Ytukyg

so I'm trying to train a GAN to color images using a the new TensorFlow data set API
and I cant get it to work

I'm trying to use the simple one shot iterator for my data set and I think it might be causing the problem but I can't figure out why

so what I'm asking is

can someone tell me whats wrong with the code

code:

creating the data set

def get_next():



    #where gray_ls is just a list of image paths 

    gray_ds   = tf.data.Dataset.from_tensor_slices(gray_ls).shuffle(50).map(in_parser).batch(30).repeat()

    print(f"output types = {gray_ds.output_types}")   # --> output types = <dtype: 'float32'>

    print(f"output shapes = {gray_ds.output_shapes}") # --> output shapes = (?, ?, ?, ?)



    gray_iter  = gray_ds.make_one_shot_iterator()

    next_gray  = gray_iter.get_next()



    # next_color is the same as next gray just different images

    return next_color, next_gray



# mapping function 

def in_parser(img_path):



    img_file = tf.read_file(img_path)

    img = tf.image.decode_image(img_file,channels=3)

    img = tf.image.random_flip_left_right(img)

    img = tf.image.random_brightness(img, max_delta = 0.1)

    img = tf.image.random_contrast(img, lower = 0.9, upper = 1.1)

    img = tf.cast(img, tf.float32)

    img = img/255.0

    print(img)

    return img

#some global vars 

stddev  = 0.02

decay   = 0.9

epsilon = 1e-4

k_size  = [5,5]

strides = [2,2]

def gen(input, is_train):



#chanel number

c1 , c2 ,c3 ,c4 = 64, 128, 256, 512



with tf.variable_scope("gen",reuse=tf.AUTO_REUSE):



    #this is where it crashes

    conv1 = tf.layers.conv2d(input,c1,k_size,strides,'SAME',

                             kernel_initializer=tf.truncated_normal_initializer(stddev=stddev),

                             name='conv1')



    bn1 = tf.contrib.layers.batch_norm(conv1,is_training=is_train, updates_collections=None,

                                       decay=decay,epsilon=epsilon,scope='bn1')

    ac1 = lrelu(bn1,'ac1')

#there is more code after this

trying to run it:

next_color, next_gray = get_next()



sess = tf.Session()

sess.run(tf.global_variables_initializer())

sess.run(tf.local_variables_initializer())

foo = sess.run(next_gray)



print(f"foo ndims : {foo.ndim}") # --> foo ndims : 4

gen_image = gen(foo, True)

# some more code after this 

now this rasises an error:

 AttributeError: 'tuple' object has no attribute 'ndims'

     ---------------------------------------------------------------------------

AttributeError                            Traceback (most recent call last)

<ipython-input-1-701a9276e633> in <module>()

     94 

     95 

---> 96 train()



<ipython-input-1-701a9276e633> in train()

     41 #     print(foo.shape)

     42     print("==========================+==============")

---> 43     gen_image = gen(foo, True)

     44 #     gen_image = gen(next_gray, True)

     45     print("==========================+==============")



~Desktopcodepythonimage_processingUntitled FolderUntitled Foldertesting1_2my_gen.py in gen(input, is_train)

     30         conv1 = tf.layers.conv2d(input,c1,k_size,strides,'SAME',

     31                                  kernel_initializer=tf.truncated_normal_initializer(stddev=stddev),

---> 32                                  name='conv1')

     33 

     34         bn1 = tf.contrib.layers.batch_norm(conv1,is_training=is_train, updates_collections=None,



~Anaconda2envsimage_reclibsite-packagestensorflowpythonlayersconvolutional.py in conv2d(inputs, filters, kernel_size, strides, padding, data_format, dilation_rate, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, trainable, name, reuse)

    423       _reuse=reuse,

    424       _scope=name)

--> 425   return layer.apply(inputs)

    426 

    427 



~Anaconda2envsimage_reclibsite-packagestensorflowpythonkerasenginebase_layer.py in apply(self, inputs, *args, **kwargs)

    803       Output tensor(s).

    804     """

--> 805     return self.__call__(inputs, *args, **kwargs)

    806 

    807   def _set_learning_phase_metadata(self, inputs, outputs):



~Anaconda2envsimage_reclibsite-packagestensorflowpythonlayersbase.py in __call__(self, inputs, *args, **kwargs)

    360 

    361       # Actually call layer

--> 362       outputs = super(Layer, self).__call__(inputs, *args, **kwargs)

    363 

    364     if not context.executing_eagerly():



~Anaconda2envsimage_reclibsite-packagestensorflowpythonkerasenginebase_layer.py in __call__(self, inputs, *args, **kwargs)

    718 

    719         # Check input assumptions set before layer building, e.g. input rank.

--> 720         self._assert_input_compatibility(inputs)

    721         if input_list and self._dtype is None:

    722           try:



~Anaconda2envsimage_reclibsite-packagestensorflowpythonkerasenginebase_layer.py in _assert_input_compatibility(self, inputs)

   1408           spec.min_ndim is not None or

   1409           spec.max_ndim is not None):

-> 1410         if x.shape.ndims is None:

   1411           raise ValueError('Input ' + str(input_index) + ' of layer ' +

   1412                            self.name + ' is incompatible with the layer: '



AttributeError: 'tuple' object has no attribute 'ndims'

thanks in advance

so apparently casting the out put to a tf.float32 solves the problem

next_color, next_gray = get_next()



sess = tf.Session()

sess.run(tf.global_variables_initializer())

sess.run(tf.local_variables_initializer())  

foo = sess.run(next_gray)

gray_batch = tf.cast(foo, dtype = tf.float32) 



gen_image = gen(gray_batch, True)