Conv2DTranspose throwing error but UpSampling2D works fine

[trogloditee]

Using Keras 2.0.3, Theano 0.9, Python 3.5.

Images are 256 x 256 grayscale, with binary masks. Channels first is ensured in Keras back end, appropriate axis=1, using K.set_image_data_format('channels_first') and inputs = Input((1, dim, dim)).

When running the Unet before UpSampling2D was changed to Conv2DTranspose, the model trained without problems.

When running the Unet with Conv2DTranspose, Theano throws the below error. Is there any advantage to using Conv2DTranspose?

Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/theano/compile/function_module.py", line 884, in __call__
    self.fn() if output_subset is None else\
ValueError: GpuCorrMM shape inconsistency:
  bottom shape: 5 256 32 32
  weight shape: 512 256 2 2
  top shape: 5 512 16 16 (expected 5 512 17 17)


During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/.../src/train_binary.py", line 102, in <module>
    train()
  File "/Users/.../src/train_binary.py", line 77, in train
    callbacks=[model_checkpoint, csv_log_cback])
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/keras/engine/training.py", line 1498, in fit
    initial_epoch=initial_epoch)
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/keras/engine/training.py", line 1152, in _fit_loop
    outs = f(ins_batch)
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/keras/backend/theano_backend.py", line 1158, in __call__
    return self.function(*inputs)
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/theano/compile/function_module.py", line 898, in __call__
    storage_map=getattr(self.fn, 'storage_map', None))
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/theano/gof/link.py", line 325, in raise_with_op
    reraise(exc_type, exc_value, exc_trace)
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/six-1.10.0-py3.5.egg/six.py", line 685, in reraise
    raise value.with_traceback(tb)
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/theano/compile/function_module.py", line 884, in __call__
    self.fn() if output_subset is None else\
ValueError: GpuCorrMM shape inconsistency:
  bottom shape: 5 256 32 32
  weight shape: 512 256 2 2
  top shape: 5 512 16 16 (expected 5 512 17 17)

Apply node that caused the error: GpuCorrMM_gradInputs{half, (2, 2), (1, 1)}(GpuContiguous.0, GpuContiguous.0, TensorConstant{32}, TensorConstant{32})
Toposort index: 242
Inputs types: [CudaNdarrayType(float32, 4D), CudaNdarrayType(float32, 4D), TensorType(int64, scalar), TensorType(int64, scalar)]
Inputs shapes: [(512, 256, 2, 2), (5, 512, 16, 16), (), ()]
Inputs strides: [(1024, 4, 2, 1), (131072, 256, 16, 1), (), ()]
Inputs values: ['not shown', 'not shown', array(32), array(32)]
Outputs clients: [[GpuSubtensor{int64:int64:int8, int64:int64:int8, int64:int64:int8, :int64:}(GpuCorrMM_gradInputs{half, (2, 2), (1, 1)}.0, ScalarFromTensor.0, ScalarFromTensor.0, Constant{1}, ScalarFromTensor.0, ScalarFromTensor.0, Constant{1}, Constant{0}, Constant{8}, Constant{1}, Constant{8})]]

[pratikgorji]

Can you please post your code in which you used "UpSampling2D " ,
I am also getting the same error while using Conv2dTranspose "ValueError: Error when checking target: expected conv2d_172 to have shape (None, 1, 320, 1) but got array with shape (286, 1, 314, 512) "

Using Keras 2.0.1, Theano 0.9, Python 3.4. I am using this topology

actual image size = 327 x 532 using this image size = rows, cols = 314, 512

inputs = Input((1, rows, cols))

zero =  ZeroPadding2D((3,3))(inputs)

conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(zero)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)

conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)

up6 = concatenate([Conv2DTranspose(256, (3, 3), strides=(2, 2), padding='same')(conv5), conv4], axis=3)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)

up7 = concatenate([Conv2DTranspose(128, (3, 3), strides=(2, 2), padding='same')(conv6), conv3], axis=3)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)

up8 = concatenate([Conv2DTranspose(64, (3, 3), strides=(2, 2), padding='same')(conv7), conv2], axis=3)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)

up9 = concatenate([Conv2DTranspose(32, (3, 3), strides=(2, 2), padding='same')(conv8), conv1], axis=3)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)

conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)

model = Model(inputs=[inputs], outputs=[conv10])
model.compile(optimizer=Adam(lr=1e-5), loss='sparse_categorical_crossentropy', metrics=['accuracy'])

---