## Step 1) Import Libraries
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICE"] = ""
from tensorflow.python.client import device_lib;
print(device_lib.list_local_devices());
import keras
import numpy as np
from keras.datasets import cifar10
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
#### Set some parameters
batch_size = 256
num_classes = 10
epochs = 3
## Step 2) Load and Prepare data|
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print('y_train shape:', y_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
### Prepare data
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
import matplotlib.pyplot as plt
%matplotlib inline
plt.figure()
fig_size = [20, 20]
plt.rcParams["figure.figsize"] = fig_size
idx = 0
for idx in range(1,101):
ax = plt.subplot(10, 10, idx)
img = x_train[idx,:,:,:]
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.imshow(img, cmap="Greys_r")
plt.show()
#### Preprocess class lables
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
#### Data Augmentation
datagen = ImageDataGenerator(
featurewise_center = False, # set input mean to over the dataset
samplewise_center = False, # set each sample mean to 0
featurewise_std_normalization = False, # divide inputs of the dataset
samplewise_std_normalization = False, # divide each input by its std
zca_whitening = False, # apply ZCA whitening
rotation_range = 0, # randomply rotate images in the range (degrees, 0 to 180)
width_shift_range = 0.1, # randomly shift images horiznontally (fraction of totla width)
height_shift_range = 0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip = True, # randomly flip images
vertical_flip = False) # randomly flip images
## Setp 3) Define model architecture
model = Sequential()
model.add(Conv2D(32, (3,3), padding = 'same', activation = 'relu', input_shape = x_train.shape[1:]))
model.add(Conv2D(32, (3,3), activation = 'relu',))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3,3), padding = 'same', activation = 'relu',))
model.add(Conv2D(64, (3,3), activation = 'relu',))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation = 'relu',))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation = 'softmax'))
## Step 4) Compile model
# model.compile(loss='categorical_crossentropy', optimizer = 'Adagrad', metrics = ['accuracy'])
model.compile(loss='categorical_crossentropy', optimizer = 'Adam', metrics = ['accuracy'])
#### Summary of model
model.summary()
## Step 5) Fit model on training data
# Compute quantities required for feature-wise normalization
# (std, mean, and principal components if ZCA whitening is applied).
datagen.fit(x_train)
# Fit the model on the batches generated by datagen.flow().
model.fit_generator(datagen.flow(x_train, y_train, batch_size = batch_size),
steps_per_epoch = x_train.shape[0] // batch_size, # // is the quotient without remainder
epochs = epochs,
validation_data = (x_test, y_test))
model.save('my_model')
我有错误
<小时 />AttributeError Traceback (most recent call last)
<ipython-input-18-b71b28267016> in <module>
1 from tensorflow.keras.models import load_model
----> 2 model.save('my_model')
~anaconda3envstflibsite-packageskerasenginenetwork.py in save(self, filepath, overwrite, include_optimizer)
1150 raise NotImplementedError
1151 from ..models import save_model
-> 1152 save_model(self, filepath, overwrite, include_optimizer)
1153
1154 @saving.allow_write_to_gcs
~anaconda3envstflibsite-packageskerasenginesaving.py in save_wrapper(obj, filepath, overwrite, *args, **kwargs)
447 os.remove(tmp_filepath)
448 else:
--> 449 save_function(obj, filepath, overwrite, *args, **kwargs)
450
451 return save_wrapper
~anaconda3envstflibsite-packageskerasenginesaving.py in save_model(model, filepath, overwrite, include_optimizer)
539 return
540 with H5Dict(filepath, mode='w') as h5dict:
--> 541 _serialize_model(model, h5dict, include_optimizer)
542 elif hasattr(filepath, 'write') and callable(filepath.write):
543 # write as binary stream
~anaconda3envstflibsite-packageskerasenginesaving.py in _serialize_model(model, h5dict, include_optimizer)
161 layer_group[name] = val
162 if include_optimizer and model.optimizer:
--> 163 if isinstance(model.optimizer, optimizers.TFOptimizer):
164 warnings.warn(
165 'TensorFlow optimizers do not '
AttributeError: module 'keras.optimizers' has no attribute 'TFOptimizer'
我训练了一个顺序模型，并尝试通过代码模型保存它。 但它说 AttributeError：模块 'keras.optimizers' 没有属性 'TFOptimizer'
该错误出现在 model.save(路径( 上。 我不知道为什么我无法保存顺序模型。 即使我直接从keras.optimizers导入TFOptimizer，也会出现该错误 我还安装了H
我遇到了同样的问题。当我将 keras 和 tensorflow 的版本更改为 keras2.2.5 和 tensorflow1.14.0 时，问题就解决了。