Я пытаюсь адаптировать шаблон VAE keras variational_autoencoder_deconv.py , найденный здесь , для набора данных без метки без MNIST. Я использую обучающие изображения размером 38 585 256x256 пикселей и 5000 проверочных изображений, поэтому я не могу пойти по простому маршруту mnist.load_data() и загрузить все изображения в память, поэтому я прибег к использованию класса ImageDataGenerator вместе с ImageDataGenerator.flow_from_directory(...) и vae_model.fit_generator(...) методы. Я приложил все усилия, чтобы убедиться, что вход / выход каждого слоя совпадают, чтобы мои входные и выходные размеры совпадали, и установил генератор на class_mode='input', чтобы мой выходной результат был таким же, как мой вход. К сожалению, я продолжаю получать сообщение об ошибке, которое говорит мне, что моя модель смущена целью входного изображения, например ValueError: ('Error when checking model target: expected no data, but got:', array([<input image as array>]) Код, выходные данные и трассировка включены ниже.
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)
Вывод и трассировка ниже:
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))
Я понимаю, что ошибка, скорее всего, связана с тем, что я пытался вставить мои данные в шаблон, специально созданный для данных MNIST, но, несмотря на все мои усилия по отслеживанию проблем с отслеживанием и поиском в keras, я не смог сделай это правильно. У меня есть коллеги, которые больше настроены на керы, которые презирают класс ImageDataGenerator и внедрили свои собственные классы итераторов каталогов для данных, с которыми они работают, но они пока не смогли помочь мне сделать таковые для этой неконтролируемой настройки, и я надеюсь, что в любом случае это не обязательно.
Есть идеи?