error in data shape with conv2D layer keras

0

i am a biginner in neural networks,
i get the following error when running fit,

ValueError: Error when checking target: expected conv2d_1 to have shape (64, 222, 222) but got array with shape (1, 224, 224)

i use grayscale images, as far as i know i think i'm shaping the input correctly.
i cant get what i'm doing wrong.

here is a snippet of the network

network model:

def convLayer(channels):

  return



Conv2D(channels,kernel_size=3,activation='relu',

 kernel_initializer=initializers.random_normal(mean=0.0, stddev=0.01), 

  data_format='channels_first')



class est_net():



  def __init__(self, input=None):

    if input is None:

        input=Input(shape=(1,224,224))

    self.input=input



    conv1_1 = convLayer(64)(self.input)



    self.output = conv1_1

    self.CDECNN = Model(inputs=self.input, outputs=self.output)

    print(self.CDECNN.summary())

reading data:

def __iter__(self):

    files=self.img_files

    for f in files:

        if f==".DS_Store":

            continue

        img=cv2.imread(os.path.join(self.img_path,f),cv2.COLOR_BGR2GRAY)

        img=img.reshape(1, img.shape[0], img.shape[1])

        if img is None:

            print("unable to read image %s." % f)

            exit(0)

        gt_file='GT_'+f.split('.')[0]+'.mat'

        gt=sio.loadmat(os.path.join(self.gt_path,gt_file))['d_map']

        gt=gt.reshape(1, gt.shape[0], gt.shape[1])

        yield(img,gt)

training:

input_img= #representing input segment images to be fed to the network

actual_dgt= #representing the actual dot-map ground-truth



for i, (img, dgt) in enumerate(training_DS):

    input_img.append(img)

    actual_dgt.append(dgt)



#initializing training parameters

sgd=optimizers.SGD(lr=0.01, decay=0.0005, momentum=0.9)



#compiling the network and defining the loss method

net.CDECNN.compile(optimizer=sgd, loss='mean_squared_error')



#training CDECNN network on training data

training_log=net.CDECNN.fit(np.array(input_img), np.array(actual_dgt), batch_size=1, epochs=5)

python keras conv-neural-network reshape shapes

share|improve this question

edited Nov 20 '18 at 15:30

norahik

asked Nov 20 '18 at 11:01

norahiknorahik

13

add a comment | 

0

i am a biginner in neural networks,
i get the following error when running fit,

ValueError: Error when checking target: expected conv2d_1 to have shape (64, 222, 222) but got array with shape (1, 224, 224)

i use grayscale images, as far as i know i think i'm shaping the input correctly.
i cant get what i'm doing wrong.

here is a snippet of the network

network model:

def convLayer(channels):

  return



Conv2D(channels,kernel_size=3,activation='relu',

 kernel_initializer=initializers.random_normal(mean=0.0, stddev=0.01), 

  data_format='channels_first')



class est_net():



  def __init__(self, input=None):

    if input is None:

        input=Input(shape=(1,224,224))

    self.input=input



    conv1_1 = convLayer(64)(self.input)



    self.output = conv1_1

    self.CDECNN = Model(inputs=self.input, outputs=self.output)

    print(self.CDECNN.summary())

reading data:

def __iter__(self):

    files=self.img_files

    for f in files:

        if f==".DS_Store":

            continue

        img=cv2.imread(os.path.join(self.img_path,f),cv2.COLOR_BGR2GRAY)

        img=img.reshape(1, img.shape[0], img.shape[1])

        if img is None:

            print("unable to read image %s." % f)

            exit(0)

        gt_file='GT_'+f.split('.')[0]+'.mat'

        gt=sio.loadmat(os.path.join(self.gt_path,gt_file))['d_map']

        gt=gt.reshape(1, gt.shape[0], gt.shape[1])

        yield(img,gt)

training:

input_img= #representing input segment images to be fed to the network

actual_dgt= #representing the actual dot-map ground-truth



for i, (img, dgt) in enumerate(training_DS):

    input_img.append(img)

    actual_dgt.append(dgt)



#initializing training parameters

sgd=optimizers.SGD(lr=0.01, decay=0.0005, momentum=0.9)



#compiling the network and defining the loss method

net.CDECNN.compile(optimizer=sgd, loss='mean_squared_error')



#training CDECNN network on training data

training_log=net.CDECNN.fit(np.array(input_img), np.array(actual_dgt), batch_size=1, epochs=5)

python keras conv-neural-network reshape shapes

share|improve this question

edited Nov 20 '18 at 15:30

norahik

asked Nov 20 '18 at 11:01

norahiknorahik

13

add a comment | 

0

0

0

0

i am a biginner in neural networks,
i get the following error when running fit,

ValueError: Error when checking target: expected conv2d_1 to have shape (64, 222, 222) but got array with shape (1, 224, 224)

i use grayscale images, as far as i know i think i'm shaping the input correctly.
i cant get what i'm doing wrong.

here is a snippet of the network

network model:

def convLayer(channels):

  return



Conv2D(channels,kernel_size=3,activation='relu',

 kernel_initializer=initializers.random_normal(mean=0.0, stddev=0.01), 

  data_format='channels_first')



class est_net():



  def __init__(self, input=None):

    if input is None:

        input=Input(shape=(1,224,224))

    self.input=input



    conv1_1 = convLayer(64)(self.input)



    self.output = conv1_1

    self.CDECNN = Model(inputs=self.input, outputs=self.output)

    print(self.CDECNN.summary())

reading data:

def __iter__(self):

    files=self.img_files

    for f in files:

        if f==".DS_Store":

            continue

        img=cv2.imread(os.path.join(self.img_path,f),cv2.COLOR_BGR2GRAY)

        img=img.reshape(1, img.shape[0], img.shape[1])

        if img is None:

            print("unable to read image %s." % f)

            exit(0)

        gt_file='GT_'+f.split('.')[0]+'.mat'

        gt=sio.loadmat(os.path.join(self.gt_path,gt_file))['d_map']

        gt=gt.reshape(1, gt.shape[0], gt.shape[1])

        yield(img,gt)

training:

input_img= #representing input segment images to be fed to the network

actual_dgt= #representing the actual dot-map ground-truth



for i, (img, dgt) in enumerate(training_DS):

    input_img.append(img)

    actual_dgt.append(dgt)



#initializing training parameters

sgd=optimizers.SGD(lr=0.01, decay=0.0005, momentum=0.9)



#compiling the network and defining the loss method

net.CDECNN.compile(optimizer=sgd, loss='mean_squared_error')



#training CDECNN network on training data

training_log=net.CDECNN.fit(np.array(input_img), np.array(actual_dgt), batch_size=1, epochs=5)

python keras conv-neural-network reshape shapes

share|improve this question

edited Nov 20 '18 at 15:30

norahik

asked Nov 20 '18 at 11:01

norahiknorahik

13

i am a biginner in neural networks,
i get the following error when running fit,

ValueError: Error when checking target: expected conv2d_1 to have shape (64, 222, 222) but got array with shape (1, 224, 224)

i use grayscale images, as far as i know i think i'm shaping the input correctly.
i cant get what i'm doing wrong.

here is a snippet of the network

network model:

def convLayer(channels):

  return



Conv2D(channels,kernel_size=3,activation='relu',

 kernel_initializer=initializers.random_normal(mean=0.0, stddev=0.01), 

  data_format='channels_first')



class est_net():



  def __init__(self, input=None):

    if input is None:

        input=Input(shape=(1,224,224))

    self.input=input



    conv1_1 = convLayer(64)(self.input)



    self.output = conv1_1

    self.CDECNN = Model(inputs=self.input, outputs=self.output)

    print(self.CDECNN.summary())

reading data:

def __iter__(self):

    files=self.img_files

    for f in files:

        if f==".DS_Store":

            continue

        img=cv2.imread(os.path.join(self.img_path,f),cv2.COLOR_BGR2GRAY)

        img=img.reshape(1, img.shape[0], img.shape[1])

        if img is None:

            print("unable to read image %s." % f)

            exit(0)

        gt_file='GT_'+f.split('.')[0]+'.mat'

        gt=sio.loadmat(os.path.join(self.gt_path,gt_file))['d_map']

        gt=gt.reshape(1, gt.shape[0], gt.shape[1])

        yield(img,gt)

training:

input_img= #representing input segment images to be fed to the network

actual_dgt= #representing the actual dot-map ground-truth



for i, (img, dgt) in enumerate(training_DS):

    input_img.append(img)

    actual_dgt.append(dgt)



#initializing training parameters

sgd=optimizers.SGD(lr=0.01, decay=0.0005, momentum=0.9)



#compiling the network and defining the loss method

net.CDECNN.compile(optimizer=sgd, loss='mean_squared_error')



#training CDECNN network on training data

training_log=net.CDECNN.fit(np.array(input_img), np.array(actual_dgt), batch_size=1, epochs=5)

python keras conv-neural-network reshape shapes

python keras conv-neural-network reshape shapes

share|improve this question

edited Nov 20 '18 at 15:30

norahik

asked Nov 20 '18 at 11:01

norahiknorahik

13

share|improve this question

edited Nov 20 '18 at 15:30

norahik

asked Nov 20 '18 at 11:01

norahiknorahik

13

share|improve this question

share|improve this question

edited Nov 20 '18 at 15:30

norahik

edited Nov 20 '18 at 15:30

norahik

edited Nov 20 '18 at 15:30

norahik

asked Nov 20 '18 at 11:01

norahiknorahik

13

asked Nov 20 '18 at 11:01

norahiknorahik

13

asked Nov 20 '18 at 11:01

norahiknorahik

13

13

add a comment | 

add a comment | 

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