
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:1170 if_stmt
_tf_if_stmt(cond, body, orelse, get_state, set_state, symbol_names, nouts)
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:1216 _tf_if_stmt
final_cond_vars = control_flow_ops.cond(
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/util/deprecation.py:535 new_func
return func(*args, **kwargs)
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/control_flow_ops.py:1254 cond
return cond_v2.cond_v2(pred, true_fn, false_fn, name)
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/cond_v2.py:90 cond_v2
false_graph = func_graph_module.func_graph_from_py_func(
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:999 func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:1213 aug_orelse
_verify_tf_cond_vars(new_body_vars_[0], new_orelse_vars, symbol_names)
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:365 _verify_tf_cond_vars
nest.map_structure(
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/util/nest.py:867 map_structure
structure[0], [func(*x) for x in entries],
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/util/nest.py:867 <listcomp>
structure[0], [func(*x) for x in entries],
/home/DEV/anaconda3/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:335 verify_single_cond_var
raise TypeError(
TypeError: 'retval_' has dtype int32 in the main branch, but dtype float32 in the else branch
如错误所示，您在主分支中返回了 int32 (return 0)，但在其他分支中返回了 float32 (return fbeta_score)，这发生在fbeta_score 函数。
因此，更改 return 0 => return 0.0。