Exception: Error when checking model target: expected dense_3 to have shape (None, 1000) but got array with shape (32, 2)

Question

How do I create a VGG-16 sequence for my data?

The data has the following :

model = Sequential() 
model.add(ZeroPadding2D((1, 1), input_shape=(3, img_width, img_height))) model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1')) model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1))) 
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_1')) model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1))) 
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_1')) model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_2'))
model.add(ZeroPadding2D((1, 1))) 
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_3')) model.add(MaxPooling2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1))) 
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_1')) model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_2'))
model.add(ZeroPadding2D((1, 1))) 
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_3')) model.add(MaxPooling2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1))) 
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_1')) model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_2'))
model.add(ZeroPadding2D((1, 1))) 
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_3')) model.add(MaxPooling2D((2, 2), strides=(2, 2)))

model.add(Flatten()) 
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5)) 
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5)) 
model.add(Dense(1000, activation='softmax'))

sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy')

train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
        train_data_dir,
        target_size=(img_width, img_height),
        batch_size=32)

validation_generator = test_datagen.flow_from_directory(
        validation_data_dir,
        target_size=(img_width, img_height),
        batch_size=32)

model.fit_generator(
        train_generator,
        samples_per_epoch=2000,
        nb_epoch=1,
        verbose=1,
        validation_data=validation_generator,
        nb_val_samples=800)

json_string = model.to_json()  
open('my_model_architecture.json','w').write(json_string) 
model.save_weights('Second_try.h5')

I got an error:

Exception: Error when checking model target: expected dense_3 to have shape (None, 32) but got array with shape (32, 2)

How do I change Dense to make it work?

Answer 1

I have 10 species,
I have solved the problem by
changing:

model.add(Dense(1000, activation='softmax'))

to:

model.add(Dense(10, activation='softmax'))

then it works.

Answer 2

Here instead of 1000 you should have the total number of classes because it's the output layer.

model.add(Dense(1000, activation='softmax')) 

Also shape of labels (or Y_train/Y_test) should be (total number of classes, total number records).

This helped me resolve similar kind of error.