我正在尝试使用keras VAE模板variational_autoencoder_deconv.py found here来修改非MNIST未标记的数据集。我正在使用38,585个256x256像素的训练图像和5,000个验证图像，所以我不能轻松选择mnist.load_data()并将所有图像加载到内存中，所以我已经使用了ImageDataGenerator类使用ImageDataGenerator.flow_from_directory(...)和vae_model.fit_generator(...)方法。我已尽力确保每个图层的输入/输出都匹配，以便我的输入和输出尺寸匹配，并将生成器设置为class_mode='input'，以便我的目标输出与我的输入相同。不幸的是，我不断收到错误，告诉我我的模型被输入图像目标混淆了，例如ValueError: ('Error when checking model target: expected no data, but got:', array([<input image as array>])代码，输出和回溯包含在下面。
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from keras.layers import Input, Dense, Lambda, Flatten, Reshape
from keras.layers import Conv2D, Conv2DTranspose
from keras.models import Model
from keras import backend as K
from keras import metrics
from keras.preprocessing.image import ImageDataGenerator
K.set_image_data_format('channels_first')
K.set_image_dim_ordering('th')
print("Image data format: ", K.image_data_format())
print("Image dimension ordering: ", K.image_dim_ordering())
print("Backend: ", K.backend())
# input image dimensions
img_rows, img_cols, img_chns = 256, 256, 1
# number of convolutional filters to use
filters = 64
# convolution kernel size
num_conv = 3
batch_size = 100
if K.image_data_format() == 'channels_first':
original_img_size = (img_chns, img_rows, img_cols)
else:
original_img_size = (img_rows, img_cols, img_chns)
latent_dim = 2
intermediate_dim = 128
epsilon_std = 1.0
epochs = 5
print("Original image size: ", original_img_size)
x = Input(shape=original_img_size)
conv_1 = Conv2D(img_chns,
kernel_size=(2, 2),
padding='same', activation='relu')(x)
conv_2 = Conv2D(filters,
kernel_size=(2, 2),
padding='same', activation='relu',
strides=(2, 2))(conv_1)
conv_3 = Conv2D(filters,
kernel_size=num_conv,
padding='same', activation='relu',
strides=1)(conv_2)
conv_4 = Conv2D(filters,
kernel_size=num_conv,
padding='same', activation='relu',
strides=1)(conv_3)
flat = Flatten()(conv_4)
hidden = Dense(intermediate_dim, activation='relu')(flat)
z_mean = Dense(latent_dim)(hidden)
z_log_var = Dense(latent_dim)(hidden)
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim),
mean=0., stddev=epsilon_std)
return z_mean + K.exp(z_log_var) * epsilon
# note that "output_shape" isn't necessary with the TensorFlow backend
# so you could write `Lambda(sampling)([z_mean, z_log_var])`
z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])
# we instantiate these layers separately so as to reuse them later
decoder_hid = Dense(intermediate_dim, activation='relu')
decoder_upsample = Dense(filters * 128 * 128, activation='relu')
if K.image_data_format() == 'channels_first':
output_shape = (batch_size, filters, 128, 128)
else:
output_shape = (batch_size, 128, 128, filters)
print('Output shape 1: ', output_shape)
decoder_reshape = Reshape(output_shape[1:])
decoder_deconv_1 = Conv2DTranspose(filters,
kernel_size=num_conv,
padding='same',
strides=1,
activation='relu')
decoder_deconv_2 = Conv2DTranspose(filters,
kernel_size=num_conv,
padding='same',
strides=1,
activation='relu')
if K.image_data_format() == 'channels_first':
output_shape = (batch_size, filters, 256, 256)
else:
output_shape = (batch_size, 256, 256, filters)
print('Output shape 2: ', output_shape)
decoder_deconv_3_upsamp = Conv2DTranspose(filters,
kernel_size=(3, 3),
strides=(2, 2),
padding='valid',
activation='relu')
decoder_mean_squash = Conv2D(img_chns,
kernel_size=2,
padding='valid',
activation='sigmoid')
hid_decoded = decoder_hid(z)
up_decoded = decoder_upsample(hid_decoded)
reshape_decoded = decoder_reshape(up_decoded)
deconv_1_decoded = decoder_deconv_1(reshape_decoded)
deconv_2_decoded = decoder_deconv_2(deconv_1_decoded)
x_decoded_relu = decoder_deconv_3_upsamp(deconv_2_decoded)
x_decoded_mean_squash = decoder_mean_squash(x_decoded_relu)
# instantiate VAE model
vae = Model(x, x_decoded_mean_squash)
# Compute VAE loss
xent_loss = img_rows * img_cols * metrics.binary_crossentropy(
K.flatten(x),
K.flatten(x_decoded_mean_squash))
kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
vae_loss = K.mean(xent_loss + kl_loss)
vae.add_loss(vae_loss)
vae.compile(optimizer='rmsprop')
vae.summary()
# train the VAE on MNIST digits
#(x_train, _), (x_test, y_test) = mnist.load_data()
train_datagen = ImageDataGenerator(data_format='channels_first',
rescale=1./255)
test_datagen = ImageDataGenerator(data_format='channels_first',
rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'../trpa1-sigma3-particles/train', # images are contained in subdir train/imgs
#target_size=(300, 300), # all images will be resized to 150x150
color_mode='grayscale',
class_mode='input',
batch_size=batch_size)
validation_generator = test_datagen.flow_from_directory(
'../trpa1-sigma3-particles/val',
#target_size=(300, 300),
color_mode='grayscale',
class_mode='input',
batch_size=batch_size)
#x_train = x_train.astype('float32') / 255.
#x_train = x_train.reshape((x_train.shape[0],) + original_img_size)
#x_test = x_test.astype('float32') / 255.
#x_test = x_test.reshape((x_test.shape[0],) + original_img_size)
#print('x_train.shape:', x_train.shape)
vae.fit_generator(train_generator,
steps_per_epoch=38585 // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=5000 // batch_size)
输出和回溯如下：
Image data format: channels_first
Image dimension ordering: th
Backend: theano
Original image size: (1, 256, 256)
Output shape 1: (100, 64, 128, 128)
Output shape 2: (100, 64, 256, 256)
ipykernel_launcher.py:140: UserWarning: Output "conv2d_186" missing from loss dictionary. We assume this was done on purpose, and we will not be expecting any data to be passed to "conv2d_186" during training.
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_38 (InputLayer) (None, 1, 256, 256) 0
__________________________________________________________________________________________________
conv2d_182 (Conv2D) (None, 1, 256, 256) 5 input_38[0][0]
__________________________________________________________________________________________________
conv2d_183 (Conv2D) (None, 64, 128, 128) 320 conv2d_182[0][0]
__________________________________________________________________________________________________
conv2d_184 (Conv2D) (None, 64, 128, 128) 36928 conv2d_183[0][0]
__________________________________________________________________________________________________
conv2d_185 (Conv2D) (None, 64, 128, 128) 36928 conv2d_184[0][0]
__________________________________________________________________________________________________
flatten_37 (Flatten) (None, 1048576) 0 conv2d_185[0][0]
__________________________________________________________________________________________________
dense_181 (Dense) (None, 128) 134217856 flatten_37[0][0]
__________________________________________________________________________________________________
dense_182 (Dense) (None, 2) 258 dense_181[0][0]
__________________________________________________________________________________________________
dense_183 (Dense) (None, 2) 258 dense_181[0][0]
__________________________________________________________________________________________________
lambda_37 (Lambda) (None, 2) 0 dense_182[0][0]
dense_183[0][0]
__________________________________________________________________________________________________
dense_184 (Dense) (None, 128) 384 lambda_37[0][0]
__________________________________________________________________________________________________
dense_185 (Dense) (None, 1048576) 135266304 dense_184[0][0]
__________________________________________________________________________________________________
reshape_37 (Reshape) (None, 64, 128, 128) 0 dense_185[0][0]
__________________________________________________________________________________________________
conv2d_transpose_109 (Conv2DTra (None, 64, 128, 128) 36928 reshape_37[0][0]
__________________________________________________________________________________________________
conv2d_transpose_110 (Conv2DTra (None, 64, 128, 128) 36928 conv2d_transpose_109[0][0]
__________________________________________________________________________________________________
conv2d_transpose_111 (Conv2DTra (None, 64, 257, 257) 36928 conv2d_transpose_110[0][0]
__________________________________________________________________________________________________
conv2d_186 (Conv2D) (None, 1, 256, 256) 257 conv2d_transpose_111[0][0]
==================================================================================================
Total params: 269,670,282
Trainable params: 269,670,282
Non-trainable params: 0
__________________________________________________________________________________________________
Found 38585 images belonging to 1 classes.
Found 5000 images belonging to 1 classes.
Epoch 1/5
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-42-e5b8012e53e0> in <module>()
174 epochs=epochs,
175 validation_data=validation_generator,
--> 176 validation_steps=5000 // batch_size)
/usr/local/miniconda/envs/dl/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name +
90 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
/usr/local/miniconda/envs/dl/lib/python3.6/site-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
2222 outs = self.train_on_batch(x, y,
2223 sample_weight=sample_weight,
-> 2224 class_weight=class_weight)
2225
2226 if not isinstance(outs, list):
/usr/local/miniconda/envs/dl/lib/python3.6/site-packages/keras/engine/training.py in train_on_batch(self, x, y, sample_weight, class_weight)
1875 x, y,
1876 sample_weight=sample_weight,
-> 1877 class_weight=class_weight)
1878 if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
1879 ins = x + y + sample_weights + [1.]
/usr/local/miniconda/envs/dl/lib/python3.6/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
1478 output_shapes,
1479 check_batch_axis=False,
-> 1480 exception_prefix='target')
1481 sample_weights = _standardize_sample_weights(sample_weight,
1482 self._feed_output_names)
/usr/local/miniconda/envs/dl/lib/python3.6/site-packages/keras/engine/training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
54 raise ValueError('Error when checking model ' +
55 exception_prefix + ': '
---> 56 'expected no data, but got:', data)
57 return []
58 if data is None:
ValueError: ('Error when checking model target: expected no data, but got:', array([[[[ 1. , 1. , 1. , ..., 0.38823533,
0.41568631, 0.49019611],
[ 1. , 1. , 1. , ..., 0.28627452,
0.27843139, 0.30588236],
[ 1. , 1. , 1. , ..., 0.21568629,
0.18431373, 0.18431373],
...,
[ 0.44313729, 0.35686275, 0.30980393, ..., 0.15686275,
0.10588236, 0.03529412],
[ 0.10196079, 0.04705883, 0.03529412, ..., 0.22352943,
0.19215688, 0.14117648],
[ 0. , 0. , 0. , ..., 0.32941177,
0.32941177, 0.3137255 ]]],
[[[ 0. , 0. , 0. , ..., 0.30980393,
0.19215688, 0.07058824],
[ 0. , 0. , 0.10588236, ..., 0.41176474,
0.32941177, 0.24313727],
[ 0.18823531, 0.27843139, 0.34509805, ..., 0.48235297,
0.43529415, 0.38823533],
...,
[ 1. , 0.97647065, 0.87450987, ..., 0.37647063,
0.29019609, 0.21176472],
[ 1. , 1. , 0.9450981 , ..., 0.45490199,
0.36862746, 0.29411766],
[ 1. , 1. , 1. , ..., 0.57647061,
0.50588238, 0.44705886]]],
[[[ 0. , 0.08235294, 0.3019608 , ..., 0.75294125,
0.72156864, 0.65490198],
[ 0. , 0.14509805, 0.32549021, ..., 0.73333335,
0.72549021, 0.68627453],
[ 0.02745098, 0.19215688, 0.34117648, ..., 0.74117649,
0.76078439, 0.74901962],
...,
[ 0.71372551, 0.65098041, 0.58823532, ..., 0.29803923,
0.26274511, 0.21960786],
[ 0.72549021, 0.67450982, 0.63529414, ..., 0.26666668,
0.27843139, 0.29019609],
[ 0.70980394, 0.67843139, 0.66274512, ..., 0.22352943,
0.29019609, 0.34901962]]],
...,
[[[ 0.46274513, 0.37254903, 0.29019609, ..., 1. ,
1. , 1. ],
[ 0.47450984, 0.38039219, 0.29803923, ..., 1. ,
1. , 1. ],
[ 0.48627454, 0.3921569 , 0.3019608 , ..., 0.85098046,
0.9450981 , 1. ],
...,
[ 0.92156869, 0.89411771, 0.83921576, ..., 0.66274512,
0.9333334 , 1. ],
[ 1. , 0.9333334 , 0.83921576, ..., 0.61960787,
0.91764712, 1. ],
[ 1. , 0.95294124, 0.82352948, ..., 0.53333336,
0.86666673, 1. ]]],
[[[ 1. , 1. , 1. , ..., 0.0627451 ,
0. , 0. ],
[ 1. , 1. , 1. , ..., 0.08627451,
0. , 0. ],
[ 1. , 1. , 1. , ..., 0.12156864,
0. , 0. ],
...,
[ 1. , 1. , 1. , ..., 0.40000004,
0.52156866, 0.64313728],
[ 1. , 1. , 1. , ..., 0.45098042,
0.57647061, 0.7019608 ],
[ 1. , 1. , 1. , ..., 0.54509807,
0.67843139, 0.82352948]]],
[[[ 0.09019608, 0.23529413, 0.41176474, ..., 0. ,
0. , 0. ],
[ 0.34901962, 0.45098042, 0.57647061, ..., 0.08235294,
0. , 0. ],
[ 0.61960787, 0.67843139, 0.75686282, ..., 0.18039216,
0.01960784, 0. ],
...,
[ 0.81176478, 0.81176478, 0.7843138 , ..., 0.43529415,
0.41568631, 0.3921569 ],
[ 0.78823537, 0.7843138 , 0.74901962, ..., 0.60000002,
0.61176473, 0.62352943],
[ 0.76470596, 0.75686282, 0.72156864, ..., 0.76078439,
0.81176478, 0.86274517]]]], dtype=float32))
我认识到这个错误很可能是因为我试图将我的数据塞进一个专为MNIST数据构建的模板中，但尽管我尽最大努力追踪追溯和搜索keras问题，但我一直无法修正它。我有更多的同事更喜欢keras，他们鄙视ImageDataGenerator类并为他们使用的数据实现了他们自己的目录迭代器类，但是他们还是无法帮助我为这个无人监督做一个设置，我希望无论如何都不必这样做。
有什么想法吗？