Keras CNN dimension problems

Question

I am trying to build a CNN using Keras for an image segmentation task, based on this article. Because my dataset is small, I wanted to use Keras ImageDataGenerator and feed it to fit_generator(). So, I followed the example on the Keras website. But, since zipping the image and mask generators didn't work, I followed this answer and created my own generator.

My input data is of size (701,256,1) and my problem is binary (foreground, background). For each image I have a label of the same shape.

Now, I am facing a dimensionality problem. This was also mentioned in the answer, but I am unsure of how to solve it.

The error:

 ValueError: Error when checking target: expected dense_3 to have 2 dimensions, but got array with shape (2, 704, 256, 1)

I am pasting the entire code I have here:

import numpy
import pygpu
import theano
import keras

from keras.models import Model, Sequential
from keras.layers import Input, Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D, Reshape
from keras.layers import BatchNormalization
from keras.preprocessing.image import ImageDataGenerator

from keras.utils import np_utils
from keras import backend as K

def superGenerator(image_gen, label_gen):
    while True:
      x = image_gen.next()
      y = label_gen.next()
      yield x[0], y[0]


img_height = 704
img_width = 256

train_data_dir = 'Dataset/Train/Images'
train_label_dir = 'Dataset/Train/Labels'
validation_data_dir = 'Dataset/Validation/Images'
validation_label_dir = 'Dataset/Validation/Labels'
n_train_samples = 1000
n_validation_samples = 500
epochs = 50
batch_size = 2

input_shape = (img_height, img_width,1)
target_shape = (img_height, img_width)

model = Sequential()

model.add(Conv2D(80,(28,28), input_shape=input_shape))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))

model.add(Conv2D(96,(18,18)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))

model.add(Conv2D(128,(13,13)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))


model.add(Conv2D(160,(8,8)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())

model.add(Dense(1024, activation='relu'))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.25))

model.add(Dense(2, activation='softmax'))

model.summary()

model.compile(loss='binary_crossentropy', optimizer='nadam', metrics=['accuracy'])

data_gen_args = dict(
    rescale=1./255,
    horizontal_flip=True,
    vertical_flip=True
    )

train_datagen = ImageDataGenerator(**data_gen_args)
train_label_datagen = ImageDataGenerator(**data_gen_args)
test_datagen = ImageDataGenerator(**data_gen_args)
test_label_datagen = ImageDataGenerator(**data_gen_args)

seed = 1

train_image_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=target_shape,
    color_mode='grayscale',
    batch_size=batch_size,
    class_mode = 'binary',
    seed=seed)
train_label_generator = train_label_datagen.flow_from_directory(
    train_label_dir,
    target_size=target_shape,
    color_mode='grayscale',
    batch_size=batch_size,
    class_mode = 'binary',
    seed=seed)

validation_image_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=target_shape,
    color_mode='grayscale',
    batch_size=batch_size,
    class_mode = 'binary',
    seed=seed)

validation_label_generator = test_label_datagen.flow_from_directory(
    validation_label_dir,
    target_size=target_shape,
    color_mode='grayscale',
    batch_size=batch_size,
    class_mode = 'binary',
    seed=seed)

train_generator = superGenerator(train_image_generator, train_label_generator,batch_size)
test_generator = superGenerator(validation_image_generator, validation_label_generator,batch_size)

model.fit_generator(
    train_generator,
    steps_per_epoch= n_train_samples // batch_size,
    epochs=50,
    validation_data=test_generator,
    validation_steps=n_validation_samples // batch_size)

model.save_weights('first_try.h5')

I am new to Keras (and CNNs), so any help would be very much appreciated.

Answer 1

Ok. I did some rubberduck-debugging and read a few more articles. Of course the dimensionality was a problem. This simple answer did it for me. My labels are of shape same as the input image so the output of the model should be of that shape as well. I used Conv2DTranspose to solve this issue.