Question

使用Keras训练CNN，即使我做了model.compile，keras. fit_generator也会引发运行时错误，提示您在使用fit之前要编译我的模型。

Error: 
Using TensorFlow backend.
WARNING:tensorflow:From C:\Users\..\Desktop\venvpy36\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
Found 468 images belonging to 2 classes.
Found 86 images belonging to 2 classes.
Traceback (most recent call last):
  File "C:/Users/../Desktop/miscfiles/template_classifier_cnn.py", line 75, in <module>
    model.fit_generator(train_generator)
  File "C:\Users\..\Desktop\venvpy36\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "C:\Users\..\Desktop\venvpy36\lib\site-packages\keras\engine\training.py", line 1418, in fit_generator
    initial_epoch=initial_epoch)
  File "C:\Users\..\Desktop\venvpy36\lib\site-packages\keras\engine\training_generator.py", line 40, in fit_generator
    model._make_train_function()
  File "C:\Users\..\Desktop\venvpy36\lib\site-packages\keras\engine\training.py", line 496, in _make_train_function
    raise RuntimeError('You must compile your model before using it.')
RuntimeError: You must compile your model before using it.

尝试了不同的优化程序，造成了损失。尝试构建没有功能的模型。

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D,BatchNormalization
from keras.optimizers import Adam

import numpy as np
np.random.seed(1000)


train_datagen = ImageDataGenerator(
    rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=False
)


test_datagen = ImageDataGenerator(rescale=1./255)

def build_model():
    model = Sequential()
    model.add(Conv2D(32, (3, 3), padding='same'))
    model.add(Activation('relu'))
    model.add(Conv2D(32, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(BatchNormalization())
    model.add(Conv2D(64, (3, 3), padding='same'))
    model.add(Activation('relu'))
    model.add(Conv2D(64, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(2))
    model.add(Activation('softmax'))


    model.compile(loss='categorical_crossentropy',
                  optimizer=Adam(0.001),
                  metrics=['accuracy'])
    return model

model = build_model()


train_generator = train_datagen.flow_from_directory(
        'data/images/template/cnn_train',
        target_size=(256,256),
        batch_size=32,
        class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
        'data/images/template/cnn_validate',
        target_size=(256,256),
        batch_size=32,
        class_mode='binary')
#model.summary()

model.fit_generator(train_generator)

Answer 1

您必须为模型分配输入形状，我想这就是它所缺少的。因为在您的model.add(Conv2D(32, (3, 3), padding='same'))模型中，您尚未分配输入。

在代码input_shape中，必须为第一层分配Report.belongsTo(Branch); Branch.belongsTo(Client);。

Model.fit_generator抛出运行时错误：

1 个答案: