load_weights Keras模型錯誤

我對CNN相當新。這是我第一次使用keras，tensorflow等。我有一個load_weights函數的問題。我已經培訓了CNN（cifar100），現在我想通過加載它的權重並評估它來測試它。load_weights Keras模型錯誤

這是錯誤，我得到的堆棧回溯：

Traceback (most recent call last): 

    File "<ipython-input-17-247d6312ea1b>", line 1, in <module> 
    runfile('/home/nikola/Desktop/cifar100-Version2.py', wdir='/home/nikola/Desktop') 

    File "/home/nikola/anaconda2/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 866, in runfile 
    execfile(filename, namespace) 

    File "/home/nikola/anaconda2/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 94, in execfile 
    builtins.execfile(filename, *where) 

    File "/home/nikola/Desktop/cifar100-Version2.py", line 80, in <module> 
    model.load_weights('cifar100_best_accuracy.hdf5') 

    File "/home/nikola/anaconda2/lib/python2.7/site-packages/keras/engine/topology.py", line 2520, in load_weights 
    self.load_weights_from_hdf5_group(f) 

    File "/home/nikola/anaconda2/lib/python2.7/site-packages/keras/engine/topology.py", line 2605, in load_weights_from_hdf5_group 
    K.batch_set_value(weight_value_tuples) 

    File "/home/nikola/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 1045, in batch_set_value 
    assign_op = x.assign(assign_placeholder) 

    File "/home/nikola/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 575, in assign 
    return state_ops.assign(self._variable, value, use_locking=use_locking) 

    File "/home/nikola/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gen_state_ops.py", line 47, in assign 
    use_locking=use_locking, name=name) 

    File "/home/nikola/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op 
    op_def=op_def) 

    File "/home/nikola/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2242, in create_op 
    set_shapes_for_outputs(ret) 

    File "/home/nikola/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1617, in set_shapes_for_outputs 
    shapes = shape_func(op) 

    File "/home/nikola/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1568, in call_with_requiring 
    return call_cpp_shape_fn(op, require_shape_fn=True) 

    File "/home/nikola/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 610, in call_cpp_shape_fn 
    debug_python_shape_fn, require_shape_fn) 

    File "/home/nikola/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 675, in _call_cpp_shape_fn_impl 
    raise ValueError(err.message) 

ValueError: Dimension 0 in both shapes must be equal, but are 3 and 32 for 'Assign_11' (op: 'Assign') with input shapes: [3,3,3,32], [32,3,3,3].

我試圖延長keras cifar10代碼cifar100代碼。我設法訓練它，但我也想評估它。通過評估，我可以確定我的模型是否好，它的得分是多少。

這是我的代碼：

from __future__ import print_function 
from keras.datasets import cifar100 
from keras.preprocessing.image import ImageDataGenerator 
from keras.callbacks import ModelCheckpoint 
from keras.models import Sequential 
from keras.layers import Dense, Dropout, Activation, Flatten 
from keras.layers import Convolution2D, MaxPooling2D 
from keras.utils import np_utils, generic_utils 
from six.moves import range 

#import numpy as np 
#import matplotlib.pyplot as plt 

batch_size = 32 
nb_classes = 100 

classes = [...100 classes...`enter code here`] 

test_only =True; 
save_weights = True; 

nb_epoch = 200 
data_augmentation = True 

# input image dimensions 
img_rows, img_cols = 32, 32 
# The CIFAR10 images are RGB. 
img_channels = 3 

# The data, shuffled and split between train and test sets: 
(X_train, y_train), (X_test, y_test) = cifar100.load_data() 
print('X_train shape:', X_train.shape) 
print(X_train.shape[0], 'train samples') 
print(X_test.shape[0], 'test samples') 

# Convert class vectors to binary class matrices. 
Y_train = np_utils.to_categorical(y_train, nb_classes) 
Y_test = np_utils.to_categorical(y_test, nb_classes) 

model = Sequential() 

model.add(Convolution2D(32, 3, 3, border_mode='same', 
         input_shape=X_train.shape[1:])) 
model.add(Activation('relu')) 
model.add(Convolution2D(32, 3, 3)) 
model.add(Activation('relu')) 
model.add(MaxPooling2D(pool_size=(2, 2))) 
model.add(Dropout(0.25)) 

model.add(Convolution2D(64, 3, 3, border_mode='same')) 
model.add(Activation('relu')) 
model.add(Convolution2D(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(nb_classes)) 
model.add(Activation('softmax')) 

# Let's train the model using RMSprop 
model.compile(loss='categorical_crossentropy', 
       optimizer='rmsprop', 
       metrics=['accuracy']) 




if test_only: 
    model.load_weights('cifar100_best_accuracy.hdf5') 

X_train = X_train.astype('float32') 
X_test = X_test.astype('float32') 
X_train /= 255 
X_test /= 255 

if not data_augmentation: 
    print('Not using data augmentation.') 
    model.fit(X_train, Y_train, 
       batch_size=batch_size, 
       nb_epoch=nb_epoch, 
       validation_data=(X_test, Y_test), 
       shuffle=True) 
    score = model.evaluate(X_test, Y_test, batch_size = batch_size) 
    print('Test score:', score) 
else: 
    print('Using real-time data augmentation.') 
    # This will do preprocessing and realtime data augmentation: 
    datagen = ImageDataGenerator(
     featurewise_center=False, # set input mean to 0 over the dataset 
     samplewise_center=False, # set each sample mean to 0 
     featurewise_std_normalization=False, # divide inputs by std of the dataset 
     samplewise_std_normalization=False, # divide each input by its std 
     zca_whitening=False, # apply ZCA whitening 
     rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180) 
     width_shift_range=0.1, # randomly shift images horizontally (fraction of total 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 

    # Compute quantities required for featurewise normalization 
    # (std, mean, and principal components if ZCA whitening is applied). 
    datagen.fit(X_train) 


    model_check_point = ModelCheckpoint('cifar100_best_accuracy.hdf5', monitor='acc', verbose=0, save_best_only=True, save_weights_only=False, mode='auto') 


    # Fit the model on the batches generated by datagen.flow(). 
    model.fit_generator(datagen.flow(X_train, Y_train, 
         batch_size=batch_size), 
         samples_per_epoch=X_train.shape[0], 
         nb_epoch=nb_epoch, 
         callbacks=[model_check_point], 
         validation_data=(X_test, Y_test))

來源

2016-12-29 Nikola Berdić

看起來像是某處形狀不匹配。我認爲訣竅是在形狀中設置正確的順序：batch_size，圖像高度，圖像大小和通道數量。我認爲你需要調試一下才能找出問題所在。如果我是你，我會減少模型到1層模型，以簡化調試。 –

我已經在一臺PC（Windows 10）上訓練過CNN，並且我試圖在另一臺PC上的Ubuntu上load_weights。這兩者之間的任何不匹配是否會使我成爲問題？ –

我想不出任何。你能否在同一臺電腦上加載（Windows 10）？ –

要保存的模型，然後加載回作爲權數，即重新訓練模型之後。

首先，修復腳本以僅保存權重，將其加載回來並檢查問題是否存在。

來源

2017-05-18 04:55:27 bigdata2

load_weights Keras模型錯誤

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