ValueError: Input 0 is incompatible with layer conv2d

ValueError: Input 0 is incompatible with layer conv2d_1: expected ndim=4, found ndim=3

After inquiring into the questions already asked about this problem, I keep presenting it. Im trying to classify letters from A to D. All input images are 64x64 and graycolor.

The first layer of my CNN is:

model = Sequential()

model.add(Conv2D(32, (3,  3), input_shape = input_shape, activation = 'relu'))

And input_shape it's coming from:

# Define the number of classes

num_classes = 4

labels_name={'A':0,'B':1,'C':2,'D':3}



img_data_list=

labels_list=



for dataset in data_dir_list:

    img_list=os.listdir(data_path+'/'+ dataset)

    print ('Loading the images of dataset-'+'{}n'.format(dataset))

    label = labels_name[dataset]

    for img in img_list:

    input_img=cv2.imread(data_path + '/'+ dataset + '/'+ img )

          input_img=cv2.cvtColor(input_img, cv2.COLOR_BGR2GRAY)

          input_img_resize=cv2.resize(input_img,(128,128))

          img_data_list.append(input_img_resize)

          labels_list.append(label)



img_data = np.array(img_data_list)

img_data = img_data.astype('float32')

img_data /= 255

print (img_data.shape)



labels = np.array(labels_list)

print(np.unique(labels,return_counts=True))



#convert class labels to on-hot encoding

Y = np_utils.to_categorical(labels, num_classes)



#Shuffle the dataset

x,y = shuffle(img_data,Y, random_state=2)



# Split the dataset

X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)



#Defining the model

input_shape=img_data[0].shape

print(input_shape)

Thanks,

asked Nov 11 at 13:48

J. Dav

113

What is the value of input_shape?
– today
Nov 11 at 18:58

input_shape=img_data[0].shape and img_data is coming from input_shape=img_data[0].shape
– J. Dav
Nov 11 at 20:40

add a comment |

After inquiring into the questions already asked about this problem, I keep presenting it. Im trying to classify letters from A to D. All input images are 64x64 and graycolor.

The first layer of my CNN is:

model = Sequential()

model.add(Conv2D(32, (3,  3), input_shape = input_shape, activation = 'relu'))

And input_shape it's coming from:

# Define the number of classes

num_classes = 4

labels_name={'A':0,'B':1,'C':2,'D':3}



img_data_list=

labels_list=



for dataset in data_dir_list:

    img_list=os.listdir(data_path+'/'+ dataset)

    print ('Loading the images of dataset-'+'{}n'.format(dataset))

    label = labels_name[dataset]

    for img in img_list:

    input_img=cv2.imread(data_path + '/'+ dataset + '/'+ img )

          input_img=cv2.cvtColor(input_img, cv2.COLOR_BGR2GRAY)

          input_img_resize=cv2.resize(input_img,(128,128))

          img_data_list.append(input_img_resize)

          labels_list.append(label)



img_data = np.array(img_data_list)

img_data = img_data.astype('float32')

img_data /= 255

print (img_data.shape)



labels = np.array(labels_list)

print(np.unique(labels,return_counts=True))



#convert class labels to on-hot encoding

Y = np_utils.to_categorical(labels, num_classes)



#Shuffle the dataset

x,y = shuffle(img_data,Y, random_state=2)



# Split the dataset

X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)



#Defining the model

input_shape=img_data[0].shape

print(input_shape)

Thanks,

asked Nov 11 at 13:48

J. Dav

113

What is the value of input_shape?
– today
Nov 11 at 18:58

input_shape=img_data[0].shape and img_data is coming from input_shape=img_data[0].shape
– J. Dav
Nov 11 at 20:40

add a comment |

After inquiring into the questions already asked about this problem, I keep presenting it. Im trying to classify letters from A to D. All input images are 64x64 and graycolor.

The first layer of my CNN is:

model = Sequential()

model.add(Conv2D(32, (3,  3), input_shape = input_shape, activation = 'relu'))

And input_shape it's coming from:

# Define the number of classes

num_classes = 4

labels_name={'A':0,'B':1,'C':2,'D':3}



img_data_list=

labels_list=



for dataset in data_dir_list:

    img_list=os.listdir(data_path+'/'+ dataset)

    print ('Loading the images of dataset-'+'{}n'.format(dataset))

    label = labels_name[dataset]

    for img in img_list:

    input_img=cv2.imread(data_path + '/'+ dataset + '/'+ img )

          input_img=cv2.cvtColor(input_img, cv2.COLOR_BGR2GRAY)

          input_img_resize=cv2.resize(input_img,(128,128))

          img_data_list.append(input_img_resize)

          labels_list.append(label)



img_data = np.array(img_data_list)

img_data = img_data.astype('float32')

img_data /= 255

print (img_data.shape)



labels = np.array(labels_list)

print(np.unique(labels,return_counts=True))



#convert class labels to on-hot encoding

Y = np_utils.to_categorical(labels, num_classes)



#Shuffle the dataset

x,y = shuffle(img_data,Y, random_state=2)



# Split the dataset

X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)



#Defining the model

input_shape=img_data[0].shape

print(input_shape)

Thanks,

asked Nov 11 at 13:48

J. Dav

113

After inquiring into the questions already asked about this problem, I keep presenting it. Im trying to classify letters from A to D. All input images are 64x64 and graycolor.

The first layer of my CNN is:

model = Sequential()

model.add(Conv2D(32, (3,  3), input_shape = input_shape, activation = 'relu'))

And input_shape it's coming from:

# Define the number of classes

num_classes = 4

labels_name={'A':0,'B':1,'C':2,'D':3}



img_data_list=

labels_list=



for dataset in data_dir_list:

    img_list=os.listdir(data_path+'/'+ dataset)

    print ('Loading the images of dataset-'+'{}n'.format(dataset))

    label = labels_name[dataset]

    for img in img_list:

    input_img=cv2.imread(data_path + '/'+ dataset + '/'+ img )

          input_img=cv2.cvtColor(input_img, cv2.COLOR_BGR2GRAY)

          input_img_resize=cv2.resize(input_img,(128,128))

          img_data_list.append(input_img_resize)

          labels_list.append(label)



img_data = np.array(img_data_list)

img_data = img_data.astype('float32')

img_data /= 255

print (img_data.shape)



labels = np.array(labels_list)

print(np.unique(labels,return_counts=True))



#convert class labels to on-hot encoding

Y = np_utils.to_categorical(labels, num_classes)



#Shuffle the dataset

x,y = shuffle(img_data,Y, random_state=2)



# Split the dataset

X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)



#Defining the model

input_shape=img_data[0].shape

print(input_shape)

Thanks,

python tensorflow keras conv-neural-network

asked Nov 11 at 13:48

J. Dav

113

asked Nov 11 at 13:48

J. Dav

113

asked Nov 11 at 13:48

J. Dav

113

asked Nov 11 at 13:48

J. Dav

113

asked Nov 11 at 13:48

J. Dav

113

What is the value of input_shape?
– today
Nov 11 at 18:58

input_shape=img_data[0].shape and img_data is coming from input_shape=img_data[0].shape
– J. Dav
Nov 11 at 20:40

add a comment |

What is the value of input_shape?
– today
Nov 11 at 18:58

input_shape=img_data[0].shape and img_data is coming from input_shape=img_data[0].shape
– J. Dav
Nov 11 at 20:40

What is the value of input_shape?
– today
Nov 11 at 18:58

input_shape=img_data[0].shape and img_data is coming from input_shape=img_data[0].shape
– J. Dav
Nov 11 at 20:40

add a comment |

1 Answer
1

active

oldest

votes

Conv2d expects input of shape (batchsize, w, h, filters).

You need to add a reshape to fit the data before the conv layer:

 model.add(Reshape((64, 64, 1)))

This will set your model dimensions to [None, 64,64,1] and should be fine for Conv2d.

answered Nov 11 at 20:14

Dinari

1,615422

add a comment |

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1 Answer
1

active

oldest

votes

1 Answer
1

active

oldest

votes

Conv2d expects input of shape (batchsize, w, h, filters).

You need to add a reshape to fit the data before the conv layer:

 model.add(Reshape((64, 64, 1)))

This will set your model dimensions to [None, 64,64,1] and should be fine for Conv2d.

answered Nov 11 at 20:14

Dinari

1,615422

add a comment |

Conv2d expects input of shape (batchsize, w, h, filters).

You need to add a reshape to fit the data before the conv layer:

 model.add(Reshape((64, 64, 1)))

This will set your model dimensions to [None, 64,64,1] and should be fine for Conv2d.

answered Nov 11 at 20:14

Dinari

1,615422

add a comment |

Conv2d expects input of shape (batchsize, w, h, filters).

You need to add a reshape to fit the data before the conv layer:

 model.add(Reshape((64, 64, 1)))

This will set your model dimensions to [None, 64,64,1] and should be fine for Conv2d.

answered Nov 11 at 20:14

Dinari

1,615422

Conv2d expects input of shape (batchsize, w, h, filters).

You need to add a reshape to fit the data before the conv layer:

 model.add(Reshape((64, 64, 1)))

This will set your model dimensions to [None, 64,64,1] and should be fine for Conv2d.

answered Nov 11 at 20:14

Dinari

1,615422

answered Nov 11 at 20:14

Dinari

1,615422

answered Nov 11 at 20:14

Dinari

1,615422

answered Nov 11 at 20:14

Dinari

1,615422

add a comment |

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