Masterarbeit/TeX/Appendix_code.tex

  \section{Code...}
  \begin{itemize}
    \item Code for randomized shallow neural network
    \item Code for keras
  \end{itemize}

  \clearpage
  \begin{lstfloat}
\begin{lstlisting}[language=iPython]
import breeze.stats.distributions.Uniform
import breeze.stats.distributions.Gaussian
import scala.language.postfixOps

object Activation {
  def apply(x: Double): Double = math.max(0, x)

  def d(x: Double): Double = if (x > 0) 1 else 0
}

class RSNN(val n: Int, val gamma: Double = 0.001) {
    val g_unif = Uniform(-10, 10)
    val g_gauss = Gaussian(0, 5)

    val xis = g_unif.sample(n)
    val vs = g_gauss.sample(n)
    val bs = xis zip vs map {case(xi, v) => xi * v}

    def computeL1(x: Double) = (bs zip vs) map {
        case (b, v) => Activation(b + v * x) }
  
    def computeL2(l1: Seq[Double], ws: Seq[Double]): Double =
        (l1 zip ws) map { case (l, w) => w * l } sum
  
    def output(ws: Seq[Double])(x: Double): Double =
        computeL2(computeL1(x), ws)
  
    def learn(data: Seq[(Double, Double)], ws: Seq[Double],
        lamb: Double, gamma: Double): Seq[Double] = {
    
        lazy val deltas = data.map {
            case (x, y) =>
                val l1 = computeL1(x)
                val out = computeL2(l1, ws)
                (l1 zip ws) map {case (l1, w) => (l1 * 2 * (out - y) +
                    lam * 2 * w)  * gamma * -1}
        }
  
        deltas.foldRight(ws)(
            (delta, ws) => ws zip (delta) map { case (w, d) => w + d })  
    }

    def train(data: Seq[(Double, Double)], iter: Int, lam: Double,
        gamma: Double = gamma): (Seq[Double], Double => Double)= {
      
        val ws = (1 to iter).foldRight((1 to n).map(
            _ => 0.0) :Seq[Double])((i, w) => {
            println(s"Training iteration $i")
            println(w.sum/w.length)
            learn(data, w, lam, gamma / 10)
        })
        (ws, output(ws))
    }
}
\end{lstlisting}
\caption{Scala code used to build and train the ridge penalized
  randomized shallow neural network in .... The parameter \textit{lam}
in the train function represents the $\lambda$ parameter in the error
function. The parameters \textit{n} and \textit{gamma} set the number
of hidden nodes and the stepsize for training.}
\label{lst:rsnn}
\end{lstfloat}
\clearpage

\begin{lstlisting}[language=iPython]
import tensorflow as tf
import numpy as np
from tensorflow.keras.callbacks import CSVLogger
from tensorflow.keras.preprocessing.image import ImageDataGenerator

mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_train = x_train / 255.0
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
x_test = x_test / 255.0

y_train = tf.keras.utils.to_categorical(y_train)
y_test = tf.keras.utils.to_categorical(y_test)

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(24,kernel_size=5,padding='same',
    activation='relu',input_shape=(28,28,1)))
model.add(tf.keras.layers.MaxPool2D())
model.add(tf.keras.layers.Conv2D(64,kernel_size=5,padding='same',
    activation='relu'))
model.add(tf.keras.layers.MaxPool2D(padding='same'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(256, activation='relu'))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss="categorical_crossentropy",
    metrics=["accuracy"])

datagen = ImageDataGenerator(
    rotation_range = 30,
    zoom_range = 0.15,
    width_shift_range=2,
    height_shift_range=2,
    shear_range = 1)

csv_logger = CSVLogger(<Target File>)
    
history = model.fit(datagen.flow(x_train, y_train, batch_size=50),
validation_data=(x_test, y_test),
    epochs=125, callbacks=[csv_logger],
    steps_per_epoch = x_train.shape[0]//50) 

\end{lstlisting}
\clearpage
\begin{lstlisting}[language=iPython]
import tensorflow as tf
import numpy as np
from tensorflow.keras.callbacks import CSVLogger
from tensorflow.keras.preprocessing.image import ImageDataGenerator
mnist = tf.keras.datasets.fashion_mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
x_train, x_test = x_train / 255.0, x_test / 255.0

y_train = tf.keras.utils.to_categorical(y_train)
y_test = tf.keras.utils.to_categorical(y_test)

model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(filters = 32, kernel_size = (3, 3),
    activation='relu', input_shape = (28, 28, 1), padding='same'))
model.add(tf.keras.layers.Conv2D(filters = 32, kernel_size = (2, 2), activation='relu', padding = 'same'))
model.add(tf.keras.layers.MaxPool2D(strides=(2,2)))
model.add(tf.keras.layers.Conv2D(filters = 64, kernel_size = (3, 3), activation='relu', padding='same'))
model.add(tf.keras.layers.Conv2D(filters = 64, kernel_size = (3, 3), activation='relu', padding='same'))
model.add(tf.keras.layers.MaxPool2D(strides=(2,2)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(256, activation='relu'))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(10, activation='softmax'))

model.compile(optimizer=tf.keras.optimizers.Adam(lr = 1e-3), loss="categorical_crossentropy", metrics=["accuracy"])

datagen = ImageDataGenerator(
    rotation_range = 15,
    zoom_range = 0.1,
    width_shift_range=2,
    height_shift_range=2,
    shear_range = 0.5,
    fill_mode = 'constant',
    cval = 0)
        
    csv_logger = CSVLogger(<Target File>)
    
    history = model.fit(datagen.flow(x_train, y_train, batch_size=30),
        steps_per_epoch=2000,
        validation_data=(x_test, y_test),
        epochs=125, callbacks=[csv_logger],
        shuffle=True)

\end{lstlisting}
\clearpage
\begin{lstlisting}[language=iPython]
def get_random_sample(a, b, number_of_samples=10):
    x = []
    y = []
    for category_number in range(0,10):
    # get all samples of a category
        train_data_category = a[b==category_number]
        # pick a number of random samples from the category
        train_data_category = train_data_category[np.random.randint(
            train_data_category.shape[0], size=number_of_samples), :]
        x.extend(train_data_category)
        y.append([category_number]*number_of_samples)

        return (np.asarray(x).reshape(-1, 28, 28, 1),
            np.asarray(y).reshape(10*number_of_samples,1))
\end{lstlisting}
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