train_fc.py

# -*- coding: utf-8 -*-
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Dropout
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.losses import categorical_crossentropy
from tensorflow.keras.optimizers import Adam
import tensorflow.keras.backend as K
from gpu_helpers import init_all_gpu
init_all_gpu()

batch_size = 128
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
#x_train = x_train[:128]
#y_train = y_train[:128]

"""from PIL import Image
for i in range(3):
    img = Image.fromarray(x_test[i], 'L')
    img = img.resize((896, 896), Image.NEAREST)
    img.show()
    print("Bild #" + str(i) + " - Klasse: " + str(y_test[i]))"""

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)

model = Sequential()

from tensorflow.keras.layers import Conv2D, MaxPooling2D
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())

model.add(Dense(128, activation='relu'))

model.add(Dropout(0.5))

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

model.compile(loss=categorical_crossentropy,
              optimizer=Adam(),
              metrics=['accuracy'])

history = model.fit(x_train, y_train,
    batch_size=batch_size,
    epochs=epochs,
    verbose=1,
    validation_data=(x_test, y_test)
)

from pandas import DataFrame
df_loss = DataFrame(data={
    'Epoche': history.epoch * 2,
    'Legende': ['Loss auf Trainingsdaten'] * len(history.epoch) + ['Loss auf Testdaten'] * len(history.epoch),
    'Loss': history.history['loss'] + history.history['val_loss']
})
df_accuracy = DataFrame(data={
    'Epoche': history.epoch * 2,
    'Legende': ['Accuracy auf Trainingsdaten'] * len(history.epoch) + ['Accuracy auf Testdaten'] * len(history.epoch),
    'Accuracy': history.history['accuracy'] + history.history['val_accuracy']
})

import altair as alt
chart_loss = alt.Chart(df_loss).mark_line().encode(
    x='Epoche', y='Loss', color='Legende')
chart_accuracy = alt.Chart(df_accuracy).mark_line().encode(
    x='Epoche', y='Accuracy', color='Legende')
chart = chart_loss + chart_accuracy
chart.resolve_scale(y='independent')
chart.save('chart.html')

score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])