python - 图像分类器错误该层的所有输入都应该是张量

Question

嗨，我一直在制作图像分类器，这些源代码来自我的大学，但是当我使用这些代码时，我不断收到错误，源代码是用于多图像分类器的，我只是将分类更改为二进制，我认为我做错了，但 idk什么

import numpy as np
import keras
from keras.layers import Dense,GlobalAveragePooling2D
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model
from sklearn.metrics import confusion_matrix
import itertools
import matplotlib.pyplot as plt

train_path=r'C:\Users\Acer\imagerec\Brain\TRAIN'
valid_path=r'C:\Users\Acer\imagerec\Brain\VAL'
test_path=r'C:\Users\Acer\imagerec\Brain\TEST'

class_labels=['yes', 'no']

train_batches=ImageDataGenerator(preprocessing_function=keras.applications.xception.preprocess_input)\
    .flow_from_directory(train_path, target_size=(299,299),classes=class_labels,batch_size=5)
valid_batches=ImageDataGenerator(preprocessing_function=keras.applications.xception.preprocess_input)\
    .flow_from_directory(train_path, target_size=(299,299),classes=class_labels,batch_size=5)
test_batches=ImageDataGenerator(preprocessing_function=keras.applications.xception.preprocess_input)\
    .flow_from_directory(train_path, target_size=(299,299),classes=class_labels,batch_size=5, shuffle=False)

base_model=keras.applications.xception.Xception(include_top=False)

x=base_model.output
x=GlobalAveragePooling2D
x=Dense(1,activation='sigmoid')(x)
predictions=Dense(2,activation='Adam')(x)

for layer in base_model.layers:
    layer.trainable=False

    model=Model(inputs=base_model.input,outputs=predictions)

    model.summary()

N=30

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

history=model.fit_generator(train_batches, steps_per_epoch=412,
                            validation_data=valid_batches,
                            validation_steps=35,epochs=N,verbose=1)

我得到这个错误

2019-12-09 13:43:11.107461: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
Traceback (most recent call last):
  File "C:\Users\Acer\Anaconda3\envs\condas\lib\site-packages\keras\engine\base_layer.py", line 310, in assert_input_compatibility
    K.is_keras_tensor(x)
  File "C:\Users\Acer\Anaconda3\envs\condas\lib\site-packages\keras\backend\tensorflow_backend.py", line 697, in is_keras_tensor
    str(type(x)) + '`. '
ValueError: Unexpectedly found an instance of type `<class 'type'>`. Expected a symbolic tensor instance.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:/Users/Acer/PycharmProjects/condas/nyah.py", line 28, in <module>
    x=Dense(1,activation='sigmoid')(x)
  File "C:\Users\Acer\Anaconda3\envs\condas\lib\site-packages\keras\backend\tensorflow_backend.py", line 75, in symbolic_fn_wrapper
    return func(*args, **kwargs)
  File "C:\Users\Acer\Anaconda3\envs\condas\lib\site-packages\keras\engine\base_layer.py", line 446, in __call__
    self.assert_input_compatibility(inputs)
  File "C:\Users\Acer\Anaconda3\envs\condas\lib\site-packages\keras\engine\base_layer.py", line 316, in assert_input_compatibility
    str(inputs) + '. All inputs to the layer '
ValueError: Layer dense_1 was called with an input that isn't a symbolic tensor. Received type: <class 'type'>. Full input: [<class 'keras.layers.pooling.GlobalAveragePooling2D'>]. All inputs to the layer should be tensors.

标签： pythonpycharm

Answer 1

几件事。
1) 对于二元分类，使用带损失的sigmoid激活。 2)不是激活函数。它是一个优化器。 3）您正在尝试在循环中定义模型。binary_crossentropy
adam
for

更多信息，请参考 Keras 文档：https ://keras.io/

修改后的代码：

import numpy as np
import keras
from keras.layers import Dense,GlobalAveragePooling2D
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model
from sklearn.metrics import confusion_matrix
import itertools
import matplotlib.pyplot as plt

train_path=r'C:\Users\Acer\imagerec\Brain\TRAIN'
valid_path=r'C:\Users\Acer\imagerec\Brain\VAL'
test_path=r'C:\Users\Acer\imagerec\Brain\TEST'

class_labels=['yes', 'no']

train_batches=ImageDataGenerator(preprocessing_function=keras.applications.xception.preprocess_input)\
    .flow_from_directory(train_path, target_size=(299,299),classes=class_labels,batch_size=5)
valid_batches=ImageDataGenerator(preprocessing_function=keras.applications.xception.preprocess_input)\
    .flow_from_directory(train_path, target_size=(299,299),classes=class_labels,batch_size=5)
test_batches=ImageDataGenerator(preprocessing_function=keras.applications.xception.preprocess_input)\
    .flow_from_directory(train_path, target_size=(299,299),classes=class_labels,batch_size=5, shuffle=False)

base_model=keras.applications.xception.Xception(include_top=False, input_shape=(299,299,3))

x=base_model.output
x=GlobalAveragePooling2D()(x)
x=Dense(1, activation='sigmoid')(x)
model=Model(inputs=base_model.input, outputs=x)


for layer in base_model.layers:
    layer.trainable=False

N=30

model.compile(loss='binary_crossentropy',
            optimizer='rmsprop',
            metrics=['accuracy'])

history=model.fit_generator(train_batches, steps_per_epoch=412,
                            validation_data=valid_batches,
                            validation_steps=35,epochs=N,verbose=1)