Masterarbeit/TeX/Figures/test.tex

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%%
%% Python definition (c) 1998 Michael Weber
%% Additional definitions (2013) Alexis Dimitriadis
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%%
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\begin{document}
\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(j))
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}
\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}
\end{document}