python Kerasによる多层ニューラルネット

Posted 2021-05-09

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import RMSprop
from keras.callbacks import EarlyStopping, CSVLogger
import matplotlib.pyplot as plt

batch_size = 128
num_classes = 10
epochs = 20

# load mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(60000, 784).astype('float32')
x_test = x_test.reshape(10000, 784).astype('float32')
x_train /= 255
x_test /= 255

y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)

# build model
model = Sequential()
model.add(Dense(512, input_shape=(784, )))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(10))
model.add(Activation('softmax'))

model.summary()

# fit model
model.compile(loss='categorical_crossentropy',
              optimizer=RMSprop(),
              metrics=['accuracy'])

es = EarlyStopping(monitor='val_loss', patience=2)
csv_logger = CSVLogger('training.log')
hist = model.fit(x_train, y_train,
                 batch_size=batch_size,
                 epochs=epochs,
                 verbose=1,
                 validation_split=0.1,
                 callbacks=[es, csv_logger])

# evaluate model
score = model.evaluate(x_test, y_test, verbose=0)
print('test loss:', score[0])
print('test acc:', score[1])

# plot results
loss = hist.history['loss']
val_loss = hist.history['val_loss']

epochs = len(loss)
plt.plot(range(epochs), loss, marker='.', label='acc')
plt.plot(range(epochs), val_loss, marker='.', label='val_acc')
plt.legend(loc='best')
plt.grid()
plt.xlabel('epoch')
plt.ylabel('acc')
plt.show()