I am training a neural networks with three different output prediction. For computing the loss of one output I need one of the input that is passed into the network. I am not able to access it as the training data is feed into the network by a keras data generator object. Is there any workaround for this problem.
This is the Generator class that feds data into the model
class DataGenerator(tf.keras.utils.Sequence):
def __init__(self,list_ID,centers,sizes,batch_size=2,dims=(512,512),n_channels=3,n_classes=10,shuffle=True) -> None:
assert len(list_ID) == len(centers)
self.dims = dims
self.batch_size = batch_size
self.list_ID = list_ID
self.centers = centers
self.n_channels = n_channels
self.n_classes = n_classes
self.shuffle = shuffle
self.sizes = sizes
self.on_epoch_end()
self.mask = None
def __len__(self):
return int(np.floor(len(self.list_ID) / self.batch_size))
def on_epoch_end(self):
self.indexes = np.arange(len(self.list_ID))
if self.shuffle:
np.random.shuffle(self.indexes)
def __getitem__(self, index):
indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]
list_ID_temp = [self.list_ID[k] for k in indexes]
centers_temp = [self.centers[k] for k in indexes]
sizes_temp = [self.sizes[k] for k in indexes]
X, y = self.__datageneration(list_ID_temp, centers_temp,sizes_temp)
return X, y
def __datageneration(self, list_ID_temp,centers_temp,sizes_temp):
X = np.empty((self.batch_size,*self.dims,self.n_channels))
Y_center = np.empty((self.batch_size,128,128,1))
Y_dimension = np.empty((self.batch_size,128,128,2))
Y_offset = np.empty((self.batch_size,128,128,2))
self.mask = np.empty((self.batch_size,128,128,1))
for i,ID in enumerate(list_ID_temp):
image = cv2.imread(path+'/'+ID) / 255.0
heat_center, self.mask[i,] = gaussian_2d(centers_temp[i],image.shape)
'''Here I tried to save mask which is what I need,
as an attribute to data generator but when accessed by loss function
the value is just None which is what I initialized it as in init method'''
heat_size,heat_off = size_off_heatmap(sizes_temp[i], centers_temp[i],image.shape)
image = cv2.resize(image,(512,512))
X[i,] = image
Y_center[i,] = heat_center
Y_dimension[i,] = heat_size
Y_offset[i,] = heat_off
return (X,{'center_output':Y_center,'size_output':Y_dimension,'offset_output':Y_offset})
This is the generator class I implemented and I needed the mask , which I tried to write as an attribute of data generator object(I have commented the code. For reference I will also include the function that will return the mask and the error function that requires the mask.
Function returning mask
def gaussian_2d(centers, img_shape):
heatmap = []
y_index = np.tile(np.arange(128), (128, 1))
mask = np.zeros((128,128,1))
width = img_shape[1]
height = img_shape[0]
for x_o, y_o in centers:
x = int(x_o / width * 128)
y = int(y_o / height * 128)
mask[y,x] = 1
gauss = np.exp(-((y_index.T - y) ** 2 + (y_index - x) ** 2) / 2 * 0.2 ** 2)
heatmap.append(gauss)
if len(heatmap) > 1:
heatmap = np.stack(heatmap)
heatmap = np.max(heatmap, axis=0)
else:
heatmap = np.array(heatmap)
heatmap = heatmap.reshape((128, 128,1))
return heatmap,mask
Loss function
def final_loss(mask):
def l1_loss(y_true, y_pred):
y_true = tf.cast(y_true, tf.float32)
y_pred = tf.cast(y_pred, tf.float32)
n = tf.reduce_sum(tf.cast(tf.equal(mask, 1.0),dtype=tf.float32))
tot_loss = tf.reduce_sum(tf.abs(y_pred - y_true))
if tf.greater(n,0):
loss = tot_loss / (n)
else:
loss = tot_loss
return loss
return l1_loss
The error show is as below
Epoch 1/10
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-27-74a28b075f52> in <module>()
----> 1 model.fit(gen,epochs=10,verbose=1,callbacks=Callback(patience=4))
9 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
975 except Exception as e: # pylint:disable=broad-except
976 if hasattr(e, "ag_error_metadata"):
--> 977 raise e.ag_error_metadata.to_exception(e)
978 else:
979 raise
ValueError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function *
return step_function(self, iterator)
<ipython-input-24-c45fe131feb7>:5 l1_loss *
n = tf.reduce_sum(tf.cast(tf.equal(mask, 1.0),dtype=tf.float32))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper **
return target(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:1679 equal
return gen_math_ops.equal(x, y, name=name)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_math_ops.py:3179 equal
name=name)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:540 _apply_op_helper
(input_name, err))
ValueError: Tried to convert 'x' to a tensor and failed. Error: None values not supported.
'''