Keras Error for CNN Output - keras

Question

I am getting an error when trying to create a CNN model in Keras to create a denoising autoencoder. My Keras backend is TensorFlow.
My input data is a numpy array. The numpy array were taken from grayscale images. I split this using sklearn train_test_split. I have resized the data and get an error at the output layer.
/opt/anaconda/anaconda3/lib/python3.6/site-
packages/keras/engine/training.py in _standardize_input_data(data,
names, shapes, check_batch_axis, exception_prefix)
151 ' to have shape ' + str(shapes[i]) +
152 ' but got array with shape ' +
--> 153 str(array.shape))
154 return arrays
155
ValueError: Error when checking target: expected conv2d_transpose_36
to have shape (None, 279, 559, 1) but got array with shape (129, 258,
540, 1)
train_images = os.listdir('data/train')
test_images = os.listdir('data/test')
clean_images = os.listdir('data/train_cleaned')
def set_common_size(img_link, std_size = (258, 540)):
'''Function will take in the argument of a link to an image and return the image
with the standard size.'''
img = Image.open(img_link)
img = image.img_to_array(img)
img = np.resize(img, std_size)
return img / 255
train_data = []
test_data = []
cleaned_data = []
for img in train_images:
img_file = set_common_size('data/train/' + img)
train_data.append(img_file)
for img in test_images:
img_file = set_common_size('data/test/' + img)
test_data.append(img_file)
for img in clean_images:
img_file = set_common_size('data/train_cleaned/' + img)
cleaned_data.append(img_file)
train_data = np.asarray(train_data)
test_data = np.asarray(test_data)
cleaned_data = np.asarray(cleaned_data)
x_train, x_test, y_train, y_test = train_test_split(train_data, cleaned_data, test_size=0.1, random_state=42)
input_shape = x_train[0].shape
input_layer = Input(input_shape)
#Layer 1
layer1 = Conv2D(64, 3, activation='relu', padding='same')(input_layer)
layer1 = MaxPooling2D(2)(layer1)
#Layer 2
layer2 = Conv2D(128, 3, activation='relu', padding='same')(layer1)
layer2 = MaxPooling2D(2)(layer2)
#Layer 3
layer3 = Conv2D(256, 3, activation='relu', padding='same')(layer2)
layer3 = MaxPooling2D(2)(layer3)
#Bottleneck
encoded = layer3
#Layer1
uplayer1 = UpSampling2D(2)(layer3)
uplayer1 = Conv2DTranspose(256, 2, activation='relu')(uplayer1)
#Layer2
uplayer2 = UpSampling2D(2)(uplayer1)
uplayer2 = Conv2DTranspose(128, 5, activation='relu')(uplayer2)
#Layer3
uplayer3 = UpSampling2D(2)(uplayer2)
uplayer3 = Conv2DTranspose(64, 10, activation='relu')(uplayer3)
output = Conv2DTranspose(1, 3, activation='sigmoid')(uplayer3)
model = Model(input=input_layer, output=output)
print(model.summary())
model.compile(optimizer='adadelta', loss='binary_crossentropy')
model.fit(
x_train, y_train,
epochs=100,
shuffle=True,
validation_data=(x_test, y_test)
)
Here are the Model Summary results:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 258, 540, 1) 0
_________________________________________________________________
conv2d_60 (Conv2D) (None, 258, 540, 64) 640
_________________________________________________________________
max_pooling2d_37 (MaxPooling (None, 129, 270, 64) 0
_________________________________________________________________
conv2d_61 (Conv2D) (None, 129, 270, 128) 73856
_________________________________________________________________
max_pooling2d_38 (MaxPooling (None, 64, 135, 128) 0
_________________________________________________________________
conv2d_62 (Conv2D) (None, 64, 135, 256) 295168
_________________________________________________________________
max_pooling2d_39 (MaxPooling (None, 32, 67, 256) 0
_________________________________________________________________
up_sampling2d_37 (UpSampling (None, 64, 134, 256) 0
_________________________________________________________________
conv2d_transpose_29 (Conv2DT (None, 65, 135, 256) 262400
_________________________________________________________________
up_sampling2d_38 (UpSampling (None, 130, 270, 256) 0
_________________________________________________________________
conv2d_transpose_30 (Conv2DT (None, 134, 274, 128) 819328
_________________________________________________________________
up_sampling2d_39 (UpSampling (None, 268, 548, 128) 0
___________________________________________________________
_________________________________________________________________
conv2d_transpose_32 (Conv2DT (None, 279, 559, 1) 577
=============================================================______
conv2d_transpose_31 (Conv2DT (None, 277, 557, 64) 819264====
Trainable params: 2,271,233
Non- params: 0

Answer 1

There is a mismatch between:
the shape of the outputs your model creates: (279, 559, 1)
(shown in the summary line conv2d_transpose_32 (Conv2DT (None, 279, 559, 1) which is actually the last layer, but your console output looks a bit messed up)
and
the shape of the outputs you are expecting: (258, 540, 1) (the shape of the entries in y_train that you feed in)
Use the model summary to see where the deviations from the expected shapes start and use the proper padding values to get the expected output shapes.