I am trying to categorize the dog breeding identification using CNN. I have converted the images to gray scale and re-scaled them in order to be smaller in size. So now I am trying to add them in numpy array and do the training. Also I will use Relu activation function because it performs well with multi layer and a categorical cross entropy for the different categories of dog breeding.
Below is the code for grayscale and re-scale:
def RescaleGrayscaleImg():
# iterate through the names of contents of the folder
for image_path in os.listdir(path):
# create the full input path and read the file
input_path = os.path.join(path, image_path)
# make image grayscale
img = io.imread(input_path)
img_scaled = rescale(img, 2.0 / 4.0)
GrayImg = color.rgb2gray(img_scaled)
# create full output path, 'example.jpg'
# becomes 'grayscaled_example.jpg', save the file to disk
fullpath = os.path.join(outPath, 'grayscaled_'+image_path)
misc.imsave(fullpath, GrayImg)
How I will convert the images to array? Will each column be an image?
For CNN, your input must be a 4-D tensor [batch_size, width, height, channels]
, so each image is a 3-D sub-tensor. Since your images are gray-scale, channels=1
. Also for training all images must be of the same size - WIDTH
and HEIGHT
.
The skimage.io.imread
is returning an ndarray
, and this works perfectly for keras. So you can read the data like this:
all_images = []
for image_path in os.listdir(path):
img = io.imread(image_path , as_grey=True)
img = img.reshape([WIDTH, HEIGHT, 1])
all_images.append(img)
x_train = np.array(all_images)
Not sure how you store the labels, but you'll need to make an array of labels as well. I call it y_train
. You can convert it to one-hot like this:
y_train = keras.utils.to_categorical(y_train, num_classes)
The model in keras is pretty straighforward, here's the simplest one (uses relu and x-entropy):
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu',
input_shape=[WIDTH, HEIGHT, 1]))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=100, epochs=10, verbose=1)
A complete working MNIST example can be found here.