Attempt to train VAE on unlabeled images (input=target) using ImageDataGenerator and vae.fit_generator fails when checking model target.

Hi all,

Foremost, my theano and keras are up-to-date.

I am trying to adapt the keras VAE template variational_autoencoder_deconv.py for a non-MNIST unlabeled dataset. I am using 38,585 256x256 pixel training images and 5,000 validation images, so I can’t go the easy route of mnist.load_data() and load all the images into memory, so I have resorted to using the ImageDataGenerator class along with the ImageDataGenerator.flow_from_directory(...) and vae_model.fit_generator(...) methods. I have done my best to make sure the in/out of each layer are matching so that my input and output dimensions match, and have set the generator to class_mode='input' so that my target output is the same as my input. Unfortunately, I keep getting an error that tells me that my model is confused by the input image target, e.g. ValueError: (‘Error when checking model target: expected no data, but got:’, array([]) The code is included below, followed by the output and traceback.

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, load_img, img_to_array

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

z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])

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_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', 
        color_mode='grayscale',
        class_mode='input',
        batch_size=batch_size)

validation_generator = test_datagen.flow_from_directory(
        '../trpa1-sigma3-particles/val',
        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)

The output and traceback are below:

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))

I recognize that the error is most likely due to me trying to shoehorn my data into a template that was purpose-built for MNIST data, but despite my best effort in following the traceback and scouring keras issues, I have been unable to get it right. The docs for flow_from_directory(...) suggest the use of class_mode=input, i.e. input=target, for training autoencoders in this unsupervised setting, but flow_from_directory(..., class_mode=input, ...) seems to be upsetting vae.fit_generator(...). Any thoughts as to why this could be?

Thanks and all the best.

[mattochal]

Putting the loss in a function seemed to have solved it for me.

# Compute VAE loss
def my_vae_loss(y_true, y_pred):
    xent_loss = img_rows * img_cols * metrics.binary_crossentropy(K.flatten(y_true), K.flatten(y_pred))
    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)
    return vae_loss

vae.compile(optimizer='rmsprop', loss=my_vae_loss)

[giovp]

Had the same issue, working with TF2.0 (stable). Adding the loss explicitly did not work.

vae.add_loss(vae_loss)

Disabling eager exec solved it.

config = ConfigProto()
config.gpu_options.allow_growth = True
session = Session(config = config)
tf.compat.v1.disable_eager_execution()

@eburling not sure if it make sense to reopen, but would be really interesting to get a clue on why this happen.